--- title: "Agents" description: "The agent workforce — prebuilt and custom AI agents for revenue operations, with an org chart of how they collaborate." canonical: "https://vasco.app/agents" --- # Every human gets a teammate. They start tomorrow. AI agents grounded in your real revenue data. They tell you what's happening, why, and what to do next — without hallucinating on the numbers that matter. Primary actions: [Start for free](https://my.vasco.app/get-started) · [Watch 2-min demo](/form-video) ## Why grounding matters: Claude + raw CRM vs. Vasco Tested with the same five questions across five customer datasets, on two foundations. On raw CRM access via MCP, Claude sees only surface-level data and reports everything on track ("Pipeline on track, 102% of target, forecast confidence high") while every real risk goes undetected. Grounded on Vasco's Context Graph, the agent surfaces the truth: a **21% revenue miss** ($180,390 new ARR vs. a $240,000 target), nine stalled deals, a live customer at billing risk, competitor-displacement risk, and a qualification gap (34% SQL→SAL) — because it can reason across every system that matters. Claude+MCP scored **21.9%** accuracy on standard RevOps questions; agents on the Vasco Context Graph scored **99.5%**. The difference is structure, not the model. ## What changes for your team Fewer fire drills, less data assembly, more time on decisions that move revenue. Vasco's agents map to a revenue org — leadership, sales, CS, marketing, and RevOps — and collaborate across it. Four industry benchmarks shift when every human has a teammate (modeled from Bridge Group, Pavilion, and RevOps Co-op research): - Quota-to-OTE multiplier: ~4–5x → ~7–10x - Manager-to-IC ratio: ~1:5 → ~1:10 - RevOps strategic time: ~30% → ~70% - Decision velocity: days → minutes For a $20–50M revenue company that models out to **$1.2M–$3.4M in annual impact** — [see the ROI for your company](/roi). ## Three teammates ready to start this week Pre-trained on your Context Graph, live in minutes: - **CRO/CEO Brief** (Executive Analyst) — a weekly revenue briefing: top issues, deals at stake, and the questions to ask. - **Board & Investor** — board prep in minutes, with an investor-ready narrative and quantified risks. - **ICP & TAM Discovery** (Marketing Analyst) — validate your ICP against real deal outcomes, size TAM per segment, and find where to double down. Plus 19 more agents (Data Medic, Metric Analyst, Pipeline Reviewer, Churn Detective, Rep Coach, and others) in the [Agent Marketplace](/agents/marketplace). ## How it actually works **Four capabilities that turn an LLM into a teammate** — without them you get confidently wrong answers; with them, 99.5% accuracy: 1. **Context Graph** — identity-resolved relationships across CRM, calls, tickets, and product usage. 2. **Metric definitions** — defined once, computed consistently; the same answer every time. 3. **Bowtie data model** — the full lifecycle from attract through expand, so agents see the whole motion. 4. **Plan** — targets, forecasts, and goals, so agents reason against what "good" looks like for you. **Work modes:** human delegation (the agent surfaces what matters when it matters), scheduled runs on a recurring cadence, and agent-to-agent hand-offs (e.g. Data Medic notifies Metric Analyst). **You stay in command:** agents propose, you approve. Every write is confirmed before execution and logged in an audit trail that shows exactly what data the agent saw and how it concluded. ## Custom agents When the role doesn't exist yet, design it — or have Vasco build it with you: - **Build it yourself** — describe the role in plain language; Gama generates the agent spec (objective function, KPI, job description, skills, data sources, tools); you review, tune, and deploy. - **Have us build it** — Forward-Deployed RevOps experts embed with your team and go from spec to live agent in days. Typical time-to-value: Day 1 for system agents (Data Medic), Day 1 for Metric Analyst once your plan is loaded, Week 1 for ICP Analyst once win/loss history is imported, and ~7–14 days for custom agents via Forward-Deployed RevOps. ## For agencies, fractionals, and consultants Deploy Vasco as the infrastructure layer for every engagement: one multi-tenant workspace with a separate Context Graph, agent fleet, and permissions per client; switch clients without re-platforming. White-label QBR/WBR templates and playbooks. The partner program adds revenue share on client retainers, priority support, and a dedicated CSM. (LeanScale runs Vasco across client engagements like ReversingLabs and Paramify, cutting onboarding from weeks to days.) ## FAQ - **How is this different from Claude + MCP?** MCP gives access; Vasco gives architecture. Same model, same questions: 21.9% accuracy on raw MCP vs 99.5% on the Vasco Context Graph. - **What does "works out of the box" mean?** System agents run from day one; role agents come pre-trained on your Context Graph once your sources are connected. - **Do I need a RevOps team?** No — Forward-Deployed RevOps can run the setup with you, and agents like Data Medic handle ongoing data hygiene. - **How long until real output?** Day 1 for system agents; Week 1 for role agents that need history (e.g. ICP Analyst); ~7–14 days for custom agents. - **What happens when our process changes?** The graph and definitions are versioned — update once, every agent reasons on the new reality. - **How does it work for agencies?** One multi-tenant workspace, separate Context Graph and permissions per client (see the agencies section above). ## Get started No setup fees, no engineering — first agent live in under 30 minutes. - [Get started for free](https://my.vasco.app/get-started) - [Book a demo](/request-demo) --- --- title: "AI Readiness Assessment" description: "A free, scored assessment of your GTM data quality and AI readiness — connect your stack and get a ranked report of the gaps AI agents depend on." canonical: "https://vasco.app/ai-readiness-assessment" --- # Is your GTM data ready for AI? The AI Readiness Assessment is a free diagnostic that scores your go-to-market (GTM) data quality and tells you exactly where the gaps are. Connect your stack and Vasco builds your context graph, then scores your data health across every dimension AI agents depend on to work. You get one report with a clear top-line score, a breakdown of every gap, and a ranked list of what to fix first — typically in under 15 minutes, with no credit card required. [Run your free assessment](https://my.vasco.app/get-started) ## Why AI readiness comes down to data 95% of AI projects fail, and the model is rarely the problem. AI agents return accurate answers only when the data they run on is complete, attributed, and linked. When it isn't — when records have no owner, revenue events can't be tied to an account, or dimensions are missing across thousands of contacts — agents fill the gaps with silence or, worse, with wrong answers. The real risk is that most teams don't know where those gaps are until a forecast is wrong or a report loses trust. The failure compounds quietly: 1. **Data gaps form unnoticed** — missing owners, unlinked events, and incomplete dimensions accumulate with no alerts and no visible errors. 2. **AI agents skip the affected records** — they return what they can and silently omit the rest; no error is thrown. 3. **A report turns out to be wrong** — broken reports and lost trust, with the root cause unknown. By the time it's visible, the problem has been compounding for weeks. ## What you get A complete assessment of your data quality and AI readiness, in minutes: - **Your Data Quality Score** — a single top-line score across your entire GTM data set, showing which objects and dimensions are dragging it down and by how much. - **Dimension Coverage Report** — every custom dimension scored for completeness, with records broken down by contacts, leads, companies, and deals, so you see the exact shape of your coverage gap. - **Prioritized Action List** — not just a diagnosis, but a ranked set of fixes sorted by impact, with one-click resolution paths for the issues that matter most. ## What the report looks like The assessment returns a Health Diagnosis: issues grouped by severity (Critical, Major, Minor) and sorted by impact, so you fix the critical issues that unblock the most reports first. Each issue names the affected system and the fix — for example, incomplete motion attribution to resolve in Stripe, records with no owner to fix in HubSpot, or events attributed to people Vasco doesn't recognize. Each row shows how many events it affects and how many fixes it takes to resolve. ## Trusted by revenue teams Revenue teams trust Vasco to build and deploy AI agents with 99.5% output accuracy. Reported results across teams using Vasco: - 99.5% accuracy on AI outputs - 200+ GTM teams assessed - 87% faster AI deployment Customers include Lightspeed, LeanScale, Vessel, Inovia, Wazo, and Billdr. ## How it works Up and running in three steps — most teams have their first report in under 15 minutes, with no lengthy onboarding: 1. **Connect your stack** — link your CRM, billing system, and GTM tools. Vasco pulls the data it needs to build your context graph. 2. **Get your scored report** — Vasco runs your assessment and returns a full breakdown: integrity score, dimension coverage, attribution gaps, and ownership issues. 3. **Know exactly what to fix** — your report comes with a prioritized action list and one-click resolution paths for the issues that matter most. ## Common questions **Is this actually free?** Yes. The assessment is free — no credit card, no trial expiry, and no sales call required to see your results. You get a full scored report as a free trial user. **What data do you need access to?** Vasco connects to your CRM and billing tools via standard OAuth integrations. It reads the data needed to score your GTM dimensions, does not write anything back, and does not store raw records beyond what is needed for the assessment. **How is this different from a CRM health check?** A CRM health check tells you about field completion. Vasco tells you about AI readiness — scoring your data across the dimensions AI agents actually use: motion attribution, ownership coverage, dimension completeness, and cross-system event linkage. Different questions, different answers. **What if my score is low?** That is the point of finding out. A lower score tells you exactly where to focus, and most teams find that a small number of fixable issues account for the majority of their risk. Knowing is better than deploying blind. ## Pricing Start with the free assessment and upgrade when you're ready to fix what it finds. Every plan starts with a 14-day trial, and no credit card is required to run your first assessment. - **Free ($0)** — connect your CRM and get a one-time GTM diagnostic. - **Growth (from $199/month)** — for one RevOps team connecting CRM, calls, tickets, and billing. Includes unlimited pre-built and custom agents and an MCP server. - **Scale (from $499/month)** — for multiple teams with warehouse, product, and marketing data. - **Enterprise (custom)** — custom SLAs, SSO, and tailored onboarding. To model the revenue impact of giving every quota-carrying rep a revenue agent, see the [Impact Model](https://vasco.app/roi). ## Get started Find out where you stand — it takes about 15 minutes, with no credit card and no sales call. [Run your free assessment](https://my.vasco.app/get-started) --- --- title: "Vasco Raises US$8M Seed Round to Transform Revenue Operations with AI-Powered Revenue Architecture" description: "Press release: Vasco closes a US$8M seed round led by Inovia Capital to accelerate product innovation, global expansion, and AI-driven capabilities for B2B revenue teams." canonical: "https://vasco.app/announcement" --- # Vasco Raises US$8M Seed Round to Transform Revenue Operations with AI-Powered Revenue Architecture **Press release — Montréal, Canada, January 14, 2025** Vasco, the end-to-end platform that empowers Revenue Operations (RevOps) teams to implement holistic revenue architecture strategies, today announced the close of a US$8M seed financing round. The round was led by Inovia Capital, with participation from BY Venture Partners, FRAMEWORK Venture Partners, and a network of business angels. This funding will empower Vasco to accelerate product innovation, expand its global reach, and enhance AI-driven capabilities to help B2B revenue teams achieve predictable, sustainable growth. The company also plans to deepen partnerships with RevOps agencies, fractional CROs, and strategic partners, broadening access to proven methodologies worldwide. "Most competing systems only scratch the surface of your business model, people, and product. Vasco goes deeper to deliver value," said Inovia Capital Partner Karam Nijjar. "They're creating the blueprint for unlocking sustainable growth." As markets mature and quick-fix hyper-growth tactics falter, businesses realize that real scalability demands structure, insight, and adaptability. To thrive today, RevOps teams must unify data, processes, and technology under a comprehensive framework. Vasco provides the infrastructure and insights to optimize go-to-market (GTM) functions, enabling stable, repeatable revenue growth. "Product-market fit is essential, but go-to-market fit can make or break a B2B company," said Guillaume Jacquet, Co-founder and CEO of Vasco. "We help startups and scale-ups not only find their GTM fit but also sustain high-velocity, scalable growth through every stage of their journey." Vasco's platform integrates AI-driven capabilities to provide full visibility into the GTM Bowtie — from prospecting to post-sale expansion — enabling real-time management, better decision-making, and tighter alignment across sales, marketing, and customer success. The company provides RevOps teams with a unified platform that integrates data and proven frameworks, transforming scattered processes into a guided, repeatable playbook for growth. It provides: - **Centralized, Real-Time Visibility:** a single integrated source combining CRM, marketing automation, and customer success data. - **AI-Driven Insights & Forecasting:** advanced analytics to uncover performance gaps, pinpoint root causes, and provide actionable recommendations. - **Built-In Methodologies & Best Practices:** proven GTM blueprints guiding best-in-class strategy execution. - **Continuous Improvement Loops:** ongoing monitoring and targeted suggestions that continuously refine approaches, reduce waste, and drive consistent, scalable revenue. Vasco is proud to be a Certified Partner of Winning by Design, Pavilion, and RevGenius — communities dedicated to advancing Revenue Architecture. Their frameworks, best practices, and thought leadership, embedded into the Vasco Platform, fuel modern go-to-market excellence. "I couldn't be more excited to support Vasco in their work to help companies grow more effectively as we head into what we hope will be a great year for business in 2025," says Sam Jacobs, CEO at Pavilion, the world's #1 private community for GTM leaders. "The key to profitable efficient growth begins with a unified data layer and a truly aligned GTM motion." Aside from Jacobs, other industry leaders are proud partners of the company including Dave Boyce, Executive Chairperson at Winning by Design, Special Advisor Aaron Ross, author of "Predictable Revenue" and "From Impossible to Inevitable", Jared Robin, Co-Founder of RevGenius, and long-term customer JD St-Martin, President of Lightspeed Commerce (NYSE:LSPD). "When it comes to full-bowtie visibility to growth metrics, there is no better platform than Vasco," said Boyce. "Guillaume and team have built something special here." ## About Vasco Vasco enables RevOps professionals to implement comprehensive Revenue Architecture strategies that unite GTM teams for efficient, predictable, and scalable growth. By integrating AI-driven capabilities, strategic frameworks, and deep operational expertise, Vasco transforms data into actionable insights, streamlines processes, and ensures teams continuously improve and maintain sustainable revenue growth. [Watch a demo](/watch-video) ## Trusted by the world's most innovative teams - **Dave Boyce, Executive Chairperson, Winning by Design:** "Vasco is very mature and implementable today — I am very impressed. Full visibility and management of the GTM bowtie on a near-real-time basis. Very excited for our partnership!" - **JD St-Martin, President of Lightspeed (NYSE:LSPD):** "Vasco is your blueprint to supercharge your revenue and the compass to avoid the pitfalls of high velocity sales. It drastically increases your odds of success by optimizing your go-to-market organization for sustainable growth." - **Aaron Ross, author of Predictable Revenue:** "Vasco aligns revenue teams, uncovers gaps, and seizes opportunities. It helps map your business around the customer journey, plan and execute growth phases, and set aggressive goals that drive results. Go Vasco!" - **Karamdeep Nijjar, Partner at iNovia Capital:** "Vasco is a game changer for companies facing high growth pressure. This platform not only instills confidence in the Board but also enables effective monitoring of growth, surface unit economics, and eliminates the need for painful reforecastings." - **FP Moffet, COO of Connect&Go (Series A):** "RevOps teams, check out Vasco! It's like having a GPS for scaling your revenue. Vasco bridges the gap between ambition and execution, making it an indispensable ally for every company aiming for meteoric revenue growth." - **Nicolas Marchal, CEO of Wazo (Seed):** "Vasco has been an absolute game-changer for us as we journey towards Series A. It's a new fundraising era, where fundamentals trump narrative. Vasco is the architect behind predictable, profitable scalability — ensuring we're not just another startup, but becoming a VC magnet." - **Uriel Manseau, Head of RevOps, Billdr:** "Being a smaller organization, Vasco initially acted as a catalyst for the standardization of the customer journey at Billdr. As well as having a beneficial effect on the structure of the organization, the path to our objectives is now much clearer and it's extremely easy to spot where we need to act to readjust." - **Ghaith Yafi, General Partner, BY Venture Partners:** "Vasco is a game changer for VC-backed companies. It guides startups and scale-ups in implementing and scaling a predictable revenue engine. Vasco helps avoid costly mistakes during growth and builds Board confidence for any VC-backed company with high-growth ambitions." --- --- title: "Builders — Deploy your agent on Vasco" description: "Developer-focused — connect to Vasco's MCP server, the data architecture, and why it works for GTM teams." canonical: "https://vasco.app/builders" --- # Deploy your agent anywhere. Ground it on Vasco. Claude or your own stack — wherever your agent runs, it needs the same revenue-data substrate underneath: consistent metrics, an account timeline from lead to cash, your actual plan, and integrity guarantees. Vasco provides it through one MCP endpoint: **`https://mcp.vasco.app/mcp`**, exposing **58 tools** across three layers. Works with any [MCP-compatible](https://modelcontextprotocol.io) client; the full tool reference lives in the [Vasco MCP docs](https://help.vasco.app/en/articles/11487839-vasco-mcp). ## Quick start (~30 seconds) Add Vasco as an MCP server, authenticate over OAuth (reads auto-approved; writes confirmed), then ask: ``` claude mcp add-json vasco '{"type":"http","url":"https://mcp.vasco.app/mcp","oauth":{...}}' ``` A real query composes multiple layers — e.g. "what's the win rate for expansion this quarter, and which at-risk accounts should we prioritize?" returns the metric (`expansion_win_rate` 0.42, n=12, +4pt QoQ), the plan gap ($82.5k of a $90k target booked), and the at-risk accounts (champion-change in the last 60 days), each with citations. Requires an authenticated Vasco user on a trial or paid workspace with sources connected; onboarding takes ~10 minutes and Vasco wires reconciliation for you. Supported clients include Claude Code, Claude Desktop, Cursor, a custom TypeScript SDK client, and raw `curl`. ## Why ground on Vasco: six categories of work you'd otherwise build Between your agent and a real answer sit six engineering problems — each its own project, **~9 months total for a v1**. Vasco ships all of it on day one, behind one endpoint: 1. **Identity resolution** (6–8 weeks to build yourself) — `Acme Corp` is `account_8842` in Salesforce, `cus_K9aBz` in Stripe, `workspace_117` in your product, plus 47 contacts across Gong, Slack, and Outreach. Without resolution your agent reasons over seven strangers, not one company. 2. **Account timeline** (10–14 weeks) — reconstruct lead-to-cash across CRM, calls, tickets, threads, and contracts, with conversations placed in stage. 3. **Metrics definition** (4–6 weeks) — one locked, versioned definition per metric. 4. **Planning** (3–5 weeks) — quotas, capacity, coverage, forecasts to reason against. 5. **Memory** (8–12 weeks) — outcomes remembered across time. 6. **Causality** (a research project in its own right) — the *why* behind a number, not just the value. ## How the data flows Sources in → reconciled → layered → served. Clients call `/mcp`; `/mcp` reads the three layers; the layers read a unified store; the unified store is the only thing that touches your sources. **Provenance:** every tool result carries source IDs and a citation, so you can drill from any number back to the row that produced it — even after reconciliation. ## The three layers, exposed as MCP Every tool call falls into one of three composable layers: - **Foundation** — *"What is the business? How does it perform?"* GTM Model (stages, channels, motions, functions, employees, dimensions, journeys), Source Mappings (CRM → GTM entity mapping, preview-before-save, versioned), Integrity Radar (data-quality score, issues, auto-reconciliation), and the Metric Engine (deterministic actuals, trends, conversions, breakdowns). - **Plan** — *"Where is the org headed? How is it tracking?"* Goals & Scenarios (targets, what-if plans, apply & rollback), Plan Inputs (conversion rates, quotas, costs, retention, channel spend), Forecasts (projected revenue/pipeline/customers by month and motion), and Benchmarks (industry comps at P25/P50/P75). - **Context** — *"Why do the numbers look the way they do?"* the Context Graph (entities, relationships, account context — traverse to find at-risk accounts, segment, or explain churn), Graph Schema (the intended first call before any traversal), Saved Queries (named, reusable, citable graph slices), and Artifacts (MetricQuery, ContextQuery, Reports, Slides — persisted, citable evidence). ## Why it works for GTM teams The same data layer, governed by RevOps. Builders connect once; then every rep, every agent, and every seat reasons against the same definitions, plan, and graph. RevOps governs what agents see — without re-platforming the org. Full tool reference (name, signature, scope, response shape) lives in the docs. ## Get started Connect in ~30 seconds using the quick start above (requires a Vasco workspace — trial or paid — with sources connected). [Browse all 58 tools in the docs](https://help.vasco.app/en/articles/11487839-vasco-mcp). --- --- title: "Centralize first. Buy the layer. Build the edge." description: "Why AI-native revenue teams centralize AI, buy the context layer, and build their edge on top — the operating model behind Owner.com's AI-native go-to-market, co-authored with CRO Kyle Norton." canonical: "https://vasco.app/case-studies/build-the-edge" --- # Centralize first. Buy the layer. Build the edge. *Co-authored by Kyle Norton (CRO, Owner.com) and Guillaume Jacquet (Founder, Vasco). 10 min read.* Most revenue teams ask whether to build or buy their AI. The more useful question comes first: is your AI centralized, or scattered. Get that right and the build-or-buy line draws itself — and the one piece of infrastructure that decides everything sits on the buy side. ## The scoreboard Owner.com runs an AI-native go-to-market. None of this came from buying more AI: - **4×** closed-won ARR per rep, vs competitors - **20×** ARR per dollar of AE comp - **2×** decision-maker connect rate - **$102K** ARR per outbound rep / month, up from $36K ## The argument in short Two decisions, in the right order. The first decided where AI lives in the org. The second decided what to build and what to buy — and where most teams put the wrong thing on the wrong side of the line. - **Centralize, or stall.** Decentralized AI spreads fast, then stops at the lower rungs. Nothing shared accumulates underneath it. - **Then draw one line.** Build the intelligence that is yours. Buy the infrastructure that is not. - **The context graph is infrastructure.** It runs all the time, takes specialist skill, and gives you no edge in the building. Buy it. - **The edge is what you put on top.** Your definitions, your ICP, your plan, your validated patterns, and the agents that run on them. - **The return is structural.** Accuracy, causality, and work you can delegate, on one layer every agent reasons over. **Is this you?** Past ~$10M ARR, three or more revenue systems, RevOps but no GTM data team. The decision is identical at $10M — size only changes how expensive the wrong call gets. ## Part 01 — Centralize, or nothing compounds Two operating models. One spreads literacy. The other compounds quality. - **Decentralized**: every rep and team builds their own AI. Literacy spreads fast. Quality stays uneven, and the org stalls at the lower rungs by design. - **Centralized**: a small team builds for everyone and delivers into the tools people already use. Quality compounds across surfaces, and the team keeps climbing. The split shows from the outside. Across go-to-market orgs: **47%** have zero agents in production, **89%** of agents never ship, only **2%** run more than twenty, and **53%** see no measurable return. BCG puts the profit gap between the least and most AI-mature orgs at 25 points. On the AI maturity ladder (L0 exploratory → L1 custom tools & prompts → L2 workflow automation → L3 infrastructure & leverage → L4 recursively improving), decentralized orgs climb on literacy and stall at L1–L2; centralized orgs built on one layer reach L3, then L4. (Source: Kyle Norton, RevStar Summit 2026.) Decentralization sounds like empowerment. A sales director handed Webflow's CRO an AI coaching tool the team had built. His reply was one question — "Are you on more customer calls?" The answer was no. Decentralized AI quietly turns your most expensive customer-facing people into part-time analysts. In the centralized model, reps do not run agents. They receive the output inside Salesforce or Slack and stay with customers. Centralization is the precondition. It is also where most teams stop, because they read it as an org-chart move. It is not. > Ask the same question in two parts of the company. Two numbers means two truths, whatever the org chart says. ## Part 02 — Draw one line Build your intelligence. Buy your infrastructure. Five questions tell you which is which: 1. **Uptime** — run around the clock, or fail and retry overnight? 2. **Uniqueness** — do you get something genuinely your own by building it? 3. **Engineering ROI** — what is the return on the engineering, over a real horizon? 4. **Reuse** — does it produce intelligence you reuse across surfaces? 5. **Edge** — does owning it give you an edge a vendor cannot replicate? Two worked examples: - **BUY: the dialer.** Up all the time, everyone needs the same one, terrible ROI to rebuild, no intelligence to own, no edge. Five buy signals. Owner did not build one. - **BUILD: pre-call research.** Runs overnight, market-specific signals, two weeks of engineering returned 85% more calls and 85% more opportunities, feeds CRM and coaching, no vendor builds it. Five build signals. So Owner built it. Infrastructure confers no edge in the building, so you buy it (dialer, data warehouse, the context graph). Intelligence compounds for you alone, so you build it (pre-call research, ICP & messaging, coaching & churn agents). The line is a boundary between two categories, not a dial you tune. ## Part 03 — The layer you buy: the context graph A central team and a shared layer are different things. Without the layer, centralized AI is a slide, not a system. That layer has a name: a **context graph** — your data foundation and your semantic layer, built so an agent reasons across them as one structure instead of querying systems in isolation. It does five things, each resting on the one before it: - **Identity** — one person, one account across CRM, calls, billing, and product, with a record of why two records were judged the same. - **Timeline** — every call and event placed in the account's journey, so the agent reads *why* it moved, not only that it did. - **The plan** — targets held in the layer, so "on track" has a number to check against. - **Outcome memory** — every deal tagged won, churned, expanded, or stalled, checked against billing. The graph knows which patterns led where. - **Causality** — a competitor named on a March call linked to the velocity drop two weeks later. - Plus a **semantic layer**: pin the definition of "pipeline" once — no-code, owned by RevOps — and every dashboard, workflow, and agent uses the same one. The harder question is who can change it. GTM moves faster than a data team can ship. A new motion, a channel, a reorg, a fresh signal: each one rewrites a definition, monthly not quarterly. Route every change through a data ticket with a one-to-two-month turnaround and your definitions are stale most of the year. So the semantic layer has to be no-code and owned by RevOps, where the judgment already lives. **What the absence looks like:** a team had HubSpot, Gong, Stripe, and Slack wired to an agent through MCP. They asked how the pipeline looked. The answer came back with no hedging: stable, on track. They were 21% below target. Nine deals untouched for thirty days. A recorded call had a prospect saying the budget was frozen — it never reached the deal it belonged to. A live account that had already cancelled payment in Stripe still showed as healthy, because the billing record and the CRM company were not the same entity to the agent. Read the full case study: [Claude Cowork is impressive — the infrastructure it assumes you have](/case-studies/claude-report). > "Speed is the new moat, and the layer is what makes it safe. A definition you wait a sprint to change is already wrong by the time it ships." — Kyle Norton, CRO, Owner.com Run the context graph through the same five questions. Every answer says buy: - **Uptime**: up around the clock. An alert fires on it. - **Uniqueness**: none. Identity resolution works the same for everyone. - **Engineering ROI**: poor. A first version takes 12–18 months, then drifts the day a source changes its schema. - **Reuse**: constant, but identical for everyone. Shared plumbing, not a differentiator. - **Edge**: zero. Owning the plumbing wins nothing. Under the visible case sits a quieter one that rarely makes a slide and always makes the bill: governance, security, and access control, then performance and cost. Identity has to stay auditable. Data has to respect who is allowed to see what. And the way the graph reads your sources decides both your latency and your spend. Pour every transcript in raw, with no optimization, and you get three problems at once: **slower answers** (every query drags more tokens through the model), **hallucinations** (footer-and-filler noise crowds the signal, so the model fills gaps with confident guesses), and **a 10× AI bill** (unoptimized extraction can multiply token spend by an order of magnitude). You buy the substrate, then author the rules on top of it. The graph does not replace the tools you run — it sits above them and reads from them: seven systems (CRM, call recorder, email, Slack, product, support, billing) reconciled into one account, then the timeline, stakeholders, signals, outcome memory, and causal links the agent reasons across. ### Why Vasco, not just "a context graph" Vasco is the context layer — the GTM context graph Owner runs — purpose-built so your agents reason on grounded revenue data, not inferences. > "It is the line we already draw. We buy our GTM context graph, Vasco, so we can build our agents on top." — Kyle Norton, CRO, Owner.com - **GTM-native** — identity, stages, and outcomes modeled for revenue, not a generic CDP schema you bend into shape. - **Outcome-tagged** — every deal reconciled against billing, so won, churned, and expanded are facts, not CRM guesses. - **No-code layer** — RevOps changes a definition in hours. A dbt build changes it in a sprint. - **Cost-optimized** — extraction tuned so transcripts and threads do not 10× your token bill or your latency. [Book a 30-minute demo](/request-demo) · [Start for free](https://my.vasco.app/get-started) ## Part 04 — Build the edge: what it returns On a bought layer, you build the parts that compound for you alone, and the agents that run on them: - **Deal coaching** — every deal inspected against the patterns that won before, not a 5% manager sample. - **ICP & messaging** — tuned to a scored profile, drafted to land. - **Churn detector** — usage and sentiment decline read before the renewal date. - **Whitespace** — upsell signals surfaced from the account graph, not found by hand. Building carries one discipline. Accuracy multiplies down a chain, so four steps each 95% reliable produce an answer that is 81% reliable. Ten steps at 80% collapse toward 10. The fix is generative work inside a deterministic frame: you define positioning and ICP, the AI drafts inside it, and you read the outputs until they hold. No evals, no program — today only 37% of teams run them, which is most of why 53% see no return. A worked example — a 10pm question: "Why did demos booked in the Northeast drop 18% this month?" The graph traces it, it does not guess: demos booked down 18% in the Northeast, flat everywhere else; the drop is entirely inbound; paid-search MQL volume held flat, so it is not top-of-funnel; MQL-to-demo conversion fell from 22% to 9% on one campaign; that campaign's landing page changed on the 6th, and form routing broke. Five whys, one query, every number sourced. You fix it tonight, not at the QBR. The org metrics move with it. Manager span goes from **1:5 toward 1:10**, because an agent inspects every deal and surfaces the ones that need a person. Reps go back to customers. The number a CFO pulls matches the number a rep pulls, because both query the same layer. For a $50M business, built lever by lever, the impact runs to roughly **$5M–$9M of ARR a year** before efficiency gains — illustrative, your inputs move it; the point is the order of magnitude. ### Where to start — four moves for a VP of RevOps 1. **Centralize ownership** under one AI-fluent leader, with a small team that builds for everyone and delivers into existing tools. 2. **Put the context layer in place first**, before any customer-facing automation, with the semantic layer no-code so RevOps owns the definitions. 3. **Buy the substrate, build the rules.** A vendor keeps identity resolution and maintenance running. You author definitions, ICP, plan, and validated patterns. 4. **Sequence the build.** Data foundations first, internal copilots next, customer-facing AI last and only with guardrails. ## The layer underneath The agent will keep answering with confidence. Whether it is earned depends on the layer beneath it. Vasco resolves identity across your sources, rebuilds the account timeline, holds the plan, and remembers outcomes — with a semantic layer your RevOps team owns without code. You author the rules. Vasco keeps the layer running. Start with the test: ask your own AI for your pipeline status, then peel it three layers down and watch where it breaks. - [Book a 30-minute demo](/request-demo) - [Read the Claude field report](/case-studies/claude-report) ## About the co-author Kyle Norton runs revenue at [Owner.com](https://www.owner.com), the vertical AI platform for independent restaurants, and the AI-native go-to-market behind the numbers in this piece. He also hosts [The Revenue Leadership Podcast](https://www.therevenueleadershippodcast.com/), where real revenue operators break down the frameworks they actually run. New episodes weekly. --- --- title: "Claude Cowork is impressive — here's the infrastructure it assumes you have" description: "A field report on the accuracy gap in AI revenue tooling and how a sourced semantic layer closes it." canonical: "https://vasco.app/case-studies/claude-report" --- # Claude Cowork is impressive — here's the infrastructure it assumes you have For RevOps teams building their GTM on top of AI agents, the question isn't whether Claude Cowork is useful — it's whether the data underneath it is trustworthy enough to act on. The gap isn't data *access*; it's data *architecture* — the layer between your tools and Claude that reconciles identities, enforces meaning, and remembers outcomes. What follows is one customer's story: anonymized details, real data, real consequences. ## Claude won't tell you it's wrong MCP is the connector layer — it gives Claude access to CRM records, call transcripts, billing events, and Slack threads. Most teams connect HubSpot, Gong, Stripe, and Slack and assume they've covered their bases. But four pipes are still just pipes: every MCP reports CONNECTED, and the gap is what sits *between* them. Claude doesn't say "I'm not sure" — it says "here's your pipeline summary," wrong in a way that looks exactly like being right. Five structural gaps: - **No shared definitions** — what's an SQL? How is NRR calculated? A "qualified lead" in HubSpot, a "high-intent signal" in Gong, and a "converted trial" in Stripe might be one account or three. Claude can't reconcile what nobody defined. - **No identity resolution** — the same contact is a HubSpot record, a Gong participant, and a Stripe customer. Without resolution Claude treats them as separate, so the Gong call where budget was flagged never connects to the deal it matters for. - **No plan or targets** — Claude can total closed-won from HubSpot but doesn't know your number, which motion carries it, or pace vs. target. - **No outcome memory** — Claude reasons on current state; it doesn't know the last four deals with this exact pattern (stalled at Stage 3, no exec contact, competitor in calls) all closed-lost. Every deal is assessed from scratch. - **No cross-tool sequencing** — HubSpot thinks in deals, Gong in calls, Stripe in subscriptions, Slack in threads. Nobody told Claude how they relate, how to sequence a deal journey, or which signal overrides which in conflict. ## What "confidently wrong" actually looks like A real (anonymized) setup: B2B SaaS, mid-seven-figure ARR, 38 active accounts, Claude Cowork connected to HubSpot, Gong, Stripe, and Slack via MCP. **What Claude reported:** "Pipeline is stable, you're on track" — 9 active deals, MQL→SQL 75% (healthy), quarter tracking to the $240K target with no alerts, Norden renewal on track. **What the data actually said that same week:** - A **21% revenue gap** — $180,390 actual vs. a $240,000 target. No tool encoded the target, so no tool flagged the miss. - **SQL→SAL conversion was 34%** — Claude reported the MQL→SQL number HubSpot tracks cleanly; the qualification-to-acceptance step needs a definition MCP doesn't carry. - **9 deals with zero engagement for 30+ days** — no calls, emails, or stage changes, yet HubSpot still showed them "active." No rule connects cross-tool silence to a stalled deal. - **Norden cancelled payment and closed its bank account** — the Stripe event existed and Claude had access, but a Stripe customer ID ≠ a HubSpot company without identity resolution. - **Kepler flagged budget as a "huge challenge"** on a recorded Gong call — call participants didn't map to HubSpot contacts, so the signal floated unconnected. - **Meridian competitor displacement** was called out in a Slack thread — no schema tied the thread to the HubSpot opportunity. The automation didn't just miss signals — it replaced the manual check that would have caught them. ## Won't Claude just get better at this? Probably — MCP is evolving and models keep improving, so cross-tool reasoning will get meaningfully better over 12–24 months. But there are categories of knowledge no model can conjure, because they don't live in your data: - **Your definitions** — what counts as an SQL, churn vs. downgrade. Business decisions, not data patterns. - **Your plan** — quota targets, motion benchmarks, segment thresholds. These live in spreadsheets and board decks, not connected systems. - **Your outcome history** — which patterns led to wins, losses, or churn. CRMs store deal status, not the causal chain. - **Your identity map** — revenue decisions need deterministic, auditable resolution; "probably the same account" isn't good enough for a billing-risk alert. Definitions, plans, outcome tags, and identity maps are infrastructure — they must be built, not inferred. ## What Claude needs underneath it: a revenue context graph Dashboards tell you *what happened*; context graphs tell you *why*. (HubSpot's Dharmesh Shah has written about context graphs — AI needs the relationships and decision traces that connect data to meaning — with the caveat that most companies aren't ready.) Revenue teams don't need a fully instrumented decision-trace graph; they need a bounded version that connects the five or six systems holding ~90% of revenue context and gives them shared schema, identities, and definitions. Three layers: - **Foundation** — connectors (HubSpot, Gong, Stripe, Slack) plus a single authored dictionary of what SQL, NRR, pipeline, and churn mean at your company. Where garbage-in stops. - **Planning** — what "on track" means: quota, motion-level pace, segment thresholds, ramp curves — a number Claude can compare against. - **Context** — the graph itself: shared identities, cross-tool sequencing, tagged outcomes, validated patterns. Plug Claude in here and it stops guessing and cites the structure. ## How the graph learns A graph that connects systems is useful; one that learns from outcomes is transformational. The loop: 1. **Outcome tagging** — every deal/customer gets a tagged outcome (Won, Churned, Expanded, Downgraded, Stalled) from CRM close reasons, validated against billing. 2. **Pattern extraction** — with 50+ won and 50+ lost tagged, the graph runs comparative (cohort-style) analysis, surfacing correlations with confidence scores tied to sample size. 3. **Correlation validation (human in the loop)** — RevOps validates whether a pattern is causal, not spurious; "CISO engagement pre-Stage 3 → 2.4× close" becomes a rule only when the team says so. 4. **Continuous recalibration** — as outcomes flow in, correlations update; a rule that held for six months may die after a pricing change, and the graph flags the degradation. ## What it changed in practice Same customer, six months of data — two findings no CRM report could surface: - **38% of pipeline was structurally unlikely to convert.** The winning profile (Series C+ Fintech/HealthTech, 200–500 employees, compliance pain) closed in 132 days at $62K ACV; outside the ICP the numbers collapsed. The move: reallocate ~60% of outbound SDR capacity to mid-market Fintech, stop targeting sub-50, and pivot messaging to "Compliance Automation for International Expansion." - **Speed, not effort.** Top performers closed 3× larger ACVs ($25K vs. $7,560) on lower activity volume — the differentiator was velocity (early qualification, executive stakeholder mapping, proactive ROI defense), written by the graph from patterns rather than gut feel. ## Build it yourself? You can, and some teams do — but the failure mode is the same: ship v1, it works for a quarter, then it drifts because nobody maintains it. If you've built all of this and maintain it continuously, you don't need a platform. If you haven't, that's what Vasco is for. ## Claude is a reasoning engine — give it something to reason on - **MCP gives access** — it plumbs Claude into your systems. Necessary, but never meant to be the architecture. - **Vasco gives meaning** — identities reconciled, definitions enforced, journeys sequenced, outcomes remembered. - **You set the rules** — definitions, plan, ICP, validated correlations. RevOps stays the author; Claude reasons on what you authored, not what it inferred. ## Questions from RevOps leaders - *We already connected our tools to Claude — isn't that enough?* You have access, not architecture. The case study had all four MCPs connected and still missed a 21% revenue gap. - *Won't MCP improve?* At access and basic entity matching, yes — but definitions, plan targets, outcome history, and validated identity maps are business decisions encoded as infrastructure, not patterns a model infers. - *Doesn't my BI/CRM tool already do this?* Those tools analyze the data they own; a context graph is an infrastructure layer underneath all of them — it connects, reconciles, and remembers, without replacing any tool. - *Should I stop using Claude?* No — it's useful today for drafting and summarizing. What to solve is *trusting* its output for pipeline reviews and forecasting, where a wrong number drives a wrong decision. - *How much history do I need?* Tagged outcomes mapped to complete deal journeys; a statistical floor of ~50+ per category. Below that the graph connects and reports; above it, it identifies what works. - *Who should own this?* RevOps. MCP connections inherit the authorizing user's permissions — treat Claude's access like any integration: explicit authorization, documented scope, named owner. ## See it on your data Use Claude now — just know where the trust boundary is. Vasco is the revenue context graph underneath your AI; see it reconcile your four MCPs into one queryable layer in a 30-minute demo on your actual data. [Book a 30-min demo](/request-demo). --- --- title: "Contact Us" description: "Get in touch with the Vasco team via the contact form." canonical: "https://vasco.app/contact-us" --- # Contact us We're here to help, whether you're setting up your account, exploring what Vasco can do for your team, or something else entirely. The page carries a contact form (name, email, message) — submissions go to the Vasco team. To book a demo directly, use [Request a demo](/request-demo) instead. --- --- title: "Vasco for AI-Native Revenue Teams" description: "Your revenue agents are only as reliable as the data they run on — Vasco is the GTM context graph built for AI-native revenue teams: 750+ integrations, unified and structured so agents can trust what they're working with." canonical: "https://vasco.app/for/ai-teams" --- # Your revenue agents are only as reliable as the data they run on. Vasco is the GTM context graph built for AI-native revenue teams — 750+ integrations, unified and structured so your agents can actually trust what they're working with. Primary actions: [Start for free](https://my.vasco.app/get-started) · [Watch a 2 min. demo](/form-video) ## Why AI teams' current approach breaks down Your pipeline agent knows the CRM. Your churn agent knows the CS platform. Your forecast agent knows the spreadsheet. None share context. When a decision requires all three — the outputs conflict and someone has to manually reconcile before anyone can trust the answer. ## What Vasco does for RevOps for AI teams From fragmented context to root cause in one click — powered by Claude and Vasco: - **One GTM context graph — 750+ integrations.** CRM, ERP, billing, calls, support, product. One source of truth. Arguments about the number end. - **Trusted agent data layer.** Deterministic, unified context your revenue agents can actually rely on. - **Works in Claude for Teams.** Your RevOps team stays in Claude. Vasco's unified intelligence is already there. ## Three teammates ready to start this week Pre-trained on your context graph and live in minutes, the AI-teams trio: Autonomous CRM (MEDDIC auto-capture), the Sales–Marketing Feedback Loop, and Win / Loss Analysis. [Explore all agents in the marketplace](/agents/marketplace). ## What customers say > "We run Vasco across every client engagement. One workspace, multiple deployments. Onboarding goes from weeks to days because the context graph does the heavy lifting before we walk in." > > — Joe Zaghloul, Partner & COO, LeanScale ## Ready to see the full picture? Connect your data free. No credit card required. [Start free](https://my.vasco.app/get-started) · [Get a demo](/request-demo) --- --- title: "Vasco for Customer Success" description: "Know an account is at risk — and exactly why — before anyone has to ask: product usage, support tickets, billing changes, call recordings, and engagement data in one account health picture with root cause built in." canonical: "https://vasco.app/for/customer-success" --- # Know an account is at risk – and exactly why – before anyone has to ask. Vasco connects product usage, support tickets, billing changes, call recordings, and engagement data into one account health picture — with root cause built in. Primary actions: [Start for free](https://my.vasco.app/get-started) · [Watch a 2 min. demo](/form-video) ## Why CS teams' current approach breaks down Your CS team runs a Claude analysis on the health score report. It looks stable. But the product usage data is in a different system. The support ticket backlog is in another. The call recording that flagged a frustrated champion is somewhere else. No one connected the dots – because no tool could. ## What Vasco does for Customer Success From fragmented context to root cause in one click — powered by Claude and Vasco: - **One GTM context graph — 750+ integrations.** CRM, ERP, billing, calls, support, product. One source of truth. Arguments about the number end. - **Self-generating reviews.** WBRs, MBRs, QBRs built from live unified data. Show up with the story. Leave with the decision. - **Trusted agent data layer.** Deterministic, unified context your revenue agents can actually rely on. ## Three teammates ready to start this week Pre-trained on your context graph and live in minutes, the CS trio: Churn Prediction, Customer Health Score, and Expansion & Upsell signals. [Explore all agents in the marketplace](/agents/marketplace). ## What customers say > "If you're trying to build a mature, scalable go-to-market system, Vasco is definitely worth it. What it gives you isn't just visibility. It makes you and your team strategic, because you stop spending your time chasing data and start spending it on decisions that matter." > > — Alban van Rijsewijk, Head of RevOps, Fabriq ## Ready to see the full picture? Connect your data free. No credit card required. [Start free](https://my.vasco.app/get-started) · [Get a demo](/request-demo) --- --- title: "Vasco for Marketing" description: "Stop funding the wrong accounts — Vasco connects campaign data to billing outcomes, product usage, support history, and renewal patterns, so your ICP is built from accounts that closed, stayed, and expanded." canonical: "https://vasco.app/for/marketing" --- # Stop funding the wrong accounts. Target the ones that close. Vasco connects campaign data to billing outcomes, product usage, support history, and renewal patterns — so your ICP is built from accounts that closed, stayed, and expanded. Primary actions: [Start for free](https://my.vasco.app/get-started) · [Watch a 2 min. demo](/form-video) ## Why marketing teams' current approach breaks down Your team runs a Claude analysis on MQL data and campaign performance. It looks compelling. But that data doesn't include what happened after the sale. Which accounts churned in month 4. Which expanded three times. Which segments have a 9-month CAC payback versus a 3-year one. That context never made it into the analysis. ## What Vasco does for Marketing From fragmented context to root cause in one click — powered by Claude and Vasco: - **Call recordings connected to outcomes.** Which conversations precede closed-won? Which ones predict churn? Vasco shows you. - **ICP from full-journey data.** Built from accounts that closed, expanded, and stayed — not just top-of-funnel signals. - **Bowtie attribution.** Trace every campaign to pipeline, closed revenue, and NRR impact. Not first-touch. The full picture. ## Three teammates ready to start this week Pre-trained on your context graph and live in minutes, the Marketing trio: the ICP Analyst, the Sales–Marketing Feedback Loop, and the Channel Attribution Model. [Explore all agents in the marketplace](/agents/marketplace). ## What customers say > "With Vasco, we prioritize what matters most, make high-leverage decisions, and avoid side quests that don't move the needle." > > — Joe Zaghloul, Partner & COO, LeanScale ## Ready to see the full picture? Connect your data free. No credit card required. [Start free](https://my.vasco.app/get-started) · [Get a demo](/request-demo) --- --- title: "Vasco for Product" description: "Stop reporting on features and start showing which ones drive revenue — Vasco connects product usage to billing outcomes, support history, and renewal patterns, grounding product decisions in commercial reality." canonical: "https://vasco.app/for/product" --- # Stop reporting on features. Start showing which ones drive revenue. Vasco connects product usage to billing outcomes, support history, and renewal patterns — so product decisions are grounded in commercial reality. Primary actions: [Start for free](https://my.vasco.app/get-started) · [Watch a 2 min. demo](/form-video) ## Why product teams' current approach breaks down Your team runs a Claude analysis on feature adoption data. Looks healthy. But that data doesn't include what happened to accounts with high adoption – did they expand? Churn? Renew flat? That context lives in billing, CS, and the renewal CRM. It never made it into the product analytics export. ## What Vasco does for Product From fragmented context to root cause in one click — powered by Claude and Vasco: - **Usage connected to revenue.** Feature adoption joined with billing, renewals, and expansion — see which usage patterns compound into revenue and which just look busy. - **Activation moments, surfaced.** The graph flags when accounts hit activation, stall, or show buying signals — while there is still time to act on them. - **Roadmaps grounded in commercial reality.** Walk into the roadmap review with evidence: which features drive expansion, which correlate with churn, and what that is worth. ## Three teammates ready to start this week Pre-trained on your context graph and live in minutes: Activation Signals, Usage Signals, and Usage to Revenue Patterns — product usage connected to revenue outcomes. [Explore all agents in the marketplace](/agents/marketplace). ## What customers say > "If you're scaling and want to know where your growth is really coming from, Vasco gives you clarity and control. For me, it's part of my morning routine. I wouldn't want to run elia without it." > > — Anthony Blais, Founder, elia ## Ready to see the full picture? Connect your data free. No credit card required. [Start free](https://my.vasco.app/get-started) · [Get a demo](/request-demo) --- --- title: "Vasco for Revenue Operations" description: "Own the one revenue data layer every team — and every agent — runs on: 750+ data sources unified into one bowtie-structured context graph, one source of truth for every team and every Claude session." canonical: "https://vasco.app/for/revops" --- # Own the one revenue data layer every team – and every agent – runs on. Vasco connects 750+ data sources into one bowtie-structured context graph. One source of truth. Every team and every Claude session running from the same complete picture. Primary actions: [Start for free](https://my.vasco.app/get-started) · [Watch a 2 min. demo](/form-video) ## Why RevOps' current approach breaks down The CRM says one thing. Billing says another. Marketing measures pipeline their way. Someone ran a Claude analysis on last month's export and now the board has a different version of the story. And it's your job to explain why four tools are saying four different things. ## What Vasco does for Revenue Operations From fragmented context to root cause in one click — powered by Claude and Vasco: - **One GTM context graph — 750+ integrations.** CRM, ERP, billing, calls, support, product. One source of truth. Arguments about the number end. - **Trusted agent data layer.** Deterministic, unified context your revenue agents can actually rely on. - **Works in Claude for Teams.** Your RevOps team stays in Claude. Vasco's unified intelligence is already there. ## Three teammates ready to start this week Pre-trained on your context graph and live in minutes, the RevOps trio: Revenue Health (finds where growth is stuck, with quantified ARR impact), Board & Investor reporting, and the Business Review Prepper. [Explore all agents in the marketplace](/agents/marketplace). ## What customers say > "Vasco has been a critical part of our journey, providing the structure and insights necessary to support Lightspeed's rapid growth. The platform's ability to bring clarity and accountability to our teams has been a game-changer." > > — JD St-Martin, President & CRO, Lightspeed ## Ready to see the full picture? Connect your data free. No credit card required. [Start free](https://my.vasco.app/get-started) · [Get a demo](/request-demo) --- --- title: "Vasco for Sales" description: "Stop guessing why deals stall — Vasco connects pipeline data to call recordings, product usage, support activity, and billing signals, so deal risk is a traceable pattern, not a gut feel." canonical: "https://vasco.app/for/sales" --- # Stop guessing why deals stall. Trace it to the source. Vasco connects pipeline data to call recordings, product usage, support activity, and billing signals — so deal risk is a traceable pattern, not a gut feel. Primary actions: [Start for free](https://my.vasco.app/get-started) · [Watch a 2 min. demo](/form-video) ## Why sales teams' current approach breaks down A deal has been in stage 3 for 6 weeks. Someone runs a Claude analysis on the CRM data and the call transcript. Claude says it looks healthy. But the product usage data shows the champion hasn't logged in since the last demo. Three support tickets were open. That context never made it into the analysis. ## What Vasco does for Sales From fragmented context to root cause in one click — powered by Claude and Vasco: - **Call recordings connected to outcomes.** Which conversations precede closed-won? Which ones predict churn? Vasco shows you. - **Deal risk, traced to the source.** Champion gone quiet, tickets piling up, usage dropping — every signal joined to the deal it belongs to, before the forecast slips. - **Forecasts that survive scrutiny.** Commit, best case, coverage — every number sourced from the same layer the CFO reads. Same answer every time. ## Three teammates ready to start this week Pre-trained on your context graph and live in minutes, the Sales trio: the CRO / CEO Brief, Deal Intelligence, and the Rep Coach. [Explore all agents in the marketplace](/agents/marketplace). ## What customers say > "When you're building a company, you need to know what's working and what isn't—fast. Vasco gives us that clarity. We know what inputs drive our pipeline, and we have the numbers to back it up." > > — Thierry Ajaltouni, Co-Founder, Vessel ## Ready to see the full picture? Connect your data free. No credit card required. [Start free](https://my.vasco.app/get-started) · [Get a demo](/request-demo) --- --- title: "Vasco — The Revenue Context Layer for AI Agents" description: "What Vasco is — the context layer purpose-built for AI agents; one context graph on top of your stack so revenue agents run in production on numbers that hold up." canonical: "https://vasco.app" --- # Put a GTM agent to work on anything you need Win/loss analysis, pipeline forecasting, churn detection, board-deck reporting, QBR prep, rep coaching, CRM auto-fill, and more. Vasco grounds each one in a context graph of 750+ sources, so the answers hold up when you check them. Primary actions: [Start for free](https://my.vasco.app/get-started) · [Watch a 2 min. demo](/form-video) ## The problem: agents are confidently wrong on raw CRM data Connect Claude (or any LLM) directly to your CRM and GTM stack via MCP and it will confidently get the numbers wrong. In a head-to-head accuracy test, Claude + raw MCP access scored **21.9%** (10 of 29 metrics correct) while the same questions against the Vasco Context Graph scored **99.5%** (29 of 29, with 3 small deltas being rounding artifacts). The connectors work; the architecture between them doesn't exist. See the field report: [Claude Cowork is impressive — the infrastructure it assumes you have](/case-studies/claude-report). ## Same Claude intelligence. Finally, the full picture. - **Connect everything** — every GTM system (CRM, calls, docs, product) lands in one place, automatically. [Learn more](/product/data) - **Unified on arrival** — related records link as they come in: accounts, contacts, revenue — connected, not just collected. [Learn more](/product/data) · [Deploy anywhere](/builders) - **Root cause in one click** — when a number moves, trace it to the signal that caused it. Not just what changed, but why. [Start for free](https://my.vasco.app/get-started) ## Who it's for Built for every team in the revenue motion: - **[Revenue Operations](/for/revops)** — See the number. Know why it moved. - **[Marketing](/for/marketing)** — Stop funding the wrong accounts. - **[Sales](/for/sales)** — Know where deals stall and why. - **[Customer Success](/for/customer-success)** — Catch churn before it happens. - **[Product](/for/product)** — Connect usage to revenue outcomes. - **[RevOps for AI teams](/for/ai-teams)** — Give your agents a trusted data layer. ## Done-with-you setup (Forward Deployed RevOps) Don't want to set it up yourself? Vasco's revenue-operations experts embed with your team and build alongside you. A typical first week: **CRM audit & cleanup** (fix stage mapping, reconcile sources), **motion & channel mapping** (define your GTM motions once, correctly), **your first agents** (first custom agent, workflow, and self-running report), and **ongoing** support as you evolve — a RevOps function from day one, not just onboarding. ## Security & compliance Vasco is audited and certified to industry-leading third-party standards (see the [Security](/legal/security) page for specifics). ## Get started You don't have 18 months to build this from scratch. Connect your CRM, describe your business, and deploy agents that reason — in days. - [See it live](https://my.vasco.app/get-started) - [Talk to sales](/request-demo) --- --- title: "Pricing" description: "Plans and pricing — pay for the AI you use, not the people who use it. Free, Growth, Scale, and Enterprise tiers." canonical: "https://vasco.app/pricing" --- # One platform. AI credits on top. Connect your revenue stack, let agents reason on a unified Context Graph, and pay for the AI you use — not the people who use it. All prices are in US dollars (USD), billed monthly or annually (save ~17% annually). Users are unlimited on every plan; everything is included, and higher tiers unlock more data sources, deeper context, and higher AI throughput. ## Plans - **Free — $0.** Connect your CRM and get a one-time GTM diagnostic. 500 AI credits (hard cap), unlimited users, 5 pre-built agents, 2 connectors (one-time sync), read-only Context Graph, growth planning & quotas, community support. 14-day trial. - **Growth — $239/month, or $199/month billed annually.** One RevOps team connecting CRM, calls, tickets, and billing. Everything in Free, plus ~2,000 AI credits/month (annual-billing credit tiers scale up: 4k/$290, 6k/$460, 12k/$880, 30k/$2,125), unlimited pre-built & custom agents, the MCP Server, 2 connectors (continuous sync), Context Graph + pattern memory, and priority email + Slack support. - **Scale — $599/month, or $499/month billed annually.** Multiple teams with warehouse, product, and marketing data. Everything in Growth, plus ~4,000 AI credits/month (annual-billing tiers scale up: 6k/$660, 12k/$1,080, and beyond), 5 connectors, Context Graph with SLA, and agent scheduling + priority. - **Enterprise — custom.** Custom SLAs, SSO/SAML, and tailored onboarding, with unlimited connectors, business intelligence, Pulse, custom integrations, warehouse sync (export), and a dedicated CSM with an onboarding program. Prices reflect the current published plans (live billing data may adjust them at any time — the page always shows the current figures). ## What is an AI credit? An AI credit is Vasco's unit of AI compute. Every time the platform runs an agent, generates an insight, or processes data through the Context Graph, it consumes credits — simple lookups cost less, complex multi-step workflows cost more: - **Quick lookup** ("What's our win rate?") — ~3 credits. - **Agent analysis** ("Why did pipe drop in EMEA?") — ~15–40 credits. - **Deep workflow** ("Build a WBR from CRM data") — ~70–135 credits. ## Done-with-you services Every engagement can start with a Graph & Agent Build: - **Graph & Agent Build** — Vasco configures the Context Graph on your data and builds the agents that run your revenue operations. - **CRM Setup for the Graph** — if your CRM needs restructuring first, Vasco rebuilds the data architecture so every field, stage, and workflow feeds the graph cleanly. - **Ongoing Advisory** — your graph evolves, agents improve, and you get board-ready reporting without hiring ops. ## Get started Start a 14-day trial on Growth and keep a free workspace when it ends. - [Start a trial](https://my.vasco.app/get-started) - [Talk to sales](/request-demo) for Scale and Enterprise. - See what your sales org makes in Year 1 with the [Impact Model](/roi). --- --- title: "Data — One unified GTM context graph" description: "The Vasco Data foundation — 750+ integrations unified into one bowtie-structured GTM context graph: the sourced, deterministic layer every module and every Claude session runs on." canonical: "https://vasco.app/product/data" --- # 750+ integrations. One unified GTM context graph. Connect your CRM, ERP, billing, call recordings, support, and product analytics into a single bowtie-structured context layer. The foundation every module, and every Claude session, runs on. Primary actions: [Start for free](https://my.vasco.app/get-started) · [Watch a 2 min. demo](/form-video) ## Powered by Claude The Vasco data foundation is built to power Claude-driven revenue work end to end, in three steps: 1. **Connect** — link your sources (HubSpot, Salesforce, Slack, Pipedrive, Notion, and the rest of the 750+ connectors). The Data Hub shows every account, contact, call, and signal reconciled into one browsable context graph, with per-account properties and connections resolved automatically. 2. **Create** — work from a plain-language composer ("Let's grow your revenue together"): create reports, visualize data, create skills and metrics, or start from ready-made agent templates. 3. **Deploy** — put agents to work on the graph. Example: an ICP Analysis report generated through chat — revenue health scorecard (starting/ending/net-new ARR), win-rate trends, and prioritized recommendations, every number reconciled against the Metric Engine. It also works with OpenAI, LangChain, n8n, Gemini, and other MCP-compatible tools — the context graph is the substrate; bring the model or client you prefer. ## The Revenue Context Graph — the brain for AI agents Every metric, signal, transcript, and contact is automatically reconciled and connected through a Context Graph, so revenue agents can reason about _why_, not just _what_. It is built in four layers: 1. **Data Structure** — all your data sources (CRM, call recorders, billing engines, data warehouses, support tickets), automatically reconciled and unified under a unique ID. 2. **Foundation Layer** — enforce consistent metric calculations, stage definitions, and go-to-market structure (GTM motions, teams, handoffs, the bowtie model), so every team works from the same definitions. 3. **Planning Layer** — compare actuals against targets (quotas, capacity, coverage, forecasts) to spot revenue leakage and prioritize actions by impact. 4. **Context Layer** — every call, message, product event, and signal — auto-connected and placed in each account's journey, alongside qualitative frameworks (MEDDIC, SPICED, BANT), playbooks, and risks. Why it matters, in numbers: **7x** more context per AI query than raw CRM access alone; **no-code** setup (describe your business in plain language — no CRM cleanup, data team, or migration); **100%** API-level accuracy on every metric query (same answer every time); live in **days**, not the 6–8 months a semantic layer normally takes to engineer. ## What you can do from day one Live the moment your sources are connected — no six-month implementation, no waiting on a data team: - **Connect your tools** — link your CRM, billing, conversations, and support tools in minutes. - **Ask your revenue data** and get sourced answers. - **Build agents** from templates or from scratch. - **Identity resolution** — 47 contacts across 2 CRMs reconciled into one account with one ID, however messy the data. - **Auto-reconcile conflicting sources** — when Salesforce says churned and Stripe says paid, see every conflict resolved. - **Run pipeline review with an AI agent** — stalled deals, missing context, and SLA breaches surfaced automatically. Vasco ships **750+ integrations** across CRM, billing, data warehouses, call recording, support, and product analytics. ## Two ways to build on the foundation One platform, two ways in: - **Out-of-the-box agents in Vasco** — specialized agents trained on your GTM context, with no setup or prompting, ready on day one. - **Bring your own tools via MCP** — connect Claude, Gemini, or any MCP-compatible client to Vasco's Context Graph. See [Builders](/builders). ## Get started - [Start for free](https://my.vasco.app/get-started) - [Talk to sales](/request-demo) --- --- title: "ROI Calculator" description: "Estimate the revenue impact of deploying Vasco across your team." canonical: "https://vasco.app/roi" --- # What does this do to your numbers? A revenue-impact model: what your sales org makes in Year 1 when every quota-carrying rep has a revenue agent as a teammate. Four metrics, one impact number — no payback or breakeven math (this models revenue impact, not cost recovery). [Talk to sales](/request-demo) — or model your own org with the interactive calculator on the page. ## The four metrics it moves For an illustrative $100M ARR company with 160 quota-carrying reps at a 14% productivity lift, the model projects roughly +$18M in Year-1 impact, driven by: - **Quota-to-OTE multiplier** rises from ~4.5x to ~6.4x — about **+$88K per rep, per year**. (A rep with a $1M quota and $200K OTE is at 5x; a 12% productivity lift compounded across coverage, attach rate, and cycle time pushes effective quota past $1.6M at the same OTE.) - **Manager-to-IC ratio** widens from ~1:5 to ~1:10 — roughly **16 frontline FTEs deferred**, as forecast roll-ups, deal reviews, and pipeline hygiene shift from the manager's calendar to the agent. - **RevOps strategic time** grows from ~40% to ~82% — about **10,080 hours freed per year**, as the agent absorbs ad-hoc dashboard requests and data reconciliation. - **CRO decision velocity** drops from days to minutes — about **$432K in pipeline preserved** that would otherwise slip during decision delay (median time-to-answer falls from ~2.4 days to ~90 seconds). ## The assumptions The model rests on a ~12% productivity lift per rep, backed by Gartner's 2025 GTM AI benchmark and Vasco customer telemetry across 14 deployments. The on-site calculator exposes this as a slider (drag to 8% for a conservative case) along with six org inputs (ARR, rep count, ACV, win rate, productivity lift, and % of RevOps time on ad-hoc work). ## Get the real number Book a 30-minute call with the solutions team. Bring your ARR, headcount, and quota plan; leave with a tailored impact model and a working sandbox on your data. [Talk to sales](/request-demo). --- --- title: "Workshop — Build a revenue agent" description: "An invite-only, in-person Vasco x Claude workshop: deploy a revenue agent that reasons on your real data in one half-day." canonical: "https://vasco.app/workshop" --- # Build a revenue agent: a Vasco x Claude workshop An invite-only, in-person pre-summit workshop. Your CRM may be live with Claude, but your context isn't. In four hours with the Vasco team you deploy a revenue agent that actually reasons on your data — live and ready to use by end of day. Applications are per city (see Locations & dates below). - **99.5%** accuracy gained when Claude reasons on a Vasco Context Graph vs. raw CRM data. - **4 hours** to a deployed, production-ready agent in one half-day session. - **10–12 companies** per cohort, with 1:1 support in breakout rooms. ## Who it's for For revenue operators at any stage of agentic adoption. Most B2B teams hit the same wall: the AI is connected but the data isn't ready — context is missing, quotas aren't encoded, and the agent confidently produces the wrong answer. This workshop fixes the foundation. It's for you if you use HubSpot or Salesforce, own your revenue data and reporting, have seen AI produce a confident-but-wrong answer, and want to leave with something that runs the next day. Skip it if you're still evaluating whether AI fits your stack, don't have access to your CRM data, or want a demo rather than a build. ## The accuracy gap (why your stack lies to your agents) Real examples from connecting HubSpot, Gong, Stripe, and Slack via MCP: - Asked for a pipeline summary, Claude reported $1.2M; the actual number was $740K — no uncertainty flagged. - Asked to rank rep performance, it put the wrong rep on top because closed-lost reasons weren't encoded. - Asked to flag churn risk, it marked three accounts healthy; two churned within 30 days. The signals existed; the context to surface them didn't. ## Agenda You don't spend the day setting up data — a free pre-event call connects your CRM, maps your stack, and scopes context beforehand, so the day is spent building: 1. **Setting the stage** — the accuracy gap, with five real scenarios where Claude had full CRM access and still produced the wrong answer; map your own stack and find the breaks. The fix: a Context Graph acting as the GTM brain for LLMs. 2. **The diagnosis** — a full GTM diagnostic with the Vasco team: funnel performance, channel/motion deep dive, and ICP discovery. 3. **Agent design sprint** — design and ship a custom agent for your specific revenue motion, with hands-on 1:1 support in breakout rooms. 4. **Cohort showcase** — present your agent (the problem it solves, the data it runs on, the before/after) and get judged. ## Facilitators - **Justin Hudon** — Head of Sales & Customer Success at Vasco; a career across frontline sales, CS, and GTM leadership (Lightspeed), now translating what revenue teams struggle with into the Context Graph, metric definitions, and lifecycle logic that turns 21.9% accuracy into 99.5%. - **Alec Oghassabian** — an architect of the agents you'll build; 7+ years in revenue infrastructure including six as Director of RevOps at Potloc (CRM architecture, forecasting, GTM alignment). Focused on fixing the data foundation before trusting the output. ## Locations & applications Invite-only, competitive selection (priority to Boreal portfolio companies and existing Vasco customers); free; max 2 per company: - **Toronto** — Monday, June 22, 2026 — [Apply now](https://luma.com/a8n405r3) - **Montreal** — Thursday, June 25, 2026 — [Apply now](https://luma.com/9os4i8b3) More cities are being added after RevStar; not in a listed city? Join the virtual-cohort waitlist. ## What you leave with - **Winner's prize** — two passes to RevStar Summit (2026 Toronto / 2027 Montreal); accommodation covered for Montréal attendees. - **Judging criteria** — a clean data foundation, an agent that solves a real recurring GTM problem, and a clear before/after story. - **Mandatory pre-session** — every accepted company completes a 60–90 min pre-workshop call (CRM connected, stack mapped, use cases scoped) so the day is spent building, not troubleshooting logins. - **Opt-in case study** — winners can opt into a co-branded case study, sanitized and approved by you before publication. --- # Data Processing Agreement ## 1. Definitions Capitalized terms not defined in this DPA have the meaning given in the Agreement. **"Applicable Data Protection Laws"** means all laws and regulations applicable to the Processing of Personal Data under the Agreement, including (where applicable) Regulation (EU) 2016/679 ("GDPR"), the United Kingdom General Data Protection Regulation and the UK Data Protection Act 2018 ("UK GDPR"), the Swiss Federal Act on Data Protection ("FADP"), the Act respecting the protection of personal information in the private sector (Quebec) ("Law 25"), and the Personal Information Protection and Electronic Documents Act (Canada) ("PIPEDA"). **"Customer Data"** has the meaning given to "Client Data" in the Agreement. **"Personal Data"** means any personal data or personal information contained in Customer Data that is Processed by Vasco on behalf of Customer. For purposes of Law 25, the term "Personal Data" includes "personal information" within the meaning of that statute. **"Personal Data Breach"** means a breach of security leading to the accidental or unlawful destruction, loss, alteration, unauthorized disclosure of, or access to, Personal Data. For purposes of Law 25, a Personal Data Breach is a "confidentiality incident". **"Process"** or **"Processing"** has the meaning given to it under Applicable Data Protection Laws. **"Subprocessor"** means any third party appointed by Vasco to Process Personal Data on behalf of Customer. --- ## 2. Roles of the Parties Customer acts as a Controller (or, under Law 25, as the enterprise that collects Personal Data and determines the purposes of its Processing). Vasco acts as a Processor (or, under Law 25, as a service provider acting on behalf of Customer) and Processes Personal Data only on documented instructions from Customer, including to provide the Services under the Agreement. --- ## 3. Scope of Processing The subject matter, nature, purpose, and duration of Processing, as well as the categories of Personal Data and Data Subjects, are described in Annex I. --- ## 4. Processor Obligations Vasco shall: - Process Personal Data only on documented instructions from Customer; - Ensure personnel authorized to Process Personal Data are bound by confidentiality obligations; - Implement appropriate technical and organizational measures as described in Annex II; - Assist Customer, taking into account the nature of Processing, with appropriate technical and organizational measures insofar as possible, for the fulfillment of Customer's obligation to respond to requests for exercising Data Subject rights; - Assist Customer in ensuring compliance with obligations relating to security of Processing, breach and confidentiality incident notifications, and (where applicable) data protection impact assessments and prior consultation, taking into account the nature of Processing and information available to Vasco; - Notify Customer without undue delay, and in any event within seventy-two (72) hours, after becoming aware of a Personal Data Breach affecting Customer's Personal Data. The initial notification shall include, to the extent then known to Vasco: (i) a description of the nature of the Personal Data Breach; (ii) the categories and approximate number of Data Subjects and Personal Data records concerned; (iii) the likely consequences of the Personal Data Breach; and (iv) the measures taken or proposed to be taken to address the Personal Data Breach and mitigate its possible adverse effects. Where information cannot be provided at the same time, it may be provided in phases without further undue delay. Any such notification shall not be construed as an admission of fault or liability by Vasco; - Upon termination or expiration of the Services, and at Customer's written election made no later than thirty (30) days following the effective date of termination, delete or return all Personal Data in Vasco's possession or control, unless retention is required by applicable law. Where Customer elects deletion, or where Customer fails to make an election within thirty (30) days of termination, Vasco shall delete the Personal Data within ninety (90) days following termination. Personal Data retained in routine backup media shall be deleted in accordance with Vasco's standard backup rotation cycle and shall not be restored or used for any purpose during that period. Upon written request, Vasco shall provide Customer with a written certificate of deletion. --- ## 5. Subprocessors Customer authorizes Vasco to engage the Subprocessors listed in Annex III as of the Effective Date. Vasco will remain responsible for the acts and omissions of its Subprocessors with respect to Personal Data to the same extent Vasco would be responsible if performing the services of each Subprocessor directly under this DPA, and will impose data protection obligations on each Subprocessor that are substantially equivalent to those imposed on Vasco under this DPA. Vasco will provide Customer with at least thirty (30) days' prior written notice of the appointment or replacement of any Subprocessor that will Process Personal Data. Customer may subscribe to such notices via the mechanism made available at https://trust.vasco.app or by emailing privacy@vasco.app. If Customer has a reasonable, documented data protection objection to the appointment or replacement of a Subprocessor, Customer shall notify Vasco in writing within fifteen (15) days of Vasco's notice, setting out the grounds for the objection. The parties shall work together in good faith to resolve the objection. If the parties are unable to reach a mutually acceptable resolution within a reasonable period not to exceed thirty (30) days, Customer may, as its sole and exclusive remedy, terminate the affected portion of the Services for convenience, with a pro-rata refund of any pre-paid fees applicable to the unused portion of the then-current term. --- ## 6. International Transfers Customer acknowledges that Personal Data is hosted and Processed in the United States. Where required under Applicable Data Protection Laws, Vasco relies on appropriate safeguards for international and cross-border transfers of Personal Data, as set out in Annex IV. --- ## 7. Audits Customer may request information reasonably necessary to demonstrate Vasco's compliance with this DPA. Any audit rights are limited as follows: - Audits may occur no more than once per twelve (12) months; - Audits shall be limited to document reviews or written responses, unless a higher level of audit is required by Applicable Data Protection Laws; - Where Vasco makes available a recent independent third-party security or compliance report (including its SOC 2 Type 2 report), Customer agrees to accept such report in lieu of conducting an audit; - On-site audits are excluded unless expressly required by a competent supervisory authority; - Audits must be conducted during normal business hours, subject to reasonable advance notice, and must not unreasonably interfere with Vasco's business operations; - Customer shall bear all costs associated with any audit. --- ## 8. Canadian Privacy Law **8.1 General.** Vasco HQ Inc. is incorporated in Quebec, Canada, and is subject to Law 25 and (where applicable) PIPEDA. This Section 8 supplements, and does not limit, the obligations of Vasco set out elsewhere in this DPA. **8.2 Person in charge of the protection of personal information.** Vasco has designated a person in charge of the protection of personal information as required by Law 25. The contact details are: Sebastien Rothlisberger, Chief Technology Officer and Data Protection Officer, privacy@vasco.app. **8.3 Confidentiality incidents.** A Personal Data Breach as defined in this DPA constitutes a "confidentiality incident" for purposes of Law 25 and a "breach of security safeguards" for purposes of PIPEDA. Vasco shall notify Customer of any such incident in accordance with Section 4 in order to support Customer's compliance with its own notification obligations to the Commission d'acces a l'information du Quebec ("CAI"), the Office of the Privacy Commissioner of Canada ("OPC"), and affected individuals. Vasco shall maintain a register of confidentiality incidents as required by section 3.8 of Law 25, and shall make the relevant entries of that register available to Customer or to the CAI upon lawful request. **8.4 Privacy impact assessments.** Where Customer is required to conduct a privacy impact assessment ("PIA") under Law 25 — including in connection with the communication of Personal Data outside Quebec under section 17 of Law 25 — Vasco shall provide reasonably available information regarding its technical and organizational measures, Subprocessors, hosting locations, and applicable transfer safeguards to support that PIA. **8.5 Communication outside Quebec.** The parties acknowledge that Customer Data is hosted in the United States. The safeguards applicable to such communication are described in Annex IV.D. **8.6 Cooperation with supervisory authorities.** Vasco shall provide reasonable cooperation, taking into account the nature of Processing and the information available to Vasco, in connection with inquiries or investigations by the CAI, the OPC, or any other competent Canadian supervisory authority relating to Customer's Processing of Personal Data through the Services. --- ## 9. Liability This DPA does not create additional liability beyond what is set out in the Agreement. --- ## 10. Order of Precedence In the event of any conflict between this DPA and the Agreement, this DPA shall prevail with respect to data protection and privacy matters. In the event of any conflict between this DPA and the Standard Contractual Clauses incorporated under Annex IV.A, the Standard Contractual Clauses shall prevail. --- ## 11. Governing Law This DPA is governed by the law specified in the Agreement, except where Applicable Data Protection Laws or the Standard Contractual Clauses require otherwise. --- ## Annex I — Details of Processing **Subject matter:** Provision of the Vasco platform and related services. **Duration:** For the term of the Agreement. **Nature of Processing:** Hosting, storage, structuring, analysis, and presentation of Customer Data; user access and administration; support and maintenance. **Categories of Data Subjects:** - Customer employees - Customer prospects, leads, and contacts **Categories of Personal Data:** - Contact information (name, email, role) - CRM and engagement metadata - Usage and activity data **Purpose of Processing:** - Provision, maintenance, and support of the Services **Customer responsibility and data restrictions:** Customer determines the scope and content of Personal Data submitted to the Services and is responsible for ensuring that such Processing complies with Applicable Data Protection Laws. The Services are not designed to Process, and Customer shall not submit, any special categories of personal data or other highly sensitive data, including without limitation health data, payment card information, government-issued identification numbers, biometric data, or criminal records, unless expressly agreed in writing by Vasco. --- ## Annex II — Technical and Organizational Measures Vasco implements appropriate technical and organizational measures designed to protect Personal Data against accidental or unlawful destruction, loss, alteration, unauthorized disclosure, or access. These measures are aligned with industry best practices and are informed by Vasco's internal security policies, including information security, encryption, incident response, access management, logging and monitoring, vulnerability management, business continuity, and data retention. Key measures include: - **Access controls:** Role-based access, least-privilege principles, multi-factor authentication for administrative access, and formal joiner/mover/leaver processes. - **Encryption:** Encryption in transit using TLS 1.2+ and encryption at rest using industry-standard algorithms. - **Monitoring & logging:** Centralized logging, monitoring, and alerting for security-relevant events with restricted access to logs. Automated detection and redaction of personal data within log streams. - **Vulnerability management:** Periodic vulnerability scanning, remediation based on risk prioritization, and annual independent penetration testing. - **Incident response:** Documented incident response procedures, escalation paths, post-incident reviews, and an annual incident response simulation covering platform outage, third-party component failure, and data-breach scenarios. - **Data lifecycle controls:** Defined data retention and deletion processes aligned with contractual requirements. - **Business continuity:** Documented business continuity and disaster recovery planning, with periodic testing. - **Independent assessment:** Annual SOC 2 Type 2 audit covering the Security and Availability Trust Services Criteria. These measures are reviewed periodically and updated as necessary to maintain an appropriate level of security. --- ## Annex III — Subprocessors Vasco's current Subprocessors are listed at: [vasco.app/legal/subprocessors](/legal/subprocessors) --- ## Annex IV — International Data Transfer Mechanisms ### A. European Economic Area (EU SCCs) Where Personal Data subject to the GDPR is transferred from the EEA to Vasco in the United States, the parties incorporate by reference the Standard Contractual Clauses approved by the European Commission in Implementing Decision (EU) 2021/914 ("EU SCCs"), Module Two (Controller to Processor), with the following selections: 1. **Clause 7 (Docking Clause):** included. 2. **Clause 9 (Use of Sub-processors):** Option 2 (general written authorization) applies. The notice period for changes to Sub-processors is thirty (30) days, as set out in Section 5 of this DPA. 3. **Clause 11 (Redress):** the optional independent dispute resolution body is not selected. 4. **Clause 17 (Governing Law):** the EU SCCs are governed by the laws of Ireland. 5. **Clause 18 (Choice of Forum and Jurisdiction):** disputes arising from the EU SCCs shall be resolved by the courts of Ireland. For the purposes of the EU SCCs, Customer is the "data exporter" and Vasco HQ Inc. is the "data importer." The Annexes of this DPA serve as the Annexes of the EU SCCs as follows: - **Annex I.A (List of Parties):** as identified in the Agreement. - **Annex I.B (Description of Transfer):** as set out in Annex I of this DPA. - **Annex I.C (Competent Supervisory Authority):** the Irish Data Protection Commission, except where Customer is established in an EEA member state, in which case the competent supervisory authority of that member state. - **Annex II (Technical and Organizational Measures):** as set out in Annex II of this DPA. - **Annex III (List of Sub-processors):** as set out in Annex III of this DPA. In the event of any conflict between the EU SCCs and the remainder of this DPA, the EU SCCs shall prevail. ### B. United Kingdom (UK Addendum) Where Personal Data subject to the UK GDPR is transferred from the United Kingdom to Vasco in the United States, the parties incorporate by reference the International Data Transfer Addendum to the EU Commission Standard Contractual Clauses issued by the UK Information Commissioner's Office under section 119A of the UK Data Protection Act 2018 ("UK Addendum"). The information required by Table 1 of the UK Addendum is as set out in the Agreement and this DPA. With respect to Table 4 of the UK Addendum, neither party may end the UK Addendum as set out in Section 19 of the UK Addendum. ### C. Switzerland (FADP) Where Personal Data subject to the FADP is transferred from Switzerland to Vasco in the United States, the EU SCCs apply with the following modifications, consistent with guidance from the Swiss Federal Data Protection and Information Commissioner ("FDPIC"): - references to the GDPR shall be read as references to the FADP, and references to specific GDPR articles shall be read as references to the equivalent provisions of the FADP; - references to EEA member state supervisory authorities shall be read as references to the FDPIC; - the courts and law of Switzerland shall apply to disputes brought under Clauses 17 and 18 in respect of transfers governed solely by the FADP. ### D. Quebec — Communication of Personal Information Outside Quebec (Law 25, s. 17) Where Customer is established in Quebec or otherwise subject to Law 25 in respect of Personal Data communicated to Vasco, the parties acknowledge that the communication of Personal Data to Vasco in the United States constitutes a communication of personal information outside Quebec for purposes of section 17 of Law 25. Vasco confirms that it implements the technical and organizational measures described in Annex II to maintain a level of protection of Personal Data that is consistent with the principles applicable in Quebec under Law 25, including: - encryption in transit and at rest; - role-based access controls and least-privilege principles; - contractual obligations imposed on its Subprocessors that are substantially equivalent to those imposed on Vasco under this DPA; - documented incident response procedures and a register of confidentiality incidents; - an annual independent security assessment under SOC 2 Type 2 (Security and Availability). Vasco shall, upon written request and within a reasonable period, provide Customer with information reasonably necessary to support Customer's privacy impact assessment of the communication of Personal Data outside Quebec under section 17 of Law 25, including information on hosting locations, Subprocessors, applicable safeguards, and the legal framework of the destination jurisdiction to the extent known to Vasco. --- # Politique de confidentialité ## 1. Introduction AVANT D'UTILISER LE SITE WEB ET NOS APPLICATIONS, VEUILLEZ LIRE ATTENTIVEMENT NOTRE POLITIQUE DE CONFIDENTIALITÉ (la "Politique"). Cette Politique traite de la protection des Renseignements personnels par **Vasco HQ Inc.**, faisant affaire sous le nom de **Vasco** (ci-après "Vasco" ou "nous"). Nous accordons une attention particulière à la protection de vos Renseignements personnels (définis ci-dessous) recueillis par l'entremise de notre site web **vasco.app** et de nos applications (ci-après collectivement les "Applications") et par l'entremise des Produits Vasco (ci-après collectivement, avec les Applications, la "Plateforme"). Toutefois, cette Politique s'applique, dans son intégralité, uniquement aux Renseignements personnels des utilisateurs des Applications (ci-après : "vous"). Elle vise à expliquer comment nous recueillons, utilisons et communiquons vos Renseignements personnels. Lorsque nous traitons les données de clients pour le compte de nos clients, un tel traitement est régi par l'Accord de traitement des données ([DPA](/legal/dpa)), et non par cette Politique. Si vous êtes un Utilisateur autorisé d'un Client, toute section de cette Politique s'applique à vous uniquement lorsqu'elle prévoit spécifiquement qu'elle s'applique à ces utilisateurs. Chaque Client est responsable de se conformer aux obligations légales applicables aux personnes qui recueillent des Renseignements personnels sur autrui et, à cet égard, est responsable d'obtenir un consentement valide pour leur collecte, communication et utilisation. De plus, le Client est responsable d'établir sa propre politique de confidentialité, de déterminer les mesures de sécurité applicables aux Renseignements personnels et de fournir aux personnes concernées par ces informations les moyens d'exercer leurs droits. De plus, cette Politique ne s'applique pas aux Renseignements personnels concernant nos employés, ni aux Renseignements personnels concernant nos Sous-traitants (tels que ces termes sont définis à la section 6 de cette Politique). Enfin, cette Politique vise à se conformer aux lois canadiennes et québécoises sur la protection des renseignements personnels et, le cas échéant, au Règlement général sur la protection des données ("RGPD"). Aux fins de cette Politique, les définitions suivantes s'appliquent : ### 1.1. "Administrateur de compte" Un membre d'une Organisation à qui l'Organisation accorde le droit d'administrer le compte, lequel droit comprend les prérogatives suivantes : - modification des informations du Compte utilisateur; - ajout d'Utilisateurs autorisés et de leurs Profils utilisateur; - modification des informations de paiement; - exécution de toute opération liée à la relation d'affaires de l'Organisation avec Vasco. ### 1.2. "Produits Vasco" Désigne : - Les solutions et logiciels en tant que service (SaaS) de Vasco pour les opérations de revenus (RevOps), incluant le Data Hub, Planning Hub, Review Hub, Execution Hub, BI Hub et Gama AI (analyste de revenus IA); - Les solutions Vasco offertes en tout ou en partie sous forme d'applications pour téléphones intelligents ou tablettes; - Toute autre solution offrant de nouvelles fonctionnalités pouvant être ajoutée sous forme de module aux solutions listées ci-dessus; et - Le soutien aux Utilisateurs autorisés et la maintenance connexe fournie par Vasco. ### 1.3. "Utilisateur autorisé" Un membre d'un Client, d'une Organisation ou d'une entité affiliée autorisé par un Administrateur de compte à utiliser les Produits Vasco après que le Client a payé tous les frais applicables. Un Utilisateur autorisé peut être membre de plusieurs Clients, Organisations ou entités affiliées, selon le cas. ### 1.4. "Client" Une Organisation désignée sur le formulaire d'inscription ayant reçu un courriel confirmant la commande des Produits Vasco. ### 1.5. "Organisation" Une personne exploitant une entreprise, société en commandite, société à responsabilité limitée, société de personnes, syndicat, organisation patronale, entreprise individuelle, société par actions ou compagnie (avec ou sans capital-actions), personne morale, coopérative, fiducie, association non constituée en société, coentreprise, organisme sans but lucratif, autorité gouvernementale ou toute autre entité, quelle que soit sa forme juridique, son statut de constitution ou les juridictions dans lesquelles elle exerce ses activités, exerçant une activité organisée de quelque nature que ce soit et utilisant les Produits Vasco. Un Utilisateur autorisé qui gère les comptes d'autres Utilisateurs autorisés est considéré comme une Organisation. ### 1.6. "Renseignements personnels" Toute information concernant une personne physique permettant directement ou indirectement son identification. Aux fins de cette Politique, les Renseignements personnels correspondent aux "données à caractère personnel" au sens du RGPD. ### 1.7. "Profil utilisateur" L'ensemble des Renseignements personnels concernant un Utilisateur autorisé transcrits sous forme intelligible et structurée, accessibles et modifiables via la Plateforme. ## 2. Renseignements personnels que nous recueillons Nous ne recueillons que les Renseignements personnels vous concernant nécessaires pour établir, gérer et maintenir notre relation avec vous. Cette collecte se limite, dans la plupart des cas, aux Renseignements personnels suivants : - Prénom, Nom; - Adresse courriel, numéros de téléphone; - Informations bancaires/de facturation, le cas échéant; et - Témoins de connexion (voir la section 9 de cette Politique pour plus de détails). Nous pouvons recueillir des Renseignements personnels par l'entremise des Applications, lors de la signature d'un contrat ou, plus généralement, lorsque vous interagissez avec l'un de nos employés ou représentants par courriel, téléphone ou en personne. Les Renseignements personnels saisis par les Utilisateurs autorisés dans leurs Profils utilisateur relèvent de la responsabilité de leurs Organisations respectives. ## 3. Votre consentement Votre consentement à la collecte, à l'utilisation ou à la communication de vos Renseignements personnels doit être libre, éclairé et donné à des fins spécifiques. Nos politiques et contrats sont rédigés en langage clair afin de vous aider à mieux comprendre la nature, les finalités et les conséquences de la collecte, de l'utilisation et de la communication de vos Renseignements personnels. Selon la nature et la sensibilité de vos Renseignements personnels, votre consentement peut être explicite (un tel consentement peut être donné verbalement, par écrit ou par voie électronique) ou implicite (lorsque vous fournissez volontairement des Renseignements personnels, par exemple). Généralement, nous demanderons votre consentement, sauf si la loi l'exige ou le permet autrement. Si vous êtes un Utilisateur autorisé, nos Conditions générales exigent que votre Organisation obtienne votre consentement, et nous présupposons qu'elle agit dans les limites établies par la loi. Si vous êtes témoin ou victime d'un manquement à cet égard, vous pouvez nous en aviser en utilisant les coordonnées fournies à la fin de cette Politique. En utilisant les Applications, vous consentez à l'utilisation de vos Renseignements personnels conformément à cette Politique. ## 4. Sécurité et gouvernance La cybersécurité est une priorité pour nous. Nous avons adopté un ensemble complet de politiques et de pratiques pour guider la gouvernance des Renseignements personnels. Ces politiques définissent comment nous protégeons et gérons l'information tout au long de son cycle de vie et font partie de notre programme de sécurité de l'information aligné sur la norme SOC 2. Notre cadre de gouvernance prévoit : - L'utilisation, la communication, la conservation et la destruction des Renseignements personnels conformément aux politiques documentées; - Des rôles et responsabilités clairement définis pour les employés et contractuels tout au long du cycle de vie de l'information; et - Un processus documenté pour signaler et gérer les incidents ou plaintes concernant la protection de l'information. Ces politiques et pratiques comprennent : - Politiques de classification et de protection des données : Définissent comment l'information est catégorisée selon sa sensibilité et décrivent nos obligations de protéger et de maintenir des registres des opérations effectuées sur cette information. - Politique de sécurité de l'information : Établit des processus pour protéger la confidentialité, l'intégrité et la disponibilité de l'information et des systèmes que nous gérons. - Processus d'évaluation de la sécurité des fournisseurs et sous-traitants : Décrit comment nous évaluons et approuvons les sous-traitants et fournisseurs tiers, y compris les exigences de sécurité qu'ils doivent respecter avant de traiter des Renseignements personnels. En plus de ces mesures administratives, nous avons mis en place des mesures de protection physiques et technologiques appropriées proportionnelles à la sensibilité, la finalité, la quantité et le format des Renseignements personnels traités. Nous prenons toutes les mesures raisonnables pour minimiser le risque d'incident de confidentialité. Par exemple, nous appliquons les principes de protection de la vie privée dès la conception, en veillant à ce que les paramètres de la Plateforme offrent le plus haut niveau de confidentialité par défaut. ## 5. Utilisation des Renseignements personnels Nous utilisons vos Renseignements personnels principalement pour fournir, maintenir et améliorer notre Plateforme et nos services. Plus spécifiquement, nous pouvons utiliser vos informations pour : - Fournir des services : Établir et gérer votre compte, authentifier votre identité et fournir les fonctionnalités des Produits Vasco auxquels vous êtes abonné. - Soutien : Répondre à vos commentaires, questions et demandes, et fournir un service client et un support technique. - Communication : Vous envoyer des avis techniques, des mises à jour, des alertes de sécurité et des messages administratifs. - Amélioration : Surveiller et analyser les tendances, l'utilisation et les activités liées à notre Plateforme pour améliorer l'expérience utilisateur et développer de nouvelles fonctionnalités. - Facturation : Traiter les paiements et gérer les relations de facturation. - Conformité légale : Se conformer aux obligations légales et réglementaires, résoudre les litiges et faire respecter nos accords. ## 6. Communication de vos Renseignements personnels Nous pouvons communiquer des Renseignements personnels à des tiers dans des circonstances spécifiques permises par la loi. **Aux prestataires de services, mandataires, sous-traitants (les "Sous-traitants")** Description et finalité : Nous pouvons conclure des contrats avec des Sous-traitants pour fournir un service à nos Clients, comme une fonctionnalité de la Plateforme (par exemple, l'hébergement, le traitement des paiements). Ces Sous-traitants peuvent également vous fournir un service directement en notre nom. Mesures : Le contrat exige que les Sous-traitants : - N'utilisent que les Renseignements personnels nécessaires à la prestation du service. - S'abstiennent de divulguer ou communiquer des Renseignements personnels sans notre consentement. - Mettent en œuvre des mesures de sécurité rigoureuses et nous permettent de vérifier ces mesures. - Nous notifient immédiatement de tout incident de confidentialité. - Détruisent les Renseignements personnels à la fin d'un contrat. **Une autre partie dans une transaction commerciale** Description et finalité : Nous pouvons conclure un contrat avec un tiers aux fins d'une transaction commerciale (par exemple, fusion, acquisition, financement). Mesures : Nous exigeons que l'autre partie utilise les Renseignements personnels uniquement aux fins de la conclusion de la transaction et mette en œuvre des mesures de sécurité rigoureuses. **Autorités légitimes** Description et finalité : Pour se conformer à une ordonnance du tribunal, un mandat de perquisition ou une décision réglementaire, nous pouvons être tenus de fournir des Renseignements personnels. Mesures : Nous refusons de fournir un accès lorsque la demande n'est pas valide. Nous informons les Clients des demandes concernant leurs Utilisateurs autorisés, sauf si la loi interdit une telle notification. ## 7. Conservation Nous ne conserverons vos Renseignements personnels que le temps nécessaire aux fins énoncées dans cette Politique. - Durée du compte : Nous conservons vos Renseignements personnels tant que votre compte est actif ou selon les besoins pour vous fournir les Produits Vasco. - Obligations légales : Nous conserverons et utiliserons également vos Renseignements personnels dans la mesure nécessaire pour nous conformer à nos obligations légales (par exemple, si nous sommes tenus de conserver vos données pour nous conformer aux lois applicables), résoudre les litiges et faire respecter nos accords et politiques juridiques. - Anonymisation : Lorsque nous n'avons plus de besoin commercial légitime de traiter vos Renseignements personnels, nous les supprimerons ou les anonymiserons. Si cela n'est pas possible (par exemple, parce que vos Renseignements personnels ont été stockés dans des archives de sauvegarde), nous stockerons vos Renseignements personnels de manière sécurisée et les isolerons de tout traitement ultérieur jusqu'à ce que la suppression soit possible. ## 8. Vos droits TOUTES LES DEMANDES DES UTILISATEURS AUTORISÉS DOIVENT ÊTRE ADRESSÉES AU RESPONSABLE DE LA PROTECTION DES RENSEIGNEMENTS PERSONNELS DE LEUR ORGANISATION. Pour les utilisateurs des Applications qui ne sont pas des Utilisateurs autorisés d'une Organisation (ou lorsque la loi l'exige), vous disposez des droits suivants : - Droit à l'information : Vous avez le droit d'être informé des types d'opérations effectuées sur vos Renseignements personnels. - Droit d'accès : Vous pouvez accéder à vos Renseignements personnels en vous connectant à la Plateforme ou en envoyant un courriel à l'adresse fournie à la fin de cette Politique. - Droit d'opposition/retrait du consentement : Dans certains cas, vous pouvez vous opposer au traitement ou retirer votre consentement en donnant un préavis raisonnable par courriel. Notez que le retrait du consentement peut affecter votre capacité à utiliser les Applications. - Droit de rectification : Vous pouvez demander la correction de Renseignements personnels inexacts ou incomplets. - Droit à l'effacement : Vous pouvez demander l'effacement de vos Renseignements personnels, sous réserve de nos obligations légales. - Droit à la portabilité des données : Vous pouvez obtenir vos Renseignements personnels dans un format numérique couramment utilisé. Nous répondrons à toute demande dans les 30 jours suivant sa réception, sauf si la loi permet une prolongation. ## 9. Témoins de connexion ### 9.1. Définition Un témoin est un petit texte envoyé par un serveur à votre navigateur, qui le renverra la prochaine fois qu'il se connectera à des serveurs partageant le même nom de domaine. Vous n'avez pas besoin d'accepter les témoins pour visiter nos Applications, mais les refuser pourrait limiter certaines fonctionnalités. ### 9.2. Types de témoins utilisés par Vasco - Témoins techniques : Utilisés pour faciliter l'utilisation des Applications (par exemple, se souvenir de votre nom d'utilisateur ou de vos préférences). - Témoins analytiques : Témoins anonymes utilisés pour recueillir des statistiques sur l'utilisation des Applications. - Témoins publicitaires : Peuvent être ajoutés par les Applications ou d'autres sites pour établir votre profil de visiteur de manière anonyme. ## 10. Responsable de la protection des renseignements personnels Le Responsable de la protection des renseignements personnels chez Vasco est Sébastien Rothlisberger. Cette fonction correspond à celle du Délégué à la protection des données (DPD) en vertu du RGPD. Si vous avez des questions ou des demandes concernant cette Politique, vous pouvez envoyer un courriel à : **privacy@vasco.app**. ## 11. Modifications Vasco se réserve le droit de modifier le contenu de cette Politique à tout moment. Toute modification sera publiée sur notre Plateforme et portée à votre attention lors de votre connexion. Nous vous recommandons d'imprimer une copie de cette Politique pour vos dossiers et de consulter cette section de notre Plateforme périodiquement. --- # Privacy Policy ## 1. Introduction **BEFORE USING THE WEBSITE AND OUR APPLICATIONS, PLEASE READ OUR PRIVACY POLICY CAREFULLY (the "Policy").** This Policy addresses the protection of Personal Information by **Vasco HQ Inc.**, doing business as **Vasco** (hereinafter referred to as "Vasco" or "we"). We take special care to protect your Personal Information collected through our website **vasco.app** and our applications (hereinafter collectively referred to as "Applications") and through the Vasco Products (hereinafter collectively referred to as "Platform"). However, this Policy applies, in its entirety, only to Personal Information of the Applications users (hereinafter: "you"). Its purpose is to explain how we collect, use and disclose your Personal Information. When we process customer data on behalf of customers, that's governed by the [DPA](/legal/dpa), not this Policy. If you are an Authorized User of a Customer, any section of this Policy is applicable to you only where it specifically provides that it applies to such users. Each Customer is responsible for complying with the legal obligations applicable to persons who collect Personal Information about others and, in this respect, is responsible for obtaining valid consent for its collection, disclosure and use. In addition, the Customer shall be responsible for establishing its own privacy policy, determining the safeguards applicable to Personal Information and providing the persons concerned by such information with the means to exercise their rights. In addition, this Policy does not apply to Personal Information about our employees, and Personal Information about our Subprocessors (as those terms are defined in Section 6 of this Policy). Lastly, this Policy aims to comply with Canadian and Quebec laws relating to the protection of Personal Information and, where applicable, the General Data Protection Regulation ("GDPR"). For the purposes of this Policy, the following definitions shall apply: ### 1.1. "Account Administrator" Member of an Organization to which the Organization grants the right to administer the account, which right includes the following prerogatives: - amendment of the User Account information; - addition of Authorized Users and their User Profiles; - amendment of payment information; - performance of any operation related to the business relationship of the Organization with Vasco. ### 1.2. "Vasco Products" Means: 1. The Vasco solutions and software as a service for Revenue Operations (RevOps), including the Data Hub, Planning Hub, Review Hub, Execution Hub, BI Hub, and Gama AI (AI revenue analyst); 2. The Vasco solutions offered in whole or in part as mobile apps for smartphones or tablets; 3. Any other solution providing new functionalities which may be added in the form of a module to the solutions listed above; and 4. Support for Authorized Users and related maintenance provided by Vasco. ### 1.3. "Authorized User" Member of a Customer, Organization or Affiliate which an Account Administrator authorizes to use the Vasco Products after the Customer has paid all related charges. An Authorized User may be a member of several Customers, Organizations or Affiliates, as the case may be. ### 1.4. "Customer" An Organization designated on the registration form who has received an email confirming the order of Vasco Products. ### 1.5. "Organization" A person who carries on a business, limited partnership, limited liability company, partnership, union, employer organization, sole proprietorship, business corporation or company (with or without share capital), legal person, cooperative, trust, unincorporated association, joint venture, non-profit or not-for-profit organization, government authority or any other entity, regardless its legal form, incorporation status or the jurisdictions in which it operates, carrying on an organized activity of any nature whatsoever and which uses the Vasco Products. An Authorized User who manages the accounts of other Authorized Users is considered an Organization. ### 1.6. "Personal Information" Any information pertaining to a natural person which directly or indirectly allows the person to be identified. For the purposes of this Policy, Personal Information corresponds to "personal data" within the meaning of the GDPR. ### 1.7. "User Profile" All Personal Information concerning an Authorized User transcribed in an intelligible and structured manner which is accessible and modifiable via the Platform. ## 2. Personal Information We Collect We collect only the Personal Information about you that is necessary to establish, manage and maintain our relationship with you. This collection is limited, in most cases, to the following Personal Information: - Last name, First name; - Email address, phone numbers; - Banking/Billing information, if applicable; and - Cookies (see Section 9 of this Policy for more details). We may collect Personal Information through the Applications, when you sign a contract or, more broadly, when you interact with one of our employees or representatives by email, telephone or in person. Personal Information that is entered by Authorized Users in their User Profiles is the responsibility of their Organizations. ## 3. Your Consent Your consent to the collection, use or disclosure of your Personal Information must be freely given, unambiguous, and informed. It must be given for specific purposes. Our policies and contracts are written in plain language to make it easier for you to understand the nature, purposes and consequences of the collection, use and disclosure of your Personal Information. Depending on the nature and sensitivity of your Personal Information, your consent may be explicit (such consent may be given verbally, in writing or electronically) or implied (when you voluntarily provide Personal Information, for instance). Generally, we will seek your consent, except where otherwise required or permitted by law. If you are an Authorized User, our [Terms and Conditions](/legal/terms-of-service) require your Organization to obtain your consent, and we presume that it is acting within the limits set by law. In the event that you witness or experience a breach in this regard, you may notify us using the contact information provided at the end of the Policy. By using the Applications, you consent to the use of your Personal Information in accordance with this Policy. ## 4. Security and Governance Cybersecurity is a priority for us. We have adopted a comprehensive set of policies and practices to guide the governance of Personal Information. These policies define how we protect and manage information throughout its lifecycle and form part of our SOC 2-aligned Information Security Program. Our governance framework provides for: - The use, communication, retention, and destruction of Personal Information in accordance with documented policies; - Clearly defined roles and responsibilities of employees and contractors throughout the information lifecycle; and - A documented process for reporting and managing incidents or complaints concerning the protection of information. These policies and practices include: 1. **Data Classification and Protection Policies:** Define how information is categorized according to sensitivity and outline our obligations to protect and maintain records of operations performed on that information. 2. **Information Security Policy:** Establishes processes to safeguard the confidentiality, integrity, and availability of the information and systems we manage. 3. **Vendor and Subprocessor Security Review Process:** Outlines how we evaluate and approve subprocessors and third-party vendors, including the security requirements they must meet before handling Personal Information. In addition to these administrative measures, we have implemented physical and technological safeguards appropriate to the sensitivity, purpose, quantity, and medium of Personal Information processed. We take all reasonable steps to minimize the risk of a confidentiality breach. For instance, we apply the principles of maximum protection by default, ensuring that Platform settings have the highest level of privacy by default. [Security at Vasco](/legal/security) ## 5. Use of Personal Information We use your Personal Information primarily to provide, maintain, and improve our Platform and services. Specifically, we may use your information to: - **Provide Services:** Establish and manage your account, authenticate your identity, and provide the features of the Vasco Products you have subscribed to. - **Support:** Respond to your comments, questions, and requests, and provide customer service and technical support. - **Communication:** Send you technical notices, updates, security alerts, and administrative messages. - **Improvement:** Monitor and analyze trends, usage, and activities in connection with our Platform to improve the user experience and develop new functionalities. - **Billing:** Process payments and manage billing relationships. - **Legal Compliance:** Comply with legal and regulatory obligations, resolve disputes, and enforce our agreements. ## 6. Disclosure of Your Personal Information We may disclose Personal Information to third parties in specific circumstances permitted by law. ### To Service Providers, Agents, Subprocessors ("Subprocessors") - **Description and purpose:** We may enter into contracts with [Subprocessors](/legal/subprocessors) to provide a service to our Customers, such as a Platform feature (e.g., hosting, payment processing). These Subprocessors may also provide a service to you directly on our behalf. - **Steps:** The contract requires [Subprocessors](/legal/subprocessors) to: - Use only Personal Information that is necessary for providing the service. - Refrain from disclosing or communicating Personal Information without our consent. - Implement rigorous security measures and allow us to audit these measures. - Notify us immediately of a confidentiality incident. - Destroy Personal Information at the end of a contract. ### Another Party in a Business Transaction - **Description and purpose:** We may enter into a contract with a third party for the purpose of a Business Transaction (e.g., merger, acquisition, financing). - **Steps:** We require the other party to use Personal Information only for the purposes of entering into the transaction and to implement rigorous security measures. ### Legitimate Authorities - **Description and purpose:** In order to comply with a court order, search warrant, or regulatory decision, we may be required to provide Personal Information. - **Steps:** We decline to provide access where the request is not valid. We inform Customers of requests regarding their Authorized Users unless prohibited by law. ## 7. Retention We will retain your Personal Information only for as long as is necessary for the purposes set out in this Policy. - **Account Duration:** We retain your Personal Information as long as your account is active or as needed to provide you with the Vasco Products. - **Legal Obligations:** We will also retain and use your Personal Information to the extent necessary to comply with our legal obligations (for example, if we are required to retain your data to comply with applicable laws), resolve disputes, and enforce our legal agreements and policies. - **Anonymization:** When we no longer have a legitimate business need to process your Personal Information, we will either delete or anonymize it. If this is not possible (for example, because your Personal Information has been stored in backup archives), then we will securely store your Personal Information and isolate it from any further processing until deletion is possible. ## 8. Your Rights **ALL REQUESTS FROM AUTHORIZED USERS SHOULD BE DIRECTED TO THE PRIVACY OFFICER OF THEIR ORGANIZATION.** For Applications users who are not Authorized Users of an Organization (or where applicable by law), you have the following rights: 1. **Right to be informed:** You have the right to be informed of the types of operations carried out on your Personal Information. 2. **Right to access:** You may access your Personal Information by logging into the Platform or by sending an email to the address provided at the end of this Policy. 3. **Right to object/withdraw consent:** In some cases, you may object to or withdraw your consent by giving reasonable notice via email. Note that withdrawing consent may affect your ability to use the Applications. 4. **Right to correction:** You may request correction of inaccurate or incomplete Personal Information. 5. **Right to deletion:** You may request deletion of your Personal Information subject to our legal obligations. 6. **Right to portability:** You may obtain your Personal Information in a commonly used digital form. We will respond to any request within 30 days of receipt, except where the law permits an extension. ## 9. Cookies ### 9.1. Definition A cookie is a small text sent by a server to your browser, which it will send back the next time it connects to servers sharing the same domain name. You do not need to accept cookies to visit our Applications, but refusing them may limit some features. ### 9.2. Types of cookies used by Vasco - **Technical cookies:** Used to facilitate the use of the Applications (e.g., remembering your username or preferences). - **Analytical cookies:** Anonymous cookies used to collect statistics on the use of the Applications. - **Advertising cookies:** May be added by the Applications or other sites to build up your visitor profile anonymously. The specific third-party tools we use on our marketing website are: - **Analytics:** Google Analytics and Microsoft Clarity (usage statistics). - **Marketing:** the LinkedIn Insight Tag and the Meta (Facebook) Pixel (ad measurement and retargeting), and LeadJourney (visitor identification and lead attribution, served from `t.vasco.app`). ### 9.3. Managing your choices Strictly necessary cookies are always active. For visitors in the EU, UK, EEA, and Switzerland, analytics and marketing cookies are **set only after you opt in** via the consent banner shown on your first visit. You can review or change your choice at any time using the **"Cookie preferences"** link in the website footer. ## 10. Privacy Officer The Privacy Officer at Vasco is Sebastien Rothlisberger. This function corresponds to that of the Data Protection Officer (DPO) under the GDPR. If you have any questions or requests regarding the Policy, you can send an email to the following address: **privacy@vasco.app**. ## 11. Changes Vasco reserves the right to change the content of this Policy at any time. Any changes will be posted on our Platform and brought to your attention when you log in. We recommend that you print a copy of this Policy for your records and review this section of our Platform periodically. --- Unless you wish to refer specifically to the English version of this policy, please consult the French version available at [Politique de confidentialite](/legal/politique-de-confidentialite). --- # Security At Vasco, we understand the importance of trust, especially when it involves the security of your data. Our commitment to safeguarding your information is at the core of our operations, ensuring that you can scale your business with confidence and predictability. ## Data Security at Vasco Our approach to security is designed with the dual goals of protecting your data and fostering innovation within your teams. We believe in transparency and are dedicated to sharing our practices that keep your data secure, as well as our continuous efforts to enhance data security. For detailed information on our privacy practices, please visit our [Privacy Policy](/legal/privacy). ## Reporting Security Vulnerabilities We consider the security of our systems a top priority. However, no system can be entirely free of security vulnerabilities. If you discover a vulnerability in any of our services, please help us by reporting it to us through [security@vasco.app](mailto:security@vasco.app). For general inquiries or further information, feel free to reach out at [security@vasco.app](mailto:security@vasco.app). ## Our Security Practices **Data Storage and Encryption** - We minimize data storage to only what is necessary for account access, integration with third-party tools, and workflow debugging. - All data transmitted to Vasco is encrypted in transit. We enforce TLS/SSL protocols for all our workflow and application endpoints. **Monitoring and Response** - Our infrastructure and the Vasco platform are monitored continuously, with audit trails to support security analysis and to audit access across our stack. - Vasco employs advanced monitoring and alerting systems for security incidents and system health. Our engineering team is on call 24/7 to respond promptly to any issues. **Access and Authentication** - We implement strong password policies and regular permission audits for all third-party software accounts to mitigate unauthorized access risks. Shared logins are discouraged; however, when necessary, we utilize secure tools like 1Password for teams to manage access. **Deployment and Automation** - Our deployment processes are automated, allowing us to roll out updates or security patches quickly, often multiple times a week. **Incident Management** - Vasco has a structured incident response plan to address any security issues effectively, minimizing potential impacts on our customers and their data. --- # Subprocessors ## What are subprocessors? Subprocessors are third-party software, associations, or actors that we rely upon and that may have access to or process data. These subprocessors help us improve or outright enable us to deliver our products effectively. We ensure all subprocessors maintain strict security and data privacy standards. ## Subprocessor List Our current subprocessor list is maintained in our Trust Center. --- # Terms & Policies ## IMPORTANT NOTICE The access and use of our revenue operation and management platform (the "Platform") and the provision of related platform and services by Vasco HQ Inc., its Affiliates or subcontractors (collectively, "Vasco") is subject to the terms and conditions set out below (the "Agreement"). Therefore, please read before accessing or using the Platform or our Services. This Agreement, made and entered into as of the time and date of click through or tapping action (the "Effective Date"), is a legal agreement between you ("Client" or "You" and together with Vasco the "Parties", and each a "Party") and Vasco and governs the access and use of the Platform and the provision of related Services by Vasco. By clicking or tapping the "Accept" button or similar affirmation as applicable when accessing or using the Platform, or by signing a copy of this Agreement, a Quote or an order form, Client agrees to be bound by the terms of this Agreement and that this Agreement governs Client's use of the Platform and the provision of related Services by Vasco. If you do not agree to the terms of this Agreement, do not access or use the Platform or the related Services. If you are entering into this Agreement on behalf of a company or other legal entity, you represent that you have the legal authority to bind the entity to this Agreement, in which case "You" and "Client" will mean the entity you represent. If you don't have such authority, or if you don't accept all the terms and conditions of this Agreement, then Vasco does not agree to your access and use of the Platform and the provision of the Services, and you may not use the Platform and benefit from the related Services. Vasco reserves the right, at any time and without prior notice, to modify or replace any of this Agreement. Any changes to this Agreement can be found at [vasco.app/legal/terms-of-service](/legal/terms-of-service). It is your responsibility to check the Agreement periodically for changes. Your use of the Platform Services following the posting of any changes to the Agreement constitutes acceptance of those changes. If there are any significant changes to the Agreement that materially affect our relationship with you, we will use commercially reasonable efforts to notify you by sending a notice to the primary email address specified in your account, by posting a prominent notice when you log in to your account for the first time following those changes, or by posting a prominent notice on the Platform. --- ## 1. INTERPRETATION ### 1.1 Definitions In addition to the other terms defined in the Agreement, for the purposes of this Agreement: (a) **"Affiliate"** means, with respect to any entity, any other entity directly or indirectly controlling or controlled by, or under direct or indirect common control with, such entity. For the purposes of this definition, an entity shall control another entity if the first entity: (i) owns, beneficially or of record, more than 50% of the voting securities of the other entity; or (ii) has the ability to elect a majority of the directors of the other entity. (b) **"Confidential Information"** means (i) any and all non-public, confidential or proprietary information of a Party, including any information relating to the existence or content of the Agreement, the Services, the Documentation, Client Data and a Party's business, products, services, activities, operations, business affairs, clients and prospects, Intellectual Property (including Vasco Intellectual Property), technology, know-how, design rights and trade secrets, whether such information is provided orally, in writing, in computer readable form or otherwise and whether or not it is specifically identified as confidential; and (ii) any copies, extracts or reproduction, in whole or in part, of any of the foregoing. (c) **"Client Data"** means any and all electronic data or other information that is (i) uploaded or inputted by Client to the Platform; (ii) stored by Client via the Platform; or (iii) provided by Client to Vasco, regardless of the format, to be used in connection with the Services. (d) **"Documentation"** means all documents, user manuals or other information, available in writing, online or otherwise, relating to the Services provided by Vasco. (e) **"Force Majeure"** means any circumstances beyond a Party's reasonable control, including natural disasters, acts of government, floods, fires, earthquakes, pandemics, epidemics, government-mandated quarantines, publicly declared states of emergency, civil unrest, terrorism, strikes or other labour problems (other than those involving such Party's employees), Internet service provider failures or delays, or denial of service attacks. (f) **"Intellectual Property"** means any and all ideas, concepts, inventions, methods, processes, know-how, trade secret, works, software, computer programs and other computer software (including all source and object codes, algorithms, architectures, structures, user interfaces including display screens, lay-out and development tools), databases, designs, plans, drawings, brochures, website content, sales and advertising literature and other marketing materials, and any improvements thereon or applications or derivative works thereof, and all other forms of intellectual property, all whether or not registered or capable of such registration. (g) **"Intellectual Property Rights"** means any and all patents, copyrights, trademarks, trade names and other proprietary rights, and all registrations or applications in relation to the foregoing. (h) **"Malicious Code"** means viruses, worms, time bombs, Trojan horses and other harmful or malicious code, files, scripts, agents or programs. (i) **"Patches"** means updates, upgrades, patches, bug fixes and other interim modifications applicable to the Platform Services. (j) **"Platform Data"** means (A) any and all data provided by Vasco through the Platform; and (B) any and all data (other than Client Data) generated by the Platform, including (i) all metadata and (ii) any Client Data that has been transformed by the Platform or converted by the Platform into anonymized and non-Client identifiable data. Client acknowledges that Platform Data is used, among other things, as inputs for the proprietary algorithms of the Platform. (k) **"Process"** (including any grammatically inflected forms thereof) means any operation or set of operations which is performed on data or on sets of data, whether or not by automated means, including, without limitation, collection, recording, organization, structuring, storage, adaptation or alteration, access, retrieval, consultation, use, disclosure by transmission, dissemination or otherwise making available, alignment or combination, restriction, erasure or destruction. (l) **"Quote"** means each quote or similar document between the Parties incorporating the terms of the Agreement which, among other things, sets forth the Services ordered, the term of the Services and the corresponding Fees. (m) **"Third Party Material"** means all or part of the Intellectual Property and Intellectual Property Rights, wholly or partially owned or controlled by a third party. (n) **"Trademarks"** means trademarks, tradenames, brands, trade dress, business names, domain names, designs, graphics, logos and other commercial symbols and indicia of origin, whether registered or not, and any goodwill associated therewith. (o) **"User"** means an employee of Client or its Affiliates, an independent contractor, consultant or agent of Client or its Affiliates: (i) who is authorized by Client to access and use the Services; (ii) for whom subscriptions to the Services have been ordered; and (iii) who has been supplied user identification codes and passwords by Client (or by Vasco at Client's request). (p) **"Vasco Intellectual Property"** means all Intellectual Property developed by or first conceived or reduced to practice by Vasco, its Affiliates, its licensors or by any third party on Vasco's behalf relating to the Platform, Platform Data, the Services, the Documentation, all related products or services and any other Vasco's products or services. ### 1.2 Expressions Where the word "including" or the word "includes" is used in the Agreement, it means "including (or includes) without limitation". ### 1.3 Order of Precedence In the event of any inconsistencies between the provisions set forth in the Agreement and in any Quote(s), the provisions of such Quote(s) shall prevail. ### 1.4 Standard Forms Nothing in Client terms and conditions, order forms, or any documents transmitted by Client in connection with the Agreement shall be construed to modify, amend or supplement the terms of the Agreement. ### 1.5 Language The Parties have requested that this Agreement and all documents related thereto be drafted in English. Les Parties ont exigé que le Contrat, ainsi que tous les documents y afférents, soient rédigés en anglais. --- ## 2. SERVICES ### 2.1 Scope of Services Subject to the terms and conditions of this Agreement and the applicable Quote(s) and payment in full of the applicable Fees, Vasco shall (i) make its Platform available to Client and its Affiliates (the "Platform Services"); and/or (ii) provide professional services as further set out in Section 3 (the "Professional Services" and collectively with the Platform Services, the "Services"). ### 2.2 Specifications Access and use of the Services by Client and its Affiliates and the Users may be subject to specifications and/or restrictions set forth in the applicable Quote. Client acknowledges and agrees that, subject to the terms of the applicable Quote, the Fees may be based on estimates, which shall be adjusted (plus or minus) from time to time based on Client's actual usage, in accordance with the metrics set out in the applicable Quote. --- ## 3. PROFESSIONAL SERVICES ### 3.1 Scope Vasco may provide professional services to Client, which may consist, among other things, of implementation and testing of the Platform, training on the Platform and providing related services, all as set out in the applicable separate Quote to be agreed upon between the Parties (collectively, the "Professional Services"). ### 3.2 Assumptions Client acknowledges and agrees that: (i) there are inherent uncertainties associated with the type of Professional Services provided by Vasco and Client's system environment; and (ii) Vasco's performance of the Professional Services is dependent on: (A) the assumptions, if any, made by Vasco in the applicable Quote; and (B) Client's timely and effective satisfaction of all Client obligations. Client also acknowledges and agrees that Vasco shall not be responsible for any delay in the performance of the Professional Services due to a Force Majeure event. ### 3.3 Changes During the term of a Quote, Client may request in writing that changes be made to the Professional Services (each a "Change Request"). If the Change Request is accepted in writing by Vasco, such Change Request shall be deemed to have amended the applicable Quote and form an integral part thereof. All additional costs arising out of a Change Request shall be assumed by Client. ### 3.4 Ownership of Deliverables Unless otherwise agreed in writing between the Parties in a Quote, Vasco shall own all rights, title and interest in and to the deliverables and work products (including software code, specifications, reports, notes, interfaces and related Documentation) conceived, developed, acquired or reduced to practice by Vasco in connection with this Agreement (the "Deliverables"), and all related Intellectual Property Rights. ### 3.5 Approval of Deliverables The approval procedure(s) (if any) for the Deliverables shall be set out in the applicable Quote(s). ### 3.6 Resource Cancellation Policy Scheduled dates and time for the provision of Professional Services may be cancelled or rescheduled by Client without incurring any cancellation charges provided that Vasco is given at least 7 days' prior notice, failing which Client: (i) may be subject to a cancellation fee up to a maximum of 50% of the agreed upon Fee estimate; and (ii) shall reimburse Vasco for the cost of any expenses or change fees that are incurred by Vasco in connection with such cancellation or rescheduling. Vasco shall use commercially reasonable efforts to minimize all costs and expenses associated with any Client cancellation. --- ## 4. PLATFORM SERVICES ### 4.1 Scope Subject to (i) the terms and conditions of this Agreement and the applicable Quote(s), and (ii) payment in full of the applicable Fees in accordance with the payment schedule described in the applicable Quote, Vasco shall make the Platform available to Client, its Affiliates and its and their Users. Vasco shall use commercially reasonable efforts to make the Platform available 24 hours a day, 7 days a week. ### 4.2 Amendments; Patches Client acknowledges and accepts that Vasco, from time to time and at its sole discretion, may amend or update the Platform Services. Client is required to accept all Patches necessary for the proper function and security of the Platform, as such Patches may be released by Vasco from time to time. Except for emergency or security-related maintenance activities, Vasco will use commercially reasonable efforts to coordinate with Client the scheduling of application of Patches, based on Vasco's next standard maintenance period. ### 4.3 Service Suspension Vasco may suspend Client's account, access to or use of the Platform if (i) Client or a User breaches any provision of the Agreement, and such breach is not remedied within 15 days of Client receiving written notice from Vasco; (ii) Vasco detects fraud, a security breach or any other similar threat that causes or that could cause, in Vasco's reasonable opinion, damage to the Platform, Vasco's IT infrastructure or Client Data; or (iii) Vasco is undertaking scheduled maintenance. Vasco will use commercially reasonable efforts to restore the access to or use of the Platform, as soon as possible after the suspension. Any suspension by Vasco of the Platform Services in application of this Section 4.3 shall not release Client from its payment obligations under the Agreement. --- ## 5. CLIENT OBLIGATIONS AND RESPONSIBILITIES ### 5.1 Use Client shall (i) be responsible for the means by which Client acquired Client Data, the accuracy, quality, legality and use of Client Data (which includes Client having obtained all license and other rights for the use Client Data by Vasco under this Agreement); (ii) implement commercially reasonable efforts to prevent unauthorized access to or use of the Platform, and notify Vasco promptly of any such unauthorized access or use; (iii) use the Services only in accordance with the Agreement and the Documentation and applicable laws and government regulations; and (iv) be responsible for the purchase and maintenance in good working order of all the equipment (including network equipment and systems), software and Internet connection necessary to access and use the Services. ### 5.2 Restrictions Except as provided herein, Client may not: (i) loan, rent, lease, transfer, convey, assign, sell, distribute the Services or grant sublicenses for the Services or any part thereof; (ii) modify, combine or distribute the Platform Services (or any part thereof) with any other software or code in a manner which would subject the Platform Services to open source license terms; (iii) use the Platform Services to store or transmit infringing, libellous, or otherwise unlawful or tortious material, or to store or transmit material (including Client Data) in violation of third party privacy rights or intellectual property rights; (iv) use the Platform Services to store or transmit Malicious Code; (v) interfere with or disrupt the integrity or performance of the Services; (vi) copy, frame or mirror any part or content of the Platform Services, other than copying or framing on Client's own intranets or otherwise for Client's own internal operational purposes; (vii) reverse engineer, decompile or disassemble the Platform Services or attempt to gain unauthorized access to the Platform Services or Vasco's systems or networks; or (ix) use or access the Services in order to build a competitive product or service, or copy any features, functions or graphics of the Services. ### 5.3 Assistance and Information Client shall provide Vasco with all necessary Client Data, information and assistance for the performance of Vasco's obligations hereunder or otherwise that is reasonably requested by Vasco. Without limiting the generality of the foregoing, Client shall (i) perform the tasks and assume the responsibilities and requirements of Client specified in the applicable Quote (collectively, the "Client Responsibilities"); and (ii) ensure prompt and efficient cooperation of all its personnel who must assist Vasco for the performance of the Services. ### 5.4 Passwords Client is solely responsible (i) for preserving the confidentiality of the Users' user identifications and passwords; and (ii) for restricting or protecting access to Client equipment (hardware and software) required to access and use the Platform Services. ### 5.5 Users Client is responsible for all use and misuse of the Services by its Users, or their breach of the terms of the Agreement, and shall indemnify Vasco for any damages, costs and expenses suffered as a result of such use, misuse or breach. --- ## 6. INFORMATION SECURITY ### 6.1 Protection of Client Data Vasco maintains appropriate administrative, physical, and technical safeguards designed to protect the security, confidentiality and integrity of Client Data. Vasco shall not access Client Data except (i) in connection with the performance of its obligations hereunder; (ii) to prevent or address service or technical problems; or (iii) at Client's request in connection with Client support matters. In no event will Vasco share Client Data with any third parties other than its subcontractors who need to have access to Client Data for the performance of Vasco's obligations hereunder, or as required by applicable law or a governmental authority. ### 6.2 Client Data Retention and Deletion Vasco will retain any Client Data in its possession until deleted in accordance with this Agreement. Except as otherwise required by applicable law, Vasco will delete all Client Data in its possession (i) as necessary from time to time if such Client Data is no longer required by Vasco to perform its Services under any applicable Quote; and (ii) promptly after receiving Client's written request in respect thereof. Upon request from Client at any time, Vasco shall transfer a copy of all Client Data to Client and shall provide reasonable assistance to Client to download and store such copy of Client Data. Notwithstanding the foregoing, Client may at any time instruct Vasco to retain and not to delete Client Data, provided Client may not require retention of Client Data for more than 90 business days after termination of this Agreement. ### 6.3 Privacy Laws Compliance Client represents and warrants that it will comply with all applicable privacy laws and regulations, including with respect to the collection, use, Processing, disclosure and handling of personal information that is part of Client Data or that is otherwise provided to Vasco for the purposes of this Agreement. Without limitation of the foregoing, Client represents, warrants, and covenants that: (i) it has (and will have) provided any notice and obtained all consents and rights required by applicable law to enable Vasco to lawfully Process Client Data as permitted by this Agreement; (ii) it has full right and authority to make Client Data available to Vasco under this Agreement; and (iii) Vasco's Processing of Client Data in accordance with this Agreement will not infringe upon or violate any applicable laws or any rights of any third party. Vasco represents and warrants that it will comply with all applicable privacy laws and regulations with respect to the handling of Client Data, including, without limitation, the Personal Information Protection and Electronic Documents Act (PIPEDA), and the Freedom of Information and Privacy Protection Act (FIPPA). ### 6.4 Data Processing Agreement To the extent Vasco processes Personal Data (as defined under Applicable Data Protection Laws) on behalf of Client in connection with the Services, Vasco acts as a data processor and Client acts as a data controller. The Data Processing Agreement available at [https://vasco.app/legal/dpa](/legal/dpa) (the "DPA") is hereby incorporated into and forms part of this Agreement. In the event of a conflict between the DPA and this Agreement, the DPA shall prevail with respect to data protection and privacy matters. --- ## 7. INTELLECTUAL PROPERTY ### 7.1 Vasco Property Vasco (or its licensors) retains any and all rights, title and interest (including all Intellectual Property Rights) in and to: (i) the Platform, including any Patches, enhancements or other modifications to the Platform and any related Deliverables (subject to the terms of Section 3.4); (ii) Platform Data; (iii) the Vasco Intellectual Property; (iv) the Documentation relating to any of the foregoing; (v) all enhancements, upgrades or other modifications to any of the foregoing; (vi) Vasco Trademarks; and (vii) all Intellectual Property Rights related to any of the foregoing. Client will acquire no rights or licenses to any Vasco property unless otherwise expressly provided in the Agreement. Client shall not remove any Intellectual Property Rights notice that appears on the Documentation or redisplayed through or embodied in the Services. ### 7.2 Client Property and Licenses Client owns all right, title, and interest in and to Client Data and Client Trademarks, including all Intellectual Property Rights related thereto. Client grants Vasco a royalty-free, worldwide and non-exclusive license to host, use, copy, reproduce, display, save, Process and transmit Client Data and Client Trademarks for the purposes of, and only to the extent necessary for, the provision of the Services and the operation of the Platform. ### 7.3 Feedback Vasco shall own all right, title and interest in and to any suggestions, requests or recommendations for improvements or enhancement to the Services that Client (including any of the Users) may, alone or jointly with Vasco, propose or make during the term of the Agreement (collectively, "Feedback"). Client hereby irrevocably (i) assigns all rights, titles and interests in and to the Feedback to Vasco; and (ii) waives in favour of Vasco, its successors and assigns any and all moral rights that Client has or may have in the Feedback in each jurisdiction throughout the world, to the fullest extent that such rights may be waived in each respective jurisdiction. --- ## 8. FEES AND TAXES ### 8.1 Fees In consideration of the Services, Client shall pay the fees set out in the applicable Quote(s) (the "Fees"). Payment terms are set out in the applicable Quote(s). The Fees are not reimbursable. ### 8.2 Price Increase Except if otherwise indicated on a Quote, Vasco reserves the right to increase the Fees from time to time and at its entire discretion, subject to providing a reasonable written notice to Client. ### 8.3 Suspension of Service If any amount owing by Client under the Agreement is overdue by more than 30 days as of the date of the applicable invoice, Vasco may, without limiting Vasco's other rights and remedies, suspend the Services to Client until such amounts are paid in full. ### 8.4 Interest In addition to any other rights or remedies of Vasco, any uncontested amount not paid by Client when due shall bear interest at the rate that is the lesser of 1.5% per month or the maximum rate allowable by law. ### 8.5 Taxes Unless expressly stated in a Quote, the fees and costs set forth in this Agreement do not include any applicable sales, use, value added, property, excise or any other taxes or duties of any nature whatsoever. Vasco will invoice Client for any applicable taxes in relation to any Quote. --- ## 9. TERM AND TERMINATION ### 9.1 Term The Agreement will commence on the Effective Date and will remain until terminated in accordance with the terms and conditions set forth herein. ### 9.2 Term – Professional Services The duration of the Professional Services is set out in each applicable Quote. ### 9.3 Termination Each Party may terminate the Agreement, or a Quote, at any time: (i) subject to a 30 day written notice to the other party, (ii) if the other Party is in breach of any of its material obligations hereunder or under the relevant Quote, as applicable, and such breach is not cured within 15 days after a written notice delivered to the Party in default; or (iii) by written notice to the other Party, if such other Party takes or is bound by any person to take one of the following measures: (a) an assignment, an arrangement or a comparable act in favour of its creditors; (b) seizure or receivership of its assets; (c) the filing of a petition in view of bankruptcy, insolvency or relief of debtors or the instituting of any proceedings related to bankruptcy, insolvency or the relief of debtors; (d) the execution or threat of execution of any act of bankruptcy; or (e) the liquidation, winding-up or dissolution of the enterprise in application of a court order rendered by a competent court. If Client terminates this Agreement or a Quote other than as set out in this Section 9.3, all Fees owed to Vasco until the end of the then current Term or the end of the Professional Services under a Quote, as applicable, shall remain due by Client. ### 9.4 Recourse The termination of the Agreement or any Quote, for any reason whatsoever, will in no way affect either Party's rights and recourses against the other Party, at law or in equity, for damages for failure to discharge an obligation under the Agreement or the Quote, as the case may be. ### 9.5 Effect of Termination At the expiry or termination of the Agreement or a Quote (i) Client will cease to have access to the Platform (subject to Section 9.7); (ii) all Quotes shall terminate immediately; (iii) Vasco shall be entitled to the payment of any Fees accrued as of the date of termination; and (iv) Recipient hall return immediately to Discloser (such as these terms are defined in Section 12.2) all Confidential Information and all copies thereof in any form whatsoever under the possession or control of Recipient that relate to the Agreement, or destroy said Confidential Information and its copies, as directed by Discloser. ### 9.6 Transition Services Before termination or expiration of this Agreement and notwithstanding anything to the contrary herein, Vasco shall cooperate with Client to ensure an orderly ramp-down of Client's use of the Platform and termination of this Agreement, subject to the Parties agreeing on the transition plan (including scope, related fees and ongoing access and usage of the Platform by Client during the transition period). ### 9.7 Surviving Provisions Sections, 7, 9.4 to 9.8 and 11 to 14 of this Agreement shall survive the termination of expiry of the Agreement. --- ## 10. REPRESENTATIONS AND WARRANTIES ### 10.1 Representations and Warranties of Client Client represents and warrants to Vasco that: (i) Client is a corporation, duly incorporated, organized and in good standing under the laws of its jurisdiction of formation; (ii) Client has the full right, power and authority to enter into the Agreement; (iii) Client has (or shall obtain) all necessary rights and consents to Client Data and Client Trademarks to grant Vasco the licenses granted hereunder (including to enable Vasco to lawfully Process Client Data as permitted by this Agreement); and (iv) to its knowledge, any licenses granted hereunder to Vasco do not breach or violate any third party Intellectual Property Rights. ### 10.2 Representations and Warranties of Vasco Vasco represents and warrants to Client that: (i) Vasco is a corporation, duly incorporated, organized and in good standing under the laws of its jurisdiction of formation; (ii) Vasco has the full right, power and authority to enter into the Agreement; (iii) Vasco has all the rights necessary to provide the Services; (iv) Vasco, its employees and subcontractors have the necessary knowledge, experience, and skills to perform the Services; (v) Vasco has and/or will acquire and maintain all licenses and permits required of them in order to perform the Services; and (vi) the Services will be performed in a competent and professional manner. ### 10.3 Warranty Disclaimer **VASCO DISCLAIMS ANY WARRANTY, EXPRESS OR IMPLIED, THAT THE SERVICES OR CLIENT DATA WILL REMAIN VIRUS-FREE. SUPPORT OR OTHER SERVICES IN APPLICATION OF THE AGREEMENT NECESSITATED BY COMPUTER VIRUSES, OR BY ANY FAILURE OR BREACH OF CLIENT'S SECURITY FOR ITS SYSTEMS OR DATA, INCLUDING DAMAGE CAUSED BY PERSONS LACKING AUTHORIZED ACCESS, ARE NOT COVERED UNDER THIS AGREEMENT. EXCEPT TO THE EXTENT SET FORTH IN SECTION 10.2, VASCO EXPRESSLY DECLINES, ON ITS OWN BEHALF AND ON BEHALF OF ITS SHAREHOLDERS DIRECTORS, OFFICERS, EMPLOYEES, SUBCONTRACTORS, AGENTS, VENDORS AND LICENSORS AND AGENTS ANY AND ALL EXPRESS, LEGAL OR IMPLICIT REPRESENTATIONS, WARRANTIES AND CONDITIONS NOT CONTAINED HEREIN, INCLUDING REPRESENTATIONS, WARRANTIES AND CONDITIONS OF COMMERCIALITY, PERFORMANCE, NON-INFRINGEMENT, FITNESS FOR A PARTICULAR PURPOSE AND ACCURACY. IN PARTICULAR, VASCO EXPRESSLY DECLINES THE FOLLOWING AND MAKES NO REPRESENTATION OR WARRANTY IN THESE REGARDS: (I) THE FACT THAT THE SERVICES WILL MEET CLIENT'S OPERATIONAL REQUIREMENTS; (II) THE FACT THAT THE OPERATION OF THE SERVICES AND THE DELIVERABLES WILL BE ERROR-FREE OR UNINTERRUPTED OR, THAT THE RESULTS OBTAINED FROM THEIR USE WILL BE ACCURATE OR RELIABLE; AND (III) THE FACT THAT ALL PROGRAMMING OR PLATFORM SERVICE ERRORS CAN BE CORRECTED OR FOUND IN ORDER TO BE CORRECTED. CLIENT ACKNOWLEDGES THAT THE FUNCTIONALITY AND INTERFACES OF THE PLATFORM MAY CHANGE OVER TIME.** --- ## 11. LIMITATION OF LIABILITY ### 11.1 Exclusion of Certain Damages In no event shall Vasco be liable for any claims, damages, losses, liabilities, costs, and expenses (including reasonable attorney's fees) arising directly or indirectly from (i) the modification or alteration in any manner by Client of any part of the Platform Services; (ii) any Client Data and any other materials provided by Client hereunder; or (iii) the breach by Client to comply with its obligations under Section 4. ### 11.2 Exclusion of Indirect Damages **TO THE MAXIMUM EXTENT PERMITTED BY LAW, IN NO EVENT MAY EITHER PARTY, ITS AFFILIATES, AND THEIR RESPECTIVE SHAREHOLDERS, OFFICERS, DIRECTORS, EMPLOYEES, SUBCONTRACTORS, AGENTS, VENDORS AND LICENSORS BE LIABLE FOR (I) ANY INDIRECT, INCIDENTAL, EXTRAORDINARY, CONSEQUENTIAL, SPECIAL, PUNITIVE OR EXEMPLARY DAMAGES OR (II) ANY LOSS OF REVENUE OR PROFITS, LOST OR DAMAGED DATA, LOSS OF USE, BUSINESS INTERRUPTION OR ANY OTHER FINANCIAL LOSS, ARISING DIRECTLY OR INDIRECTLY FROM THE AGREEMENT (INCLUDING ALL OF ITS RELATED QUOTES), OR CAUSED BY THE SERVICES, OR THE MISUSE OR INABILITY TO USE THE SERVICES, THE DELIVERABLES OR THE DOCUMENTATION, EVEN IF SUCH PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. THIS LIMITATION OF LIABILITY WILL APPLY REGARDLESS OF THE FORM OF ACTION: WHETHER IN CONTRACTUAL LIABILITY, APPLICATION OF THE WARRANTY, TORT, NEGLIGENCE, PRODUCT LIABILITY OF MANUFACTURERS AND VENDORS, STRICT CIVIL LIABILITY OR UNDER ANY OTHER LEGAL THEORY.** ### 11.3 Amount Limitation **THE OVERALL LIABILITY OF VASCO IN RESPECT OF CLAIMS OF CLIENT OR OF ANY OTHER PERSON ARISING UNDER THIS AGREEMENT SHALL BE LIMITED TO THE FEES PAID BY CLIENT UNDER THIS AGREEMENT DURING THE 12-MONTH PERIOD PRECEDING THE EVENT FROM WHICH THE LIABILITY ARISES.** ### 11.4 Exceptions The limitations of liability set forth in Section 11.3 above shall not apply with respect to damages attributable to a Party's gross negligence or wilful misconduct. --- ## 12. CONFIDENTIALITY ### 12.1 Prior Non-Disclosure Agreement If the Parties have entered into a non-disclosure agreement prior to the Effective Date, such agreement is hereby terminated and replaced in its entirety by the terms of this Section 12. ### 12.2 Obligation of Confidentiality The Party ("Recipient") receiving from the other Party ("Discloser") any Confidential Information, or otherwise obtaining any Confidential Information, shall keep confidential Discloser's Confidential Information and shall protect such information with the same degree of care as Recipient employs in the protection of its own confidential and proprietary information (but at least with a reasonable degree of care). ### 12.3 Use of Confidential Information Recipient may not use Discloser's Confidential Information in any manner except as reasonably required for the purpose of the Agreement or as permitted herein. ### 12.4 Disclosure of Confidential Information Recipient shall not, without Discloser's prior written consent, disclose Discloser's Confidential Information to any third party, except to those of its employees, consultants, and subcontractors who have a need-to-know Confidential Information for the purpose of this Agreement and who are bound by confidentiality provisions at least as stringent as those set out herein. Recipient remains liable for any breach of the terms of this Section 12 by its employees, consultants, or subcontractors (including, in the case of Client, the Users). ### 12.5 Exclusions The restrictions imposed by this Section 12 shall not apply to Confidential Information that Recipient can demonstrate (i) is now, or which hereafter, through no act or failure to act on the part of Recipient, becomes generally known or available to the public without breach of this Agreement; (ii) is known to Recipient at the time of disclosure of such Confidential Information provided that Recipient can satisfactorily demonstrate such prior knowledge by appropriate written records antedating the disclosure and that such knowledge was not gained from third parties through breach of secrecy; (iii) is hereafter furnished to Recipient in good faith by a third party without breach by such third party, either directly or indirectly, of an obligation of secrecy to Discloser; or (iv) is approved for such use or disclosure by written authorization of Discloser. ### 12.6 Legal Disclosure In the event Recipient becomes legally compelled to disclose any portion of Discloser's Confidential Information, Recipient immediately shall give notice thereof to Discloser and shall collaborate with Discloser reasonably and in good faith to prevent or limit the disclosure or obtain a protective order or other recourse. ### 12.7 Injunctive Relief Each Party acknowledges and agrees that the remedies at law for the breach of any of the provisions of this Section 12 may be insufficient, that such breach will cause irreparable harm within a short period of time, and that the other Party shall be entitled to preliminary injunctive relief or other injunctive relief against any such violation without the necessity of proving actual damages. Such injunctive relief shall be in addition to, and in no way in limitation of, any and all other remedies the other Party shall have at law and in equity for the enforcement of those undertakings and provisions. --- ## 13. NON-SOLICITATION Client agrees that, during the term of the Agreement and for a period of 12 months after the expiry or termination of the Agreement, it shall not, directly or indirectly, hire any employee, subcontractor, or independent contractor of Vasco or solicit, induce or attempt to induce any person who is an employee, subcontractor, or independent contractor of Vasco or was an employee, subcontractor or independent contractor during the 12-month period immediately preceding such solicitation, to terminate his or her employment with Vasco. General advertising performed by Client and not specifically directed at employees of Vasco shall not be deemed a violation of this Section 13. In the event Client breaches this Section 13, Client shall be liable to Vasco for an amount equal to 100% of the annual base compensation of the individual concerned in his/her new position, for a period of one (1) year. Although this payment is the exclusive remedy of the aggrieved Party to recover an amount from the other Party in case of breach of this Section 13, such breach shall be considered a material violation of the Agreement, and Vasco shall have the right to terminate this Agreement. --- ## 14. GENERAL ### 14.1 Publicity Neither Party shall publicize or disclose to any third party the existence or provisions of the Agreement or any of the fees, terms or conditions hereof, without the prior written consent of the other Party. Notwithstanding the foregoing, Client acknowledges and agrees that Vasco may mention, in its corporate brochures, marketing material, press releases and website, that Client is a customer of Vasco and uses the Services. In that regard, Client agrees that Vasco may use Client's corporate names and logos, subject to applicable Client trademark and logo policies. ### 14.2 Subcontracting Vasco may subcontract any of or all of its obligations under the Agreement to any third party, provided Vasco shall remain responsible for any breach of this Agreement and any Quote by its subcontractors. ### 14.3 Applicable Laws and Competent Courts The Agreement will be governed by, interpreted and construed in accordance with the laws of the Province of Quebec, Canada and the laws of Canada applicable therein, other than rules governing conflicts of laws. Each of the Parties agrees that any dispute arising out of or in connection with this Agreement, including any question regarding its existence, validity or termination shall be submitted to the exclusive jurisdiction of the courts of the Province of Quebec, Canada (district of Montreal). The foregoing choice of jurisdiction and venue shall not prevent either Party from seeking injunctive relief with respect to a violation of Intellectual Property Rights, confidentiality obligations or enforcement or recognition of any award or order in any court of competent jurisdiction. ### 14.4 Force Majeure In no event shall either Party be responsible or liable for any failure or delay in the performance of its obligations hereunder (except for any payment obligations) arising out of or caused by, directly or indirectly, by a Force Majeure event; it being understood that the Party subject to a Force Majeure event shall use reasonable efforts which are consistent with accepted practices to resume performance as soon as practicable under the circumstances. ### 14.5 Relationship Between the Parties The Agreement is an agreement between independent legal entities and neither Party is the agent or employee of the other Party for any purpose whatsoever. The Parties do not intend to create a partnership or a joint venture between themselves. Neither Party shall have the right to bind the other to any agreement or to incur any obligation or liability on behalf of the other Party. ### 14.6 Entire Agreement The Agreement (including all related Quote(s) which are an integral part thereof), constitutes the complete agreement between the Parties and cancels and replaces all prior or concomitant agreements or representations or warranties, oral or written, between the Parties concerning the subject matter of the Agreement. The Agreement may not be modified or amended, in whole or in part, except in writing signed by a duly authorized representative of each Party; no other act, document, usage or custom will be deemed to modify the Agreement. ### 14.7 Assignment Neither Party may assign the Agreement without the prior written consent of the other Party, which shall not be unreasonably withheld or delayed; provided, however, that any Party may, without such consent, assign the Agreement (i) to any person in the event of a transfer to such person of all of its shares, all or substantially all of its assets, or a merger, amalgamation or similar business combination with such person; or (ii) to an Affiliate in connection with a reorganization of such Party. ### 14.8 Successors and Assigns All obligations set forth in the Agreement will bind and apply to the benefit of the respective successors and assigns of the Parties. ### 14.9 Severability If a competent court rules a provision of the Agreement invalid, illegal or unenforceable, the validity, legality or enforceability of the other provisions of the Agreement shall in no way be affected or compromised. ### 14.10 Waiver The failure of a Party to enforce any provision of the Agreement shall not constitute a waiver of such provision or the right of such Party to enforce such provision and every other provision. ### 14.11 Notices All notices, demands or other communications required or permitted to be given or made under the Agreement shall be in writing and delivered personally or sent by prepaid registered post or by email addressed to the intended recipient thereof at its address or email address, and marked for the attention of such person (if any) as is set out on the signature block of the Agreement, or at such other address as a Party may by notice advise. --- --- title: Agent Marketplace description: Browse Vasco revenue agents you can deploy. canonical: "https://vasco.app/agents/marketplace" --- # Agent Marketplace - **Churn Prediction** (protects_revenue) — Catch churn before it happens, weekly scan of customer communications ranked by risk. - **Customer Health Score** (protects_revenue) — Every customer scored on health, with renewal risks flagged 90 days out and a next action per account. - **Expansion & Upsell** (sells_more) — Find which customers are ready to expand, ranked with a named play per account. - **CRO / CEO Brief** (runs_the_board_room) — Weekly revenue briefing, top issues, deals at stake, and the questions to ask. - **Board & Investor** (runs_the_board_room) — Board prep in minutes. Investor-ready narrative with quantified risks, opportunities, and recommendations. - **Capacity & Hiring Signal** (builds_better_reps) — Know exactly when to hire and how many, modeled against targets with ramp time. - **Pipeline Health & Coverage** (sells_more) — Know if your pipeline covers the target, at-risk deals named, gaps quantified. - **Revenue Health** (protects_revenue) — Find where growth is stuck, one bottleneck, quantified ARR impact, three actions. - **ICP & TAM Discovery** (finds_next_customer) — Validate your ICP against real deal outcomes, size your TAM per segment, and know where to double down. - **Channel Attribution Model** (finds_next_customer) — See which channels actually drive revenue. Four attribution models ranked by composite ROI. - **Customer Quote & Ambassador Candidates** (finds_next_customer) — Find your best advocates, ready-to-use quotes pulled from real conversations. - **GTM Alignment** (finds_next_customer) — Find the friction in your go-to-market, scored across ICP, motion, and messaging. - **Pipeline Contribution** (sells_more) — See which GTM functions pull their weight, pipeline by source paired with win rate. - **Sales–Marketing Feedback Loop** (finds_next_customer) — One shared view of the sales and marketing handoff. Every finding split by who owns the fix. - **Activation Signals** (sells_more) — Monitors product usage and customer engagement in real-time, identifying key activation moments and buying, churn or upsell signals. Monitors product usage and customer engagement in real-time, identifying key activation moments and buying, churn or upsell signals. - **Usage Signals** (protects_revenue) — Analyzes product usage and behavior to identify at-risk accounts and opportunities for upselling and renewals, enabling proactive retention strategies. Analyzes product usage and behavior to identify at-risk accounts and opportunities for upselling and renewals, enabling proactive retention strategies. - **Usage to Revenue Patterns** (runs_the_board_room) — Examines how product usage affects revenue, pinpointing patterns that indicate growth, churn, upselling success, and customer lifetime value. Examines how product usage affects revenue, pinpointing patterns that indicate growth, churn, upselling success, and customer lifetime value. - **Autonomous CRM (MEDDIC Auto-Capture)** (builds_better_reps) — Tracks sales activity and identifies MEDDIC signals in real-time, highlighting coaching opportunities and deal risks without manual entry. Tracks sales activity and identifies MEDDIC signals in real-time, highlighting coaching opportunities and deal risks without manual entry. - **Business Review Prepper** (runs_the_board_room) — Your business review, ready for the exec team. Covers weekly, monthly, or quarterly cadence. - **Deal Intelligence** (sells_more) — Walk into every deal review fully primed. Health score, engagement, and the single next action per deal. - **Forecast** (sells_more) — Bottom-up forecast with call-transcript signal on every Commit and Best Case deal. - **ICP Analyst** (finds_next_customer) — Discover who your best customers really are. Traits that predict deal success, drawn from real outcomes. - **Messaging Coach** (builds_better_reps) — Learn what messaging wins deals, value props and objections scored against outcomes. - **Pipeline Review** (sells_more) — Monday pipeline call in 20 minutes, deal cards, slip-risk flags, CRO summary. - **Revenue Target** (sells_more) — Reverse-engineer your revenue target into pipeline, leads, and headcount across three scenarios. - **Win / Loss Analysis** (finds_next_customer) — Understand why you win and lose, three recommendations ranked by revenue impact. --- --- title: "AI in GTM & RevOps" description: "How AI is reshaping go-to-market and RevOps. This pillar covers the economic, operational, and organizational impact of AI, helping leaders understand where it creates leverage, where it breaks assumptions, and what changes next." canonical: "https://vasco.app/blog/pillar/ai-in-gtm-revops" --- # AI in GTM & RevOps How AI is reshaping go-to-market and RevOps. This pillar covers the economic, operational, and organizational impact of AI, helping leaders understand where it creates leverage, where it breaks assumptions, and what changes next. ## What you will find - Where AI creates real leverage across GTM and RevOps - How AI changes decision-making, not just productivity - Economic, operational, and organizational impacts of AI - New constraints AI introduces (costs, data quality, reliability) - What GTM leaders should rethink as intelligence becomes embedded in systems --- --- title: "GTM & RevOps execution" description: "Content focused on how go-to-market strategies operate in practice, day to day. This pillar explores how teams activate their RevOps architecture through sales, marketing, and CS motions, enablement, workflows, and operating cadences that turn strategy into consistent, repeatable execution." canonical: "https://vasco.app/blog/pillar/gtm-revops-execution" --- # GTM & RevOps execution Content focused on how go-to-market strategies operate in practice, day to day. This pillar explores how teams activate their RevOps architecture through sales, marketing, and CS motions, enablement, workflows, and operating cadences that turn strategy into consistent, repeatable execution. ## What you will find - How GTM motions operate day to day inside the system - Sales, marketing, and CS motions in practice - Cross-functional alignment across sales, marketing, CS, and RevOps - Enablement, workflows, cadences, and operating rhythms - How teams turn strategy into repeatable behavior --- --- title: "Revenue planning, forecasting & economics" description: "How revenue teams plan, forecast, and reason about the economics of growth. This pillar covers planning playbooks, board-ready forecasting, unit economics, and the financial models behind durable, predictable revenue." canonical: "https://vasco.app/blog/pillar/revenue-planning-forecasting-economics" --- # Revenue planning, forecasting & economics How revenue teams plan, forecast, and reason about the economics of growth. This pillar covers planning playbooks, board-ready forecasting, unit economics, and the financial models behind durable, predictable revenue. ## What you will find - How revenue targets are set, cascaded, and evaluated - Forecasting models, assumptions, and failure modes - The economics behind growth: unit economics, cost structures, and tradeoffs - Board-level metrics, expectations, and decision frameworks - How financial models shape GTM strategy and investment choice --- --- title: "RevOps maturity & scaling journeys" description: "How companies evolve as they grow. This pillar explores the transitions from founder-led to scalable growth, maturity benchmarks, leadership shifts, and the challenges teams face at each stage of scale." canonical: "https://vasco.app/blog/pillar/revops-maturity-scaling-journeys" --- # RevOps maturity & scaling journeys How companies evolve as they grow. This pillar explores the transitions from founder-led to scalable growth, maturity benchmarks, leadership shifts, and the challenges teams face at each stage of scale. ## What you will find - The transition from founder-led to system-led growth - GTM and RevOps maturity stages and benchmarks - Organizational and leadership shifts required at each stage - Common failure patterns during scale (and why they repeat) - How teams adapt processes, roles, and expectations as complexity increases --- --- title: "RevOps systems & architecture" description: "Content about the systems, data models, and structural foundations that underpin modern RevOps. This pillar focuses on how revenue teams design their architecture: defining the customer lifecycle, aligning around a single source of truth, and instrumenting data, metrics, and workflows to support scale." canonical: "https://vasco.app/blog/pillar/revops-systems-architecture" --- # RevOps systems & architecture Content about the systems, data models, and structural foundations that underpin modern RevOps. This pillar focuses on how revenue teams design their architecture: defining the customer lifecycle, aligning around a single source of truth, and instrumenting data, metrics, and workflows to support scale. ## What you will find - Lifecycle design (stages, handoffs, ownership) - Data models, metrics definitions, and instrumentation - Operating models and source-of-truth architecture - How teams structure and connect systems to make execution possible --- --- title: "What stays human when RevOps goes AI-native | In conversation with Anthony Enrico" description: "Anthony Enrico of Leanscale ran a company-wide hackathon, rebuilt how his team works, and arrived at a conclusion that surprised him: the agents were never the hard part. Here's what actually determines whether AI in RevOps delivers or disappoints." canonical: "https://vasco.app/blog/ai-native-GTM-anthony-enrico" date: "2026-09-03T21:55:00.000Z" authors: - Anthony Enrico jobTitle: CEO and Founder readingTimeMinutes: 8 contentType: video intent: strategy-insights pillar: "RevOps maturity & scaling journeys" audiences: - cros - founders - fractional --- # What stays human when RevOps goes AI-native | In conversation with Anthony Enrico Anthony Enrico has spent five years building Leanscale, a RevOps agency for VC and PE-backed companies. He ran a company-wide AI hackathon in December 2024, watched his assumptions about agents get inverted, and spent the next nine months rebuilding how his team operates. We sat down with him to find out what actually changed, what the mistakes look like from inside, and what RevOps leaders still can't hand to a model. Watch the full replay: [The data model is 90% of AI-native RevOps | Anthony Enrico, LeanScale](https://www.youtube.com/watch?v=WcPjQ_bQcvs&t=2918s) ## Where most RevOps teams are right now In December 2024, Anthony flew the entire Leanscale team to Arizona for an off-site hackathon. He came back awestruck at what they built. Claude Max accounts went out the following week. Their CTO trained every person on how to use them. The instruction: stop planning and start building. Most companies are in that same window right now, without an off-site to inspire the shift. The poll we ran at the start of this webinar told the story plainly: - **33%** hadn't started yet - **33%** had connected AI to their tools but weren't seeing results - **33%** were actively building - **0%** said they were fully AI-native and iterating One-third of practitioners have built the wiring and gotten nothing. A foundation gap explains every one of them. Let’s get into it. ## Why agents fail without a data model Every stuck AI deployment runs the same sequence: 1. The board issues a mandate. 2. The company distributes licenses. 3. Reps experiment. 4. Workflows appear in Slack. It looks like momentum until someone asks the agent a question and gets a confident, wrong answer. Ask any team: how much pipeline did we build last week? You'll get ten different answers depending on how each person defines pipeline. Put an agent on that ambiguity and you don't get ten uncertain answers — you get one certain wrong one. The models aren't the issue. Anthony's analogy: an F1 car with no map. The most common mistake is starting with agents before building the data model they'll run on. ## How to build a semantic layer That 33% who connected AI and got nothing back all ran into the same wall. They plugged agents into their data without first defining what the data means. The agent looked at a field called "pipeline," made its best guess, and returned something different from what the CRO would say — and different again from what finance would say. The fix is a semantic layer, or a set of written definitions that tell your agents what your metrics actually mean. This means any definitions your business uses to measure success. "It's really just writing down definitions," Anthony said. "It's not anything more fancy or intellectual than that." The exercise runs three steps: 1. **Identify your contested terms.** SQL, pipeline, churn, customer, ICP: whatever your go-to-market motion depends on. If two people in a room would define it differently, it goes on the list. 2. **Write the definition your business actually uses.** Your version: "a customer is anyone with a Salesforce record marked account type: customer and an active billing record in QuickBooks." 3. **Connect the definition to the data.** The agent reads the definition first, pulls the data that matches it, then answers. Ask five times, get the same number. This is what Vasco does at onboarding: walk through every go-to-market definition before any agent runs on top of it. Anthony's team starts every new engagement the same way: semantic layer first, data model second, agents third. This is also why RevOps owns this problem and not data engineering. Data engineers know the schema, while RevOps knows what the metrics mean to the business. Those are different things. ## How the context graph connects your data The semantic layer tells an agent what things mean. The context graph tells it how they connect. For a single customer account, Leanscale ingests Slack data, call transcripts, CRM records, project management activity, and external GTM performance signals, all tied back to the same account. Every signal routes to the right place, so agents draw from a complete, accurate picture of each relationship. "The semantic layer has the definitions," Anthony said. "The context graph is connecting all the data points." The practical result: a go-to-market diagnostic that used to take weeks now takes 48 hours. Not because the analysis got faster — because the foundation makes the analysis possible without the manual download, pivot, and rebuild cycle. ![semantic layer and context graph](https://cdn.sanity.io/images/ys8gstp8/production/8aa7e3b1b890920322f559d71ef1f63fc7b0a1f9-2400x928.png?w=1600&fit=max&auto=format) **Related resources:** - [Context graphs for GTM teams](https://vasco.app/blog/context-graphs-for-gtm-teams): Build one without a data team. - [Revenue data provenance: A forecast accuracy guide for CROs](https://vasco.app/blog/data-provenance-forecast-accuracy): Trace numbers before your next QBR. - [Centralized vs. decentralized AI in GTM: In conversation with Kyle Norton](https://vasco.app/blog/build-or-buy-gtm-context-layer): Kyle Norton on context that compounds. ## What AI can handle today Once the data model and context graph are in place, a specific set of tasks moves to AI. These tasks were always better suited to a machine — teams did them by hand because they had no alternative. ADD TABLE Data assembly bottlenecked every task in this list. The thinking was fast. Getting the data into a usable shape was slow. Once the context layer exists, that work disappears. ## What stays human The human list is shorter than it sounds, but the things on it are irreducible: ### GTM strategy AI can surface research, run scenarios, and stress-test assumptions. Owning the call is different. Understanding the context, the trade-offs, and the stakes still requires someone accountable to the outcome — someone who can explain not just what to do, but why. ### Business process design Building a go-to-market motion that fits a specific company — their ICP, their team structure, their stage — takes lived experience and judgment. An agent can help document the process once it exists. It can't invent it from scratch. ### Accountability This one is underrated. Customers still want someone they can trust before making a big decision. These are high-stakes investments. If Leanscale makes a bad call for a customer, they can be held to it. Nobody's calling Anthropic. ### Stakeholder alignment Getting the board, CRO, and cross-functional team pointed in the same direction requires political context, relationship history, and timing judgment no model has access to. Judgment and accountability are the human work, while execution and synthesis are getting faster and cheaper every quarter. ![RevOps humans VS. AI](https://cdn.sanity.io/images/ys8gstp8/production/05ffd33a1135d6adab765cd6369563162c920acb-2400x1008.png?w=1600&fit=max&auto=format) ## The performance-to-plan blind spot Most companies build the annual plan in November and stop looking at the assumptions by February. Teams reverse-engineer the ARR target into a bookings plan, a pipeline plan, a churn plan, conversion assumptions, and velocity targets. Then the year starts, and those assumptions sit in a spreadsheet while live data lives in the CRM. The gap between them stays open all year. For AI to measure plan variance, the context layer needs to know what the plan assumed: which conversion rate, which sales cycle length, which velocity targets. A pipeline number shows what's in the funnel. Knowing whether that's enough requires the plan's assumptions to live in the same layer as the live data. Define those targets in the context layer and two numbers become visible in real time: - **Conversion rate.** A 1-point improvement on a $20M plan is $1M. A 1-point drop is the same loss, and it won't surface in the aggregate pipeline number until Q4. - **Sales cycle length.** One fewer day in a 90-day cycle adds roughly $250K. Nine days off plan is a Q4 miss already sitting in your velocity data. With those targets defined, an agent reports that your conversion rate is 3.2 points below assumption and shows exactly why the current pipeline won't close the year — rather than a graph that looks fine until someone checks the Y-axis ## Why RevOps teams win with AI Nine months before this webinar, Anthony thought LeanScale might not be relevant in a year. He assumed the future belonged to agent workflow builders, and that RevOps agencies would have to become software shops to survive. The opposite happened. "I can spin up an agent in 10 minutes. I can build a very, very good forecasting agent and workflow, if I have the data model. My value has been: the data model is 90% of it. This agent-building part? That's 10%. And anybody can build this." The bottleneck in every AI deployment is the same thing RevOps has always owned: context. Metric definitions. ICP logic. Attribution rules. Lifecycle stages. The business understanding that makes any AI answer accurate instead of just plausible. The webinar audience confirmed it. When we asked what their biggest concern was about going AI-native, 50% named the data foundation. Technology, agents, and budget barely registered. That's the RevOps problem. Companies that understand this are already leaning on their RevOps leaders to run AI strategy at the executive level. Anthony has watched heads of RevOps and VPs of RevOps move into COO and CRO roles as a direct result of this shift. "If you're sitting in RevOps right now, just soak it up. Take the opportunity. Raise your hand. Lead these strategies. It'll open up doors." ![Humans vs AI with a context graph](https://cdn.sanity.io/images/ys8gstp8/production/7a0276932ce3b03afe6e3149a1854c20e09b1fee-2400x1096.png?w=1600&fit=max&auto=format) ## Two companies, one year from now Two RevOps leaders are reading this today. **Company A** spends the next six weeks doing the unglamorous work: writing down definitions, auditing the data model, connecting CRM to Slack to billing in a context graph, and baking plan targets into the layer before a single agent runs on top of it. By Q1, their forecast takes minutes. Their CRO self-serves answers instead of waiting three days. They catch conversion rate variance against plan the week it starts drifting — weeks before it becomes a miss. **Company B** buys licenses, lets reps experiment, and waits to see what sticks. By Q1, they have a dozen disconnected workflows and answers no one quite trusts. The board asks about AI strategy. The response is a Slack thread. Six weeks of unglamorous work separates them. The data model is either your foundation or your bottleneck — and right now, it's one or the other. If you want to hear Anthony walk through how Leanscale actually made this shift — the hackathon, the data model work, the specific workflows they rebuilt — watch the full webinar. ## FAQ ### What is the semantic layer in RevOps? The semantic layer is a set of written definitions that tell your AI agents what your data actually means — what "pipeline" is, what "a customer" is, what a SQL or a churned account looks like in your business specifically. Without it, agents infer — and the "confidently wrong" answers you get back are the result. With it, agents retrieve the exact numbers behind a specific definition and return a deterministic, auditable output. ### How do you teach an AI agent to answer "why" questions about revenue? Start by zeroing in on the root cause metric. If sales velocity dropped, break it into its three components — conversion rate, average contract value, and sales cycle length — and identify which one moved. Then let the agent correlate against everything that changed in that window: call transcripts, competitive mentions, rep activity, external signals. The "why" emerges from the intersection of a precise metric definition and a broad data set. ### What AI tasks can RevOps actually delegate today? The ones that work reliably: forecast rollups, proposal and SOW generation, customer health scoring across multiple data sources, go-to-market diagnostics, and performance-to-plan gap analysis. These work because they run on structured data with clear definitions. Tasks that still need a human: strategy decisions, business process design, stakeholder alignment, and anything where accountability matters. ### What stays human when RevOps goes AI native? Three things reliably: strategy (what to do and why), business process design (what the motion should look like), and accountability (someone who can be held responsible when a big call goes wrong). AI can surface options, synthesize data, and run the analysis. It can't own the decision or take the call from the customer when the recommendation didn't work. ### How has the RevOps function changed with AI? It's become more strategic, not less. The execution work — data analysis, reporting, diagnostics — is getting faster and cheaper. That's freeing up RevOps leaders to do the thing agents can't: understand the business deeply enough to make the data definitions accurate in the first place, and own the context layer that every agent draws from. Companies are already leaning on RevOps leaders to run AI strategy at the executive level. ### How does Vasco support an AI-native go-to-market motion? asco is the revenue data layer purpose-built for AI agents. It resolves identities across your revenue systems, holds your metric definitions in one place, connects plan targets to actuals, and grounds every agent you deploy in a shared context graph of 750+ sources. You don't have to build the data infrastructure before you can get accurate answers — Vasco is that infrastructure. ### What is a context graph and why does RevOps need one? A context graph connects data from every revenue-related system — CRM, call transcripts, project management, billing, Slack — and links it to the account it belongs to. Without one, agents query systems in isolation and miss context that would change their answer. With one, every agent draws from the same grounded, connected version of reality. The semantic layer says what things mean; the context graph shows how they relate. --- --- title: Becoming an AI-native go-to-market organization description: "Modern go-to-market runs on speed and alignment. Both come from a connective tissue that ties your people, systems, strategy, and tools to the market you have chosen. To make it work for AI, that tissue needs a technical foundation." canonical: "https://vasco.app/blog/becoming-ai-native-GTM-organization" date: "2026-08-20T20:40:00.000Z" authors: - Hannah Ajikawo - Guillaume Jacquet contentType: article intent: strategy-insights pillar: "RevOps systems & architecture" audiences: - revops --- # Becoming an AI-native go-to-market organization _The connective tissue_ Last quarter, an AI agent told a revenue team their pipeline was on track. They were 21 percent below target. Nine deals had stalled for a month, and a recorded call where a buyer froze the budget had never reached the deal it belonged to. The agent could read every one of those systems. It still reported the wrong number, with complete confidence. That gap, between what the tools hold and what the agent understands, is the line between a go-to-market that runs on AI and one that only looks like it does. For a company between one and fifty million in revenue, the question is no longer whether to add AI. It is what to put in place so the AI is fast and right at the same time. That does not come from more tools, or from pointing an agent at the stack you already run. It comes from what sits underneath: the connective tissue that aligns your people, systems, strategy, and tools around your market, and the technical foundation that makes it work for AI. ## More tools and a smarter agent are not enough Start with that team. Under the same speed pressure, they had done what most organizations do: connected an AI agent to the systems they already run, HubSpot, Gong, Stripe, and Slack, through a protocol like MCP. It feels like progress. It is not, because connecting a system is not the same as connecting several of them. Peel their one-line answer apart and it gets worse the deeper you go. ![](https://cdn.sanity.io/images/ys8gstp8/production/2f35ebede0d971cd79187364eaa9319c3dbba13f-2400x882.png?w=1600&fit=max&auto=format) **Every fact sat in a system the agent could read. **None reached the summary, because reading a system is not the same as connecting several of them. The agent averaged across them and reported a number that was invented. The automation did not only miss the signals. It replaced the manual check that used to catch them. On a disconnected stack, an AI agent makes you faster and wrong at the same time. ## The connective tissue needs a foundation: the context graph. To move fast and stay right, an organization has to align its strategy and its execution. The connective tissue of a modern go-to-market, the concept behind [Revenue Funnel's Symbiotic I/O model](https://revenuefunnel.co.uk/), is how a company augments its people, systems, strategy, and tools to connect with its ideal market. To make that actionable for AI, the connective tissue needs a concrete technical form: a context graph. It sits above your tools and turns them into a single reasoned view of every account, reading from your systems and adding meaning above them. It does not replace your warehouse, your CRM, or your call recorder. ![](https://cdn.sanity.io/images/ys8gstp8/production/dac60ed3efdd37abe5f66bae0d9f699a3189b292-2400x1034.png?w=1600&fit=max&auto=format) Seven systems reconciled into one account, then the meaning the agent reasons across. The graph references your tools rather than copying them, so it stays current. Identity is the floor: one person and one account recognized across CRM, calls, billing, and product. On top of it, the graph rebuilds the timeline, holds the plan so "on track" has a number to check against, remembers which patterns led to wins and churn, and draws the causal links between them. The agent reasons over one account instead of seven systems. ## Author your definitions, or the agent reasons on noise A context graph connects the data. It cannot decide what the data means. That is the part most organizations skip, and it is the part that determines whether the whole thing works. What counts as a qualified lead. How you define churn versus downgrade. Who your ICP actually is. What this quarter's target is, by motion. These are business decisions, not patterns an agent can infer from raw records. The knowledge was never in the data. It has to be authored. A better model will not conjure it, because the same records support several valid definitions and only you know which one is yours. Authoring foundations is operator work, the judgment that comes from having fixed a go-to-market before. Most teams do not have that capacity sitting idle in-house. ![](https://cdn.sanity.io/images/ys8gstp8/production/ca876c8be88db03af12538a51d86dac6fb1883c7-2400x738.png?w=1600&fit=max&auto=format) ![](https://cdn.sanity.io/images/ys8gstp8/production/377c09602461702338cbb7c80565910c459bb3bd-2400x728.png?w=1600&fit=max&auto=format) **The foundation decides what sits on top of it.** Vague foundations produce noisy execution and confidently wrong reasoning. Authored ones produce signal. Decide what you mean, write it once, and make it the floor everything reasons on. > **“If the foundations are vague, everything built on top becomes noise.” **– Hannah Ajikawo, The Symbiotic I/O Framework ## Speed is the moat, and the layer is what makes it safe. The advantage goes to the team that can reach a correct answer quickly and change its mind quickly when the market moves. Two kinds of speed matter, and the layer governs both. The first is reasoning speed, how fast an agent turns data into a correct answer, and it rests on accuracy, because accuracy multiplies down a chain rather than averaging. ![](https://cdn.sanity.io/images/ys8gstp8/production/a7f6804cab1b0c1369c7c3458158899177add478-2400x424.png?w=1600&fit=max&auto=format) **Four steps, each 95% reliable, yield about 81%. **On a disconnected stack the error compounds into a confident guess. On a resolved layer each step reasons over one account, the chain holds, and you can let an agent act without re-checking it by hand. The second is change speed: how fast you can update what the agent believes when the motion shifts. This is where most organizations quietly lose. Go-to-market moves faster than a data team can ship. A new motion, a new channel, a new signal: each one rewrites a definition, and it happens monthly, not quarterly. Route every change through a data ticket with a one-to-two-month turnaround and your definitions are stale for most of the year. > **The rule on ownership** > Definitions belong to RevOps, and they have to be no-code. The judgment already sits with RevOps, so the people who set the definitions should change them directly, in hours, with no engineering ticket and no waiting on a sprint. A definition you wait a sprint to change is already wrong by the time it ships. ## Buy the substrate, build the intelligence The layer splits into two halves with opposite answers, and the call is quick. ![](https://cdn.sanity.io/images/ys8gstp8/production/39623890024e44fa4cac232b3a7497e52ed78f06-2400x858.png?w=1600&fit=max&auto=format) Infrastructure gives you no advantage in the building, so buy it. Intelligence compounds for you alone, so build and own it. On the buy side, a context-graph platform lets you skip the build entirely and keep your effort for the intelligence on top of it. Then centralize the ownership. A small team builds for everyone and delivers the output into the tools people already use. Reps do not run agents. They receive the answer inside their CRM or Slack and stay with customers. Because everyone queries the same layer, the number a CFO pulls matches the number a rep pulls. ## What to put in place this week You do not need a platform decision or a budget to start. The first pass fits in an afternoon, and it shows you exactly where the alignment between your strategy, systems, and market is breaking down. 1. **Run the peel test. **Ask your AI or your dashboard whether you are on track this quarter. Then check the answer against four things: which deals are stalled, what the last calls actually said, whether billing matches the CRM, and the target it is measured against. Write down every place it breaks. That list is your gap. 2. **Put your foundations on one page.** What counts as an SQL. Churn versus downgrade. Your ICP. This quarter's target by motion. Ask two people each question. Where the answers differ, you have found a foundation nobody has written down. 3. **Pin the one definition people argue about most. **Pick the metric that means three different things to marketing, sales, and RevOps. Agree one version, in writing. That single pinned agreement is the first step in aligning your go-to-market execution. 4. **Give RevOps the pen, with no ticket queue. **RevOps owns the definitions and changes them in hours, not a sprint. If a change still needs an engineer, the layer is in the wrong hands and you will fall behind your own market. 5. **Map the five or six systems that hold ninety percent of your context.** CRM, calls, email, billing, product, and the channel where decisions actually happen. Resolve identity across them before you point any customer-facing automation at them. Connect first; do not pour everything into an agent and hope. None of this needs new software. It needs you to decide what you mean and write it down, which is the build half from Part 05. --- --- title: Vasco is now in the Claude connector directory description: "Vasco is now listed in the Claude connector directory. Connecting takes one click and a sign-in, and Claude Code recognizes the same connection, so there is nothing to set up twice. Once connected, Claude reads your resolved accounts, your definitions, your targets, and your past outcomes from Vasco instead of guessing at raw CRM records." canonical: "https://vasco.app/blog/vasco-claude-connector-directory" date: "2026-08-18T21:23:00.000Z" authors: - "Sébastien Rothlisberger" jobTitle: "CTO & co-founder" readingTimeMinutes: 1 contentType: product-spotlight intent: playbooks-methods pillar: "GTM & RevOps execution" --- # Vasco is now in the Claude connector directory Vasco is now listed in the [Claude connector directory](https://claude.ai/directory/vasco), so connecting Vasco to Claude takes one click and a sign-in instead of a manual setup form. The listing is live for every Vasco organization today. [Vasco is now in the Claude connector directory](https://youtu.be/r5WT4fFdkQw) ## How do you connect Vasco to Claude? 1. Open the Claude connector directory, or open Settings and then Connectors inside Claude. 2. Search for Vasco and click Connect. 3. Sign in with your Vasco account. Anyone who connected Vasco before the listing existed is already set up and has nothing to redo. Claude Code recognizes the same connection, so connecting once covers both. ## What does connecting Vasco to Claude do? Connecting Vasco gives Claude the business context that raw CRM records leave out. A CRM stores values without storing what they mean, so Claude cannot work out on its own that a customer in your billing system and a company in your CRM are the same account. Once Vasco is connected, Claude can read four things it could not read before: - **Resolved identities.** One account is one account across every system you have connected. - **Your definitions.** A metric means the same thing every time someone asks for it. - **Plan beside actuals.** Every number arrives with its target and its variance. - **Tagged outcomes.** Today's deal is measured against the deals that already closed. Every number Claude returns then carries a source and a definition your own team wrote, and you can follow it back to the signal that moved it. ## Does connecting Claude give anyone new access to Vasco data? No. Signing in tells Vasco who you are and nothing further. Your organizations and your role still decide what you can reach, and Vasco checks that on every request. Someone with view-only permissions in Vasco keeps view-only permissions when working through Claude. ## Can Claude change things inside Vasco? Yes, with your approval. Claude can update parts of your revenue model through Vasco, including ideal customer profiles, go-to-market motions, and team structure. Claude asks for your approval the first time it uses one of these, so grant it when you genuinely want something changed. ## Connect Vasco Search for Vasco in the Claude connector directory and click Connect. Setup instructions and the full list of what Claude can do with Vasco are in the [Vasco MCP help article](https://help.vasco.app/en/articles/11487839-vasco-mcp). --- --- title: "Centralized vs. decentralized AI in GTM: In conversation with Kyle Norton" description: "We sat down with CRO of Owner.com, Kyle Norton, to discuss why 47% of go-to-market teams have zero AI agents in production. Here's why centralized AI compounds and decentralized AI stalls, plus the four-stage maturity ladder that shows where your team sits." canonical: "https://vasco.app/blog/build-or-buy-gtm-context-layer" date: "2026-07-31T18:43:00.000Z" authors: - Guillaume Jacquet jobTitle: "CEO & co-founder" readingTimeMinutes: 6 contentType: video intent: playbooks-methods pillar: "AI in GTM & RevOps" audiences: - revops - cros - founders - fractional - start-ups - scale-ups - enterprise --- # Centralized vs. decentralized AI in GTM: In conversation with Kyle Norton _Why 89% of agents never ship _ Centralized AI vs decentralized AI is the most important infrastructure decision a go-to-market team makes right now. Most teams start with the decentralized version by default: everyone gets a cloud license, every rep builds their own tools, and the org waits to see what sticks. That approach spreads AI literacy fast, but it rarely moves metrics. [Centralized AI works differently](https://vasco.app/case-studies/build-the-edge). A small team of specialists builds shared systems on a common data layer, and every agent, report, and workflow compounds on the same foundation. Owner.com, one of the most AI-native go-to-market orgs in B2B SaaS, built this way. The result was 20x ARR per dollar of AE compensation and $102K in closed-won ARR per BDR per month. This post breaks down why the gap exists, how to close it, and what to build and buy on the way there. [Why 89% of GTM AI agents never ship | Kyle Norton and Guillaume Jacquet](https://www.youtube.com/watch?v=ENQKp1ywBXU&t=1412s) ## Why decentralized AI often stalls The decentralized approach gives every rep room to experiment with ChatGPT, Claude, and automation platforms. That freedom sounds attractive. It also creates a few predictable problems: - Teams duplicate work - Outputs vary widely and quality stays uneven - Data quality breaks down without shared definitions - Results stay trapped in one-off chats and workflows - Metrics do not change Kyle Norton, CRO of Owner.com, has seen this play out at company after company: > "You can have everybody doing all this AI stuff, all these reps are posting their skills and workflows in the Slack channel, and it's really exciting…but your metrics will basically the same as where they were before." The pattern is consistent: excitement without business impact, demos stuck at 80% good. That last 20% determines whether your AI experiment goes to production, and it takes most of the effort and skill. ## Why centralized AI compounds Centralized go-to-market AI means one team, typically applied AI engineers or GTM engineers, building shared systems for everyone. Those systems connect to the right data, follow governance rules, and feed back into a common context layer that every agent draws from. Kyle is direct on the quality difference. "What an applied AI engineer builds isn't just going to be 20% or 30% better," he said. "They're going to build something that's 5 or 10x better, or maybe 100x better, because if the baseline is almost zero value, what those people build is so much higher that it was essentially a waste for anybody else to attempt it." The compounding effect comes from architecture. Centralized data shares context, reuses memory, and improves together, so every new agent makes the others more accurate. Decentralized systems build in parallel but never connect. ## What is the AI maturity ladder for go-to-market teams? The AI maturity ladder maps four stages of adoption, from using AI as a search replacement to running a self-improving GTM system. Most go-to-market teams sit at stage two. Fewer than 2% reach stage four. The framework below draws on Brendan Short's [The Signal](https://thesignal.beehiiv.com/). ### The dabbler: using ChatGPT instead of Google ![](https://cdn.sanity.io/images/ys8gstp8/production/0ee348c7e5671b2539ffe13ff3dc79e084dd00ed-2400x1260.png?w=1600&fit=max&auto=format) The dabbler uses AI to answer questions instead of search, and nothing connects back to the business. Useful for individuals, but invisible at the org level. ### The tinkerer: scattered tools ![](https://cdn.sanity.io/images/ys8gstp8/production/1a4ae5ac29306fbabb3a2fb73367127f51333d35-2400x1260.png?w=1600&fit=max&auto=format) Teams build prompts, chats, and skills for tasks like pre-call prep. People share what they build, but the effort stays grassroots. This stage struggles to improve outcomes without an infrastructure to the way GTM works across functions. ### The automator: workflows ![](https://cdn.sanity.io/images/ys8gstp8/production/8c30af3ef0b34145513c0e35e89f3cf604540381-2400x1260.png?w=1600&fit=max&auto=format) Teams build automated workflows that connect multiple systems: inputs become outputs, Salesforce gets updated, and alerts fire. It’s useful, but workflows remain loosely connected and don't share context. ### The architect: context, memory, governance, and evals ![](https://cdn.sanity.io/images/ys8gstp8/production/50038e8d065e57c2a7648503c3e19074e4b15746-2400x1260.png?w=1600&fit=max&auto=format) This is the turning point. Go-to-market AI becomes a broader infrastructure layer. Agents share context, central files stay available across the whole system, call outcomes feed back in, and every report and dashboard pulls from the same source of truth. Kyle describes the shift: > "You have this system where I am controlling the context, curating who can access what information in a thoughtful manner, and that then allows you to get better at everything that is under it: the reports, the dashboards, the automations." This is also why so few teams reach this stage. Kyle cites the data: 89% of AI agents never ship to production, and most orgs can't name the ROI from the agents they've deployed. The bottleneck is always the same: skipping the infrastructure layer. ## Why the gap from 80% to production is bigger than it looks "It takes minutes to get something from 0 to 80% good with AI," Norton said. "It's really hard to go from 80% done, which is actually not really good enough to be in production. If something is wrong 20% of the time, you have so much human in the loop that it wasn't even worth automating in the first place." Production-ready go-to-market AI requires strong access controls, accurate data, proper context, testing and evaluation, reliable workflows, and clear ownership. Most teams never invest the hours to close that gap. That's why 53% of go-to-market teams report no measurable return from AI investment. ## What is the GTM AI harness, and why does it matter? A production-ready go-to-market AI stack needs more than a model. It needs a harness: the scaffolding that directs a model's raw intelligence toward specific, trustworthy outcomes. ### The model The model provides intelligence. It can answer questions, generate content, and reason across tasks. Without direction, it hallucinates, over-generates, and produces answers that look right but aren't. ### The harness The harness includes tools and connectors, roles and permissions, governance, context curation, and access controls. Here’s Kyle’s analogy: > "The model is like the wild horse, and you need to put a harness on the horse. They need a bit, and you need reins, and a saddle and stirrups. That is how you take the wild power of a horse and direct it for some outcome." Claude and ChatGPT are themselves harnesses: they take a model and add connectors, guardrails, and web access on top. ### Context Context is the information an agent needs to reason accurately: customer data, ICP definitions, positioning, competitor handling, product how-tos, table definitions, and plan targets. This is what separates a real answer from what Kyle calls "AI slop answers that lead you astray." ### Memory Memory stores what the system learns over time, avoiding repeated failures and reusing what works. Short-term context (conversation history) and longer-term memory (learned patterns, past outcomes) both matter for go-to-market AI that improves. ### Evals Evals are tests. They confirm whether an agent does its job correctly, and their output feeds back into improvements. Only 37% of go-to-market teams run evals today. That single gap explains most of why 53% see no return. ## What is a context graph, and why does RevOps need one? A [context graph](https://vasco.app/blog/context-graphs-for-gtm-teams) is the data foundation that makes go-to-market AI trustworthy. ![](https://cdn.sanity.io/images/ys8gstp8/production/971c1e86f8cc2337b8ad9ec8da665c6ab8a0c672-578x640.png?w=1600&fit=max&auto=format) Without one, agents query CRM, Gong, Slack, and Snowflake in isolation, reconcile nothing, and return answers that sound confident but contain errors. Norton describes the failure mode directly: > "You're querying your Snowflake instance or Salesforce because it's easy to set up those MCPs, and then when you get back this really compelling analysis, and then half of it is wrong." The cause?: > "You don't have this harness above it, which is telling the agents what is what in your data ecosystem and what information is up to date and what not and what tables they should access. You start to see this all disintegrate." A context graph resolves this by resolving identities across all revenue systems, rebuilding the account timeline, holding company-specific metric definitions, recording current account status, and linking targets, plans, and performance gaps. Think of it as a semantic layer purpose-built for agents, not a generic data warehouse schema bent into shape. Vasco handles identity resolution across all revenue systems, rebuilds the account timeline, holds plan targets, and tags every deal outcome against billing. > "It is the line we already draw at Owner. We buy our GTM context graph, so we can build our agents on top." ## What should go-to-market teams build vs buy? Five questions determine which side of the line a tool sits on: How critical is uptime? Does building give you something genuinely unique? What is the real engineering ROI? Does it produce intelligence reusable across surfaces? Does owning it give you an edge a vendor cannot replicate? **Buy when** uptime matters, differentiation is low, and the engineering cost outweighs the gain. A dialer, a data warehouse, and a context graph platform all pass this test. Norton is blunt: > "Don't bother rebuilding Snowflake. Just buy Snowflake." **Build when** the use case is custom to your environment, the build is tractable, and the output moves a meaningful metric. Owner.com's pre-call research agent is the clearest example: two weeks of engineering, 85% more calls booked, 85% more opportunities created. "There's not a lot of people out there that are going to build a tool that is custom for your environment," Norton said. "That's one I can definitely build on my own." Other strong builds: churn analysis and alerts, custom Slack bots, internal research agents, BDR scoring models. The principle that holds across both sides: own the intelligence. "You own the plan, you own the metric definitions, you own the ICP," Norton said. "Those are still really core to what you see in the business, and you can't fully delegate that to an agent." ## The risk of renting context If a vendor stores your AI's intelligence in a black box, that context walks when the contract ends or someone leaves. A centralized company brain, one place where agents reuse context, memories compound, definitions stay consistent, and new systems pull from the same source of truth, avoids this entirely. The intelligence belongs to the org, not the tool. ## Which AI use cases actually move go-to-market metrics? "Pipeline saves lives," Norton said. "If you want to reason about where you should apply AI for the most benefit, generating pipeline is pretty high likelihood." ### AI for BDR teams Kyle built a scoring model that ranks hundreds of thousands of prospects and delivers daily prioritized lists to BDRs. The system then synthesizes prospect data and surfaces outreach angles based on shared work history between reps and prospects: if a BDR and a prospect both worked at Stripe in overlapping years, the system flags it. Pre-call research built this way means reps know exactly what to say before they dial. The result: Owner.com's average BDR closes $102,000 in closed-won ARR per month, or $1.2 million per year. That is not a demo metric. ### AI for CRO work A [CRO needs fast answers](https://vasco.app/for/revops) across many layers of the business simultaneously. A centralized context platform makes self-serve analysis possible, going deep without waiting days for a data team. Kyle says: > "The challenge of being a CRO is you have to juggle a lot. You have to keep an eye on many things, and then be able to go super deep on the one that seems off. Tools like Vasco let you answer those questions on your own without having to ship it to a data team and wait multiple days for an answer." ## What business metrics are possible with centralized go-to-market AI? Here’s Owner.com's full results from the [case study](https://www.vasco.app/blog/centralize-first-buy-the-layer-build-the-edge): - 20x ARR per dollar of AE compensation - $102K in closed-won ARR per outbound rep per month, up from $36K - 4x closed-won ARR per rep versus direct competitors - 2x decision-maker connect rate - In some competitive comparisons, 10x the BDR output of much larger rivals "If I have 10 BDRs doing what 100 BDRs do at another company," Norton said, "it's just such an advantage." These numbers also free up budget for marketing, brand, and product investment that compounds the gap further. ## Which AI agents work in go-to-market today? Agents that consistently reach production and move metrics: - AI pre-call research (strongest signal-to-effort ratio) - BDR scoring and prospect prioritization - CRO briefing and self-serve revenue analysis - Internal research and competitive workflows - Custom Slack bots built on company-specific context These work because they connect to company-specific context and feed learning back into the system. ## Which AI agents aren't ready yet? Inbound lead follow-up agents and agents that call prospects directly still face real reliability challenges. Norton's framing: "I would be careful not to say that doesn't work. It's like, that doesn't work right now. Things change so fast." What fails today could be standard practice in 12 months. Treat these as watch-list bets, not dismissals. ## Buy to remain competitive Centralized AI in go-to-market does not just organize tools. It creates a system that learns, improves, and supports better decisions over time. Reaching the architect stage, building a context graph, making clear build-vs-buy decisions, and keeping the company brain in one place all point in the same direction: compounding business impact. As Kyle put it: > "Centralized AI is the thing that's gonna get you to compound." ## FAQ ### What is centralized AI in go-to-market? Centralized go-to-market AI means every agent, report, and workflow in your revenue org reasons from one shared data layer: shared definitions, account history, and outcomes. Reps stop building their own tools and start receiving better outputs inside the tools they already use. Vasco is that shared layer, a context graph your RevOps team owns without code, so centralizing does not require a data engineering team or a six-month build. You connect your sources, author your definitions, and every agent you put on top gets the same grounded foundation. ### What is the difference between centralized and decentralized AI for revenue teams? Decentralized AI gives every rep or team a license and lets them build independently. It spreads AI literacy fast but rarely moves business metrics, because individual builds lack shared context, governance, and the architecture to compound. Centralized AI uses a small specialist team to build shared infrastructure. The quality gap is not 20-30%. It is often 5 to 10x, because production-grade systems require engineering depth most frontline roles do not have. ### What is the AI maturity ladder for GTM teams? The ladder runs from L0 to L3. L0 is using AI as a search replacement: ChatGPT instead of Google, nothing connected to the business. L1 is scattered tools: individuals building their own prompts and workflows, useful but siloed. L2 is workflow automation: multi-system workflows that move data between tools, but still loosely connected and without shared context. L3 is the compounding layer, where context, memory, governance, and evals sit on one shared foundation every agent draws from. Most go-to-market teams today sit at L1 or L2. Reaching L3 is where metrics start moving. ### What is a context graph in RevOps? A context graph is a data layer that resolves identities across revenue systems, rebuilds the account timeline, holds plan targets and metric definitions, and tags deal outcomes against billing. It works like a semantic layer purpose-built for AI agents: when an agent queries pipeline health, it reasons across a single reconciled view instead of querying Salesforce, Gong, and Snowflake in isolation and returning a confident-sounding wrong answer. ### What should go-to-market teams build vs buy when it comes to AI? Buy infrastructure with high uptime requirements and low differentiation: dialers, data warehouses, context graph platforms. Build intelligence that is custom to your environment and moves a specific metric: pre-call research agents, BDR scoring models, churn alerts, custom Slack bots. The line shifts with every model release, but the principle holds: own the intelligence, buy the plumbing. ### Why do most AI agents never reach production in go-to-market teams? 89% of go-to-market AI agents never ship to production. The bottleneck is not the model. It is infrastructure. Getting to 80% good takes minutes. Getting from 80% to production-grade requires access controls, data accuracy, proper context, evals, and clear ownership. Most teams stop at the demo stage because they never build the harness that makes an agent trustworthy enough to run without constant human correction. ### How do you measure ROI from go-to-market AI? The metrics that signal real ROI are pipeline-level and productivity-level: closed-won ARR per rep, BDR output per month, decision-maker connect rate, and forecast accuracy. Demo quality and agent deployment count are not ROI. If the metric you are watching is "number of AI tools in use," you are measuring the wrong thing. Owner.com's benchmark: $102K in closed-won ARR per BDR per month, 4x close rate versus competitors, 20x ARR per dollar of AE comp. --- --- title: "Why not just connect Claude to HubSpot? | Vasco" description: "Every answer your revenue team needs is already in your call recordings. Here's how to actually use it." canonical: "https://vasco.app/blog/connect-claude-hubspot" date: "2026-07-28T20:25:00.000Z" authors: - Justin Hudon jobTitle: Head of Sales and Customer Success readingTimeMinutes: 5 contentType: article intent: strategy-insights pillar: "RevOps systems & architecture" audiences: - revops - cros - founders - fractional - start-ups - scale-ups - enterprise --- # Why not just connect Claude to HubSpot? | Vasco _Connecting Claude or Copilot straight to HubSpot or Salesforce looks faster. Here's why raw CRM data drops AI accuracy to 52%, and what fixes it._ Two prospects raised versions of the same objection recently. One compared it to Power BI: HubSpot already has dashboards, forecasting, and custom objects, so why add another layer. The other asked about wiring Copilot straight into Salesforce and skipping the middle step entirely. Both are reasonable questions. Model Context Protocol (MCP) makes it easy to connect Claude to a CRM in a few clicks, and the summaries it produces look clean. That's exactly what makes the gap hard to spot. ## AI reasoning is where CRM data breaks down Power BI and HubSpot's native dashboards are built to display what's already in the CRM. That part works fine. The problem shows up when an AI is asked to reason across that data instead of just chart it. Claude or Copilot connected directly to Salesforce doesn't inherit business logic. It inherits raw fields, and it treats every field as if it means exactly what it says. That's rarely true. "Qualified lead" means one thing to the SDR team and something else to the AE who inherits the deal. "Active" in the CRM can mean a rep hasn't touched the stage in six weeks, not that the deal is actually moving. A dashboard just displays that value. An AI agent reasons on top of it and reports a conclusion with full confidence, whether or not the underlying number was ever true. MCP is the connector that makes this possible in the first place, and it deserves credit for that. It gets Claude or Copilot into a CRM, call recordings, a billing system. What it doesn't do is clean any of it. It doesn't decide that the HubSpot record for one account and the Stripe record for the same account under a slightly different name are the same customer. It doesn't know a company's specific definition of a qualified lead versus HubSpot's default. MCP is the pipe. It was never the foundation. ## Two ways to hand Claude an answer There's a second distinction worth pulling apart, because it explains why some AI-CRM setups look sophisticated and still land at the same accuracy problem. One approach hands Claude a skill: written instructions, often kept in a repo, that describe how to go find the answer. Call the CRM for deal stage, call the billing platform for payment status, call Slack for mentions, then reconcile all of it into a narrative. Every time this runs, Claude is improvising the join logic in the moment, on data that has never been reconciled ahead of time. The instructions describe a path to an answer. They don't describe the answer. The other approach hands Claude the answer directly. Vasco's MCP exposes tools like query-context-graph and query-metric-engine that return a single structured result. Identity resolution, field definitions, and plan targets are resolved before Claude ever asks the question, not while it's assembling the response. Claude isn't told where to look and how to stitch the pieces together. It receives a fact that has already been checked. Skill instructions + raw tools, step by step: 1. Claude loads a skill describing how to query HubSpot, Stripe, and Slack directly 2. Claude calls each raw tool separately and gets back unreconciled fragments 3. Claude joins and reconciles the fragments itself, in the moment 4. Claude writes an answer built from raw data, with full confidence Result: 52% accuracy Vasco context graph, step by step: 1. Claude calls one tool: query-context-graph or query-metric-engine 2. Identities and field definitions were already standardized before the call 3. Plan targets and cross-tool signals were already loaded into the graph 4. Claude receives one structured answer, already checked against the foundation. Result: 98% accuracy A skill can tell Claude where to look. It can't make the data underneath trustworthy at the moment Claude looks at it. That's the gap a well-written skill file cannot close on its own, and it's the same gap the 52% accuracy test measures directly. ## A real pipeline review, and what it missed One B2B SaaS company, managing dozens of active accounts on HubSpot, connected Claude across its CRM, call recordings, billing platform, and Slack. The output looked routine: Claude reported the pipeline on track for the quarter, in the same confident tone it always used, so nobody double-checked it. The actual number told a different story. The team had a quarterly target of $240,000, tracked in a spreadsheet the RevOps lead updated once every 90 days. Claude had never seen that spreadsheet, so when it looked at $180,390 in closed-won revenue, it had no reference point to flag a 21% miss. Claude also reported a healthy 75% lead-to-opportunity conversion rate, because HubSpot tracks that stage cleanly, while missing the real bottleneck two stages later: a 34% conversion rate from qualified opportunity to committed deal, a number that never lived anywhere Claude could see it. Nine deals had gone untouched for over 30 days, no calls, no emails, no stage changes, and HubSpot still listed every one as active. Claude reported them as healthy pipeline. Three risks sat in systems the CRM connection never reached: a customer had canceled its payment method in the billing platform while the CRM showed the account as healthy, a prospect had flagged budget as a serious concern on a recorded call while the deal kept progressing in HubSpot as if the call never happened, and a competitor threat had surfaced in a Slack thread that nobody linked back to the deal record. Claude reported all three accounts as on track, because the systems where the real signal lived were never connected to the systems it was reasoning over. None of this was a model failure. Claude reasoned correctly on the data it had access to. The data it had access to was incomplete, and nothing in the setup gave it a way to know that. ## Research shows this is not an isolated case This pattern shows up well beyond CRM data specifically. [MIT's Project NANDA](https://projectnanda.org/#/) studied more than 300 enterprise AI deployments, 52 case studies, and over 150 leadership interviews, and found that: > roughly 95% of enterprise generative AI pilots failed to produce a measurable business return despite $30 to $40 billion in enterprise investment. The researchers were direct about the cause: model quality wasn't the deciding factor. [Gartner reached a similar conclusion](https://www.gartner.com/en/newsroom/press-releases/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027) looking specifically at agentic AI. The firm predicts more than 40% of agentic AI projects will be canceled by the end of 2027 due to rising costs, unclear business value, or inadequate risk controls, based on a poll of over 3,400 organizations actively investing in the technology. ## The 52% vs. 98% accuracy test Vasco ran a controlled test on this exact question using CRM data instead of the broader enterprise samples in the research above. Claude was given full MCP access to raw CRM data and asked a set of common RevOps questions across five scenarios. Answer accuracy landed at 52%. The same questions, routed through a layer that reconciles identities, enforces field definitions, and holds plan targets as a fixed reference point, brought accuracy to 98%. Same model, same questions, a different foundation underneath. That gap is the whole argument, and it isn't a model quality problem. Claude and Copilot are both capable of correct reasoning when the inputs are trustworthy. The failure sits one layer below the model, in data that was never reconciled before the AI touched it, which is exactly the pattern MIT, Gartner, and McKinsey all describe at the broader enterprise level. ## Direct connection versus governed layer, side by side - Field definitions: whatever the CRM default happens to be, versus standardized once across every connected tool - Duplicate accounts across systems: treated as separate records, versus resolved into a single identity - Plan targets and benchmarks: not visible to the AI at all, versus held as a fixed reference point - Signals outside the CRM (calls, billing, Slack): invisible unless manually cross-checked, versus reconciled and sequenced with CRM data - Tested accuracy on common RevOps questions: 52%, versus 98% ## Why Power BI doesn't solve this either Power BI's honest tradeoff is hours, not accuracy. Cross-referencing HubSpot, Google Analytics, and call data through Power BI usually means manual joins, a data engineer's time, and a dashboard that goes stale the moment a field name changes upstream. It gets a report. It doesn't get an agent that can look at a stalled deal, cross-reference the call transcript, notice the champion left the account three weeks ago, and write that finding back to the CRM on its own. That's a reasoning problem, and it's a different problem than the one Power BI was built to solve. ## A governed layer closes the gap Reducing hallucination risk means controlling what the AI is allowed to treat as true before it starts reasoning, not swapping in a smarter model. A governed data layer standardizes definitions across every connected tool, resolves duplicate identities, and holds targets and benchmarks as a fixed reference instead of letting each system guess at its own version. Claude or Copilot still does the reasoning. It just does it on a foundation that agrees with itself. Connect an AI tool straight to a CRM and the answers come fast. Connect it to a foundation where the data has already been reconciled first, and the answers hold up without a second person checking the AI's work. ## See the difference on real CRM data [Book a 20-minute walkthrough](https://vasco.app/request-demo) and bring a real question a team has asked Claude or Copilot before. We'll show what changes when the same question runs through a reconciled data layer instead of a raw CRM connection. ## FAQ ### Is it safe to connect Claude directly to HubSpot or Salesforce? Connecting is technically safe, but the real risk sits in interpretation, not access. Claude will read whatever the CRM fields say without knowing whether those fields reflect current reality, so answers can sound confident and still be wrong. ### Does MCP clean or reconcile CRM data before Claude sees it? No. MCP is a connector. It gives Claude access to systems like HubSpot, Gong, Stripe, and Slack, but it doesn't deduplicate accounts, standardize field definitions, or sequence events across tools. That reconciliation has to happen in a separate layer. ### Why does AI accuracy drop when connected directly to a CRM? Because CRM fields carry no shared meaning across teams or tools. A term like "qualified" or "active" can mean different things depending on who last touched the record. An AI reasoning on raw fields treats every value as literal, which produces confidently wrong answers. ### Is this a known problem, or specific to one company's setup? It's broad and well documented. MIT's Project NANDA found that about 95% of enterprise generative AI pilots fail to deliver measurable returns. McKinsey found that eight in ten enterprises cite data limitations as the main roadblock to scaling AI agents. This is an industry-wide pattern, not an edge case. ### Is Power BI a substitute for a context layer? No. Power BI is built for reporting and requires manual setup to join data across sources. A context layer is built for reasoning: it reconciles identities and definitions once, so any AI agent connected afterward works from a consistent foundation. ### What's the actual accuracy difference between raw CRM access and a governed data layer? In a controlled test across five common RevOps scenarios, Claude with raw CRM access answered correctly 52% of the time. The same questions through a reconciled data layer reached 98% accuracy. ### What does a Claude MCP CRM connection actually give an AI access to? It gives Claude read access to CRM records, exactly as reps entered them, including gaps, mislabels, and stale fields. MCP moves data from the CRM to Claude. It does not clean, standardize, or verify that data along the way. ### How can a revenue team improve AI revenue data accuracy without switching tools? Accuracy improves by fixing the layer underneath the model rather than the model itself: reconcile duplicate account records, write down shared field definitions, and load plan targets so the AI has something to measure performance against. Claude or Copilot can stay the same. The foundation underneath changes. ### Should a company wait until its CRM data is perfectly clean before using Claude or Copilot? No. Low-stakes work like drafting emails or summarizing notes carries little risk from imperfect data. The trust boundary matters most for pipeline reviews, forecasting, and board reporting, where a confidently wrong number gets treated as a real one. Those use cases are where a governed data layer earns its cost first. ### What's the difference between giving Claude a skill and giving Claude a governed data layer? A skill is a set of written instructions that tells Claude how to go query raw systems and reconcile the results itself, in the moment. A governed data layer resolves identities, definitions, and targets ahead of time and returns a single checked answer. Both connect Claude to data. Only one hands over an answer that's already been verified. --- --- title: "Vasco's context graph is now available on Google Cloud Marketplace " description: "Customers can now buy Vasco through a channel they already use, applying existing Google Cloud commitments instead of finding a new budget." canonical: "https://vasco.app/blog/vasco-google-cloud-marketplace" date: "2026-07-27T20:25:00.000Z" authors: - Guillaume Jacquet jobTitle: "CEO & co-founder" readingTimeMinutes: 3 contentType: article intent: strategy-insights pillar: "AI in GTM & RevOps" audiences: - revops - cros - founders - fractional - start-ups - scale-ups - enterprise --- # Vasco's context graph is now available on Google Cloud Marketplace _Every revenue team gets an AI teammate grounded in real revenue data_ Following Vasco's completion of the Google Cloud ISV Startup Springboard program, customers can now [buy Vasco through Google Cloud](https://console.cloud.google.com/marketplace/product/vascohq-marketplace/vasco) commitments instead of finding a new budget. It follows Vasco's completion of the Google Cloud ISV Startup Springboard program. Here's what Vasco actually does and why it's built this way. [Vasco's context graph is now available on Google Cloud Marketplace ](https://www.youtube.com/watch?v=RJE2aB6jWy8) Vasco connects CRM, ERP, billing, call recordings, support, and product analytics into a single bowtie-structured context layer. It's the foundation every module, and every Claude session, runs on. [Powered by Claude](https://vasco.app/blog/claude-cowork-revenue-data-layer), Vasco assembles that data into one [context graph](https://vasco.app/blog/context-graphs-for-gtm-teams) on top of a company's existing stack, so AI agents can tell revenue teams what's happening, why, and what to do next, without hallucinating on the numbers that matter. Vasco wires it and runs it. Customers then connect their own agents over MCP, use Vasco's ready-to-deploy agents, or design new ones from scratch. AI agents fail when the data underneath them is wrong in a way that looks exactly like being right. That's the problem Vasco solves, and Google Cloud is a partner in solving it. > “Through the Google Cloud ISV Startup Springboard program, we aim to empower promising companies with Google Cloud’s impactful programs, products, and expertise," said Ritika Suri, Managing Director, AI and Data Partnerships at Google Cloud. "We’re enabling the selected companies to leverage Google Cloud’s advanced AI technologies - and in the case of Vasco, to empower enterprises with AI to help plan and execute their go-to-market strategies.” ## What's different about buying Vasco through Google Cloud Marketplace - **Faster procurement **through a channel your team already trusts - **Apply committed Google Cloud spend **instead of creating net-new budget - **Private Offers **for custom pricing and commercial terms on complex or partner-led deals - **Standardized commercial and security terms**, so deployment doesn't stall in legal review ## Inside the context graph Every metric, signal, transcript, and contact gets reconciled and connected through Vasco's Context Graph, so agents can reason about why a number moved, not just what it is. Four capabilities make that possible: without them, an LLM gives confidently wrong answers, but with them, Vasco holds 99.5% accuracy. - **Context graph**: identity-resolved relationships across CRM, ERP, calls, tickets, and product usage - **Consistent definitions**: metric and stage definitions enforced once and computed the same way every time - **Bowtie Data Model**: full lifecycle visibility from attract through expand, so agents see the whole motion - **Plan**: targets, forecasts, and goals, so agents reason against what good looks like for each business Work flows three ways on top of that foundation: agents surface what matters when a human needs it, agents run on a recurring cadence in place of old-style automation, and increasingly, agents hand off work to each other, where outcomes compound. Every action stays reviewable. Agents propose, people approve, writes get confirmed before execution, and every action lands in an audit trail. The platform holds SOC 2 and GDPR compliance. By the numbers: - **7x **more context per AI query versus raw CRM access alone - **100% **API-level accuracy on every metric query, the same answer every time - **Days**, not 6-8 months of engineering, to a production-ready Context Graph ## A teammate for every role Vasco ships with [ready-to-deploy agents](https://vasco.app/blog/ai-revenue-agents-gtm-use-cases) pre-trained on a company's own context graph, live in minutes. That includes a CRO/CEO Brief for weekly revenue reporting, a Board & Investor agent for board prep and investor narratives, an ICP & TAM Discovery agent, a Pipeline Reviewer, a Churn Detective, a Marketing Analyst, and a Data Medic that audits CRM data and proposes fixes. More than 20 additional agents are [available in the marketplace](https://vasco.app/agents/marketplace). Teams whose role doesn't exist yet can describe it in plain language and have Vasco generate the agent spec, then review, tune, and deploy it themselves. Or they can work with Vasco's Forward-Deployed RevOps team to go from spec to a live agent, running on the company's own context graph, in days. Modeled on research from Bridge Group, Pavilion, and RevOps Co-op, a $20-50M revenue company sees rep multiplier move from 4-5x to 7-10x, manager-to-IC ratios stretch from 1:5 to 1:10, RevOps strategic time rise from 30% to 70%, and CRO decision time compress from days to minutes. Modeled annual impact: $1.2M-$3.4M. ## Two ways to build Customers deploy Vasco's ready-to-deploy agents with no setup and no prompting required, or connect Claude, Gemini, or any other tool directly to Vasco's Context Graph via MCP and build their own. Either way, the workflow is the same: connect your tools, create the agents you need, deploy them into the flow of work. Agencies, fractionals, and consultants can run client work on Vasco itself, using one context graph per client inside a multi-tenant workspace, compressing weeks of audit work into days. Vasco connects to 750+ tools, including Salesforce, HubSpot, Snowflake, BigQuery, Gong, Stripe, NetSuite, Zendesk, Slack, and Notion, unifying all of it into one GTM context graph. ## Get started Vasco is available now on Google Cloud Marketplace. Google Cloud customers can apply committed spend toward Vasco, and deals that need custom terms can use Private Offers. [Explore Vasco on Google Cloud Marketplace ](https://console.cloud.google.com/marketplace/product/vascohq-marketplace/vasco) --- --- title: "New: Train agents on your revenue motion" description: The agents you hired last cycle just got a lot more capable. canonical: "https://vasco.app/blog/product-spotlight-July-2026" date: "2026-07-14T00:00:00.000Z" authors: - Guillaume Jacquet jobTitle: "CEO & co-founder" readingTimeMinutes: 1 contentType: product-spotlight intent: foundations pillar: "RevOps systems & architecture" audiences: - enterprise --- # New: Train agents on your revenue motion _New: Train agents on your revenue motion_ The agents you’ve hired in the [agent marketplace](https://vasco.app/blog/product-spotlight-June-2026) were a strong starting point. Capable, grounded in your data, ready to run. What they were missing was context: your pipeline stages, your ICP criteria, your team's definition of at-risk. That's the gap Skills closes. Every agent you run now carries your standards, your process, your institutional knowledge. The way your best analyst runs a pipeline review becomes the way every agent runs it, every time, without anyone in the room. [New: Train agents on your revenue motion](https://youtu.be/XadWRADfZuw) ## 1. Skills: your process, built into the agent A skill is a reusable set of instructions that tells an agent exactly how to behave in a given context. Think of it as onboarding documentation, except instead of sitting in a folder nobody reads, it runs every time the agent does. ![](https://cdn.sanity.io/images/ys8gstp8/production/03f1a000e74f5a63e9eabea26ea9a3bb173a98ff-1048x1036.png?w=1600&fit=max&auto=format) Where agents used to come with fixed, hard-coded behaviors, skills make them configurable. A pipeline review agent now runs your pipeline review: your stages, your definitions, your criteria for what counts as at-risk. A WBR agent runs the business review the way your team runs it, not a generic approximation. Vasco comes with 35 built-in skills out of the box. ICP analysis, GTM audits, pipeline reviews, churn signals, rep coaching: each one built around RevOps best practices and ready to assign to any agent in your hub. Teams can also build their own skills from scratch and promote them org-wide, so the way one great analyst runs a review becomes the way every agent runs it. Skills are invokable via slash command directly in chat. They travel with the agent, and agents from the marketplace now come with their associated skills pre-loaded, so there's nothing to configure when you get started. The result: agents that know your playbook before they run their first task. ## 2. Tools: from producing outputs to taking action Skills tell an agent how to think. Tools tell it what it can do. Until now, agents were read-only. They reasoned on your data, produced an output, and handed it back. Someone still had to act on what came back: a rep updating the CRM, a manager logging the call, a RevOps analyst chasing down the follow-up. The agent observed. You executed. Tools change that. **HubSpot write-back is live.** Connect HubSpot and your agent can now write deal records, update fields, and log activity directly in your CRM, with no one touching it manually. A rep finishes a call, the agent captures what happened, and the CRM reflects it. Records stay current, and the follow-up Slack asking someone to update their opportunities disappears entirely. ![](https://cdn.sanity.io/images/ys8gstp8/production/d881d98aedd133b2ddc63688ec8e2e339636c57c-3201x1356.png?w=1600&fit=max&auto=format) Beyond HubSpot, agents can now query integrations live via API, including Zoho, Amplitude, Mixpanel, and Snowflake, without waiting for data to replicate into the warehouse first. That means faster answers and a data architect who can investigate mapping issues or data gaps in real time, not three days later. Anything you used to do in Vasco by clicking through the interface, an agent can now do on your behalf. That's the shift from AI that observes your revenue motion to AI that participates in it. ## 3. Governance built in, not bolted on Every tool is opt-in at the agent level. Admins control which agents can write to which systems, and every action requires explicit permission before it runs. This matters because ungoverned AI action in a CRM is a real risk. A misconfigured agent that overwrites deal data or sends a message to the wrong Slack channel is not a theoretical concern. It's the kind of thing that erodes trust in the whole system fast. The answer isn't to limit what agents can do. It's to make sure every action is traceable, configurable, and reversible. Admins see what each agent has permission to touch. Every write requires an explicit scope. Nothing happens in your systems without your team having said it can. AI that acts without accountability is a liability. AI that acts within a governed, auditable framework is an asset. ## 4. Self-serve billing and usage transparency Teams can now subscribe directly, upgrade their AI credit package, and set usage alerts at 70%, 90%, or 100% of their monthly limit. No sales call required. There's a hard stop option for organizations that need predictable spend, and a usage dashboard that breaks down credit consumption by agent and by user so you always know where the budget is going. The free tier replaces the old trial-expiration wall. Instead of hitting a locked door when the trial ends, teams land somewhere functional and upgrade when they're ready. For organizations that want to expand, the upgrade path is self-serve too: calculate the prorated charge, pick a new package, and move forward without waiting on anyone. ## 5. Up next: custom metrics and templates The next product spotlight will cover custom metrics, the first step toward letting teams define and calculate their own GTM measures on top of Vasco's native data. Describe the metric you want in chat, and the data architect agent builds it, previews it against your actual data, and publishes it into your metric library. No SQL required. More on that soon. ## Other cool upgrades - **Homepage redesign.** New report template cards show exactly what each agent can produce before you install it. Preview an example output on real data, click generate, and your first report is running in one step. Time to first insight is now measured in minutes, not weeks. - **Report design refresh.** Artifacts are sharper, bigger, and exportable as HTML for sharing outside Vasco. Charts, metric cards, and commentary all render at a quality worth putting in front of a board. - **15 new connectors.** Call recorders, billing tools, CRMs, and data warehouses are all available now as live API, raw data, or write-back integrations. Every major call recorder flows into Vasco. Attio is live as a supported CRM. - **Google Marketplace certification.** Vasco is now part of Google's Springboard program, the approval program for advanced AI startups, with a rigorous qualification process. For enterprise buyers, it's a meaningful credibility signal. --- --- title: "Revenue data provenance: A forecast accuracy guide for CROs" description: "Enterprise AI leaders are already calling data provenance the trust layer for agentic AI. Here's what revenue data provenance means for CROs, and how to build it into forecast accuracy before agents start acting on your pipeline." canonical: "https://vasco.app/blog/data-provenance-forecast-accuracy" date: "2026-07-06T20:25:00.000Z" authors: - Guillaume Jacquet jobTitle: "CEO & co-founder" readingTimeMinutes: 5 contentType: article intent: strategy-insights pillar: "AI in GTM & RevOps" audiences: - revops - cros - founders - fractional - start-ups - scale-ups - enterprise --- # Revenue data provenance: A forecast accuracy guide for CROs _What every CRO needs before an agent starts reporting the forecast_ The real shift AI has brought to forecasting is hallucination. There was an old failure mode where a forecast nobody quite trusted was patched over every Monday with a round of guessing about why a deal moved. The new failure mode is a forecast everybody trusts by default, because it came out of a system, and systems sound authoritative even when they're wrong. Both failures trace back to the same root cause: nothing in the data distinguishes a number grounded in a real signal from a number that's just confident. Fixing that is a governance problem, not a smarter-model problem, and it's the gap this guide addresses. ## What is provenance? Provenance is the documented history of where something came from and what happened to it along the way. The word comes from art and antiques: a painting's provenance is the chain of ownership and custody that proves it's genuine and not a forgery someone assembled to look convincing. Apply that same idea to data, and provenance means the record of where a piece of information originated, who touched it, what changed, and when, kept well enough that anyone can trace a current number back to its source instead of just trusting that it's right. ![](https://cdn.sanity.io/images/ys8gstp8/production/25ee668008e990445aa45865d9b9ce54d695d900-1200x630.png?w=1600&fit=max&auto=format) The reason this matters more now than it did five years ago is straightforward: a forgery only fools someone if nobody checks the paperwork. The same is true of a data point. As long as a person is the one reading a report and deciding whether to trust it, a gap in the paper trail is a nuisance. Once a system starts acting on that number without a person in the loop first, the gap becomes the whole risk. ## Data provenance is no longer a back-office conversation Enterprise AI leaders outside of RevOps are already having this conversation, and it's worth borrowing their vocabulary. A [recent Forbes Technology Council piece](https://www.forbes.com/councils/forbestechcouncil/2026/06/10/data-provenance-the-trust-layer-for-agentic-ai/) argues that as AI moves from generating answers to taking action, the important question stops being what the model can do and becomes what data made the system act. The author calls data provenance the trust layer for agentic AI: the record of where data came from, who changed it, and which decision it influenced. He points to Gartner's projection that agentic AI will sit inside a third of enterprise software by 2028, up from under 1% in 2024, and to IBM research finding that most breached organizations studied had no AI governance policy in place at all. Revenue data provenance is that same idea applied to the system every CRO already depends on. A pipeline, a forecast category, a rolled-up ARR number: each one is a decision an agent might soon be making or influencing directly. If the enterprise-wide argument holds, and the evidence above suggests it does, then a forecast is exactly the kind of decision that needs its data history on record before an agent starts acting on it, not after. ## What actually builds trust in a forecast? A forecast has revenue data provenance when every number in it can be traced back to where it came from: which data sources fed it, which agent or person touched it, and what it was checked against before it reached a report. In practice, that's the answer to "who changed what, when, and why" for every figure that lands in front of a board. A forecast with that record intact can be walked backward, category change by category change, to source. A forecast without it is a confident guess with good formatting, and no algorithm sitting on top of ungoverned data fixes that. Governance earns the trust back. Better math doesn't. ## Why AI forecasts fail an audit today Most revenue stacks were never built to answer "where did this number come from." A handful of gaps show up on repeat. ### Forecast categories were never defined as falsifiable conditions. "Commit" should mean a specific, checkable state has been reached: a signed verbal agreement with procurement in legal review, for example, not "I feel good about it." "Best Case" should mean a named next step with a date attached, not a vibe. If a deal can move category without meeting a stated condition, the category is decoration, and every roll-up built on top of it inherits that ambiguity. ### The CRM stopped reflecting reality. CRMs track deal stages, not the consumption and billing events that now drive ACV in a usage-based world. The data that explains a customer's real state increasingly lives in billing and product systems that never talk to the CRM the way RevOps needs, which is why more operators pull straight from the warehouse where those signals actually converge. This is a pipeline data quality problem before it's ever an AI problem: an agent reasoning over stale or fragmented data will produce a confident forecast that's wrong in exactly the same way a human's would be, just faster. ### Category changes happen with no reason attached. A deal moves from Commit to Best Case, and the only record is the new value. The old value, who changed it, and why are gone the moment the field is overwritten. Without that trail, a category change grounded in a competitor win looks identical to a category change grounded in a rep protecting their number. ### Verification happens by hand, quietly. Teams add a manual check on every AI-generated output because nobody fully trusts it went in clean. That's a symptom. The audit trail lives in someone's head instead of in the system, and it disappears the day that person is out before the board meeting. ## Signs your forecast has no audit trail - A category change gets a guess in a pipeline review, not a documented reason. - Nobody can say what share of open pipeline changed category in the last seven days, or why. - Finance overrides the field's number so often, or so rarely, that nobody can tell if it's adding judgment or just rubber-stamping. - A metric discrepancy sat in the board deck for two quarters before anyone caught it. - The pipeline review spends most of its time reconstructing what happened last week instead of deciding what to do about it. If two or more of these are true, the forecast is not auditable. It just looks finished. ## The mechanisms that make a forecast defensible ![](https://cdn.sanity.io/images/ys8gstp8/production/3a8b5fe90bbf06d22f802c68fa8280393e5ec5f3-1200x630.png?w=1600&fit=max&auto=format) Revenue data provenance comes from a small set of concrete mechanisms, not a principle, and each one closes a specific hole. ### A reason required on every category change, before the system lets it save. When a rep moves a deal from Commit to Best Case, they pick a reason from a short list (competitor selected, budget delayed, champion left) before the change goes through. No reason, no save. This cannot live as a rule people are supposed to remember. Rules get followed until quarter-end pressure hits, then they don't. A change that simply won't save without a reason gets one every time, from every rep, without anyone having to police it. ### A permanent record of every category change, kept separate from the deal itself. Old value, new value, who made the change, when, and why, written somewhere nobody can quietly edit or delete later, including admins. If something needs correcting, a new entry gets added, not a rewrite of the old one. This is the difference between a history you dig for after something goes wrong and a history the team actually reads every Monday. ### A daily snapshot of the entire pipeline, filed away by date. What every open deal looked like, category, amount, close date, owner, saved once a day. The change record tells you what moved and why. The snapshot lets anyone pull up exactly what the pipeline looked like on any past date, so a board member's "why did this move so much since last month" question gets answered by pulling a report, not by reconstructing a memory. ### Finance's adjustments shown alongside the field's number, not baked into it. When finance revises a rep's or a manager's rolled-up figure, that adjustment gets recorded on its own, tied to a specific quarter, with a reason and a name attached. The original number underneath doesn't get overwritten. A report can then show both, the number the field reported and the number finance settled on, so anyone can see exactly where a human judgment call was applied versus where the number is a straight calculation. ### One customer record, not three that quietly disagree. The same account should look identical whether you're reading the CRM, the billing system, or the product usage dashboard. Without this, a churned customer can still show up as "active" in one tool while billing has already stopped charging them, and a forecast built on top of that mismatch is wrong before anyone even looks at pipeline. This is usually the least visible gap on this list, because each system individually looks fine. It only shows up when someone tries to reconcile them and the totals don't match. Miss any of these and the forecast has a hole an auditor, or a skeptical board member, will find. ## Governance: who owns the audit trail? Mechanisms only hold up if someone owns running them. A governance model for AI-assisted forecasting needs the following in place before agents start reading and writing pipeline data at scale. > "Governance sounds like a compliance function until the board asks why a number moved and nobody in the room can answer. Then it's the only thing that matters. We didn't build reason codes and change history because it's good practice. We built it because 'the AI said so' isn't going to hold up to a CFO." — Guillaume Jacquet, CEO of Vasco ### A single definitions registry, with one accountable owner. Every forecast category, and every metric derived from it, gets one falsifiable definition, stored in one place, that every tool and every agent reads from. RevOps owns changes to it. No team maintains its own local version, and no agent is allowed to infer a definition that isn't written down. ### A human check before a pattern becomes an automated rule. Say an agent notices that deals with a security review scheduled early close 30% more often. Before that observation gets turned into an automatic playbook step, someone on RevOps checks whether it holds up, or whether it's five deals out of a hundred that happened to line up that way. Skip this check and agents start automating on coincidences, then reporting those coincidences to the board with full confidence. ### A recalibration cadence on a fixed calendar. Markets shift and definitions age. Review fast-moving metrics like category flip rate monthly. Review structural definitions like what counts as Commit quarterly, unless a market shift forces an earlier look. When a previously reliable pattern weakens, the review should catch it before a playbook keeps running on a stale assumption. ### Escalation thresholds, written down before a miss happens. Decide in advance what forces a manual review: more than a set share of current-quarter pipeline flagged as slip risk, a deal that has moved category twice with no resolution, or any top-five deal by ARR with no scheduled next step. Thresholds set after a bad quarter are damage control. Thresholds set in advance are governance. ## Instrumentation: the numbers that show whether this is working Five figures tell you whether these mechanisms are actually in place or just documented on paper. 1. **Category flip rate.** The share of open pipeline that changed category in the trailing seven days. A healthy pipeline moves. Deals should shift category as facts change, and the number to watch is spikes right before quarter close, which usually signal last-minute cleanup rather than real movement. 2. **Reason-code compliance.** The share of category changes carrying a specific reason rather than a catch-all or blank field. Below roughly 90%, the reason list or the enforcement point needs a second look. 3. **Override rate and direction.** How often finance adjusts the field's number, and which way. A number that never moves means finance isn't adding judgment. A number that moves constantly in one direction points to a structural disagreement between the field's incentives and finance's caution. 4. **Forecast-to-actual variance.** For closed deals, how far the category assigned thirty and sixty days out diverged from the actual outcome, broken out by rep and manager. This is the only honest measure of forecast accuracy once these mechanisms are in place. 5. **Review meeting duration.** A blunt but telling signal. A pipeline review that used to run the better part of two hours and now runs thirty minutes has stopped re-litigating totals and started doing forecast inspection: working a short list of unexplained changes instead of reconstructing the whole quarter from memory, which is the actual point of the exercise. ## Why this matters more once agents are in the loop The confidence problem described up top is measurable, and the numbers are stark. Agents connected directly to raw CRM data with no governance layer underneath answer basic revenue questions correctly around half the time. Add a reconciled data layer, definitions, source tracing, and change history purpose-built for agents to reason on, and accuracy on the same questions climbs above 95%. While the model itself stays the same, accuracy swings based on whether the data underneath was built for a system that acts on it at face value, not a person who might double check. ![](https://cdn.sanity.io/images/ys8gstp8/production/7eaff82e10bacc420a4be610fc57ff108c373062-1200x630.png?w=1600&fit=max&auto=format) That's the case for treating the revenue data layer as its own piece of infrastructure, not an afterthought bolted onto the CRM. In practice, that means a revenue context layer sitting between the raw systems and the agent: one place where identity is resolved, definitions are fixed, category changes carry a reason, and every number can be traced back to where it came from. The agent still does the reasoning. The context layer is what keeps that reasoning honest. Once an agent is reading pipeline data and writing forecast updates, every mechanism in this guide, required reasons, permanent change history, daily snapshots, reconciled identity, stops being a nice-to-have audit trail and becomes the thing standing between a board getting a real number and a board getting a confidently wrong one. ## A revenue data provenance checklist for CROs Before the next board review, walk through each of these: - Can any reported number be traced to its source system in under two minutes? - Does every category change require a reason before it saves, rather than relying on reps to remember? - Can the team pull up exactly what the pipeline looked like on any past date, not just today? - Are finance's adjustments visible next to the raw number, rather than written over it? - Are escalation thresholds for slip risk and stalled pipeline written down and reviewed on a fixed cadence? - Would a new hire, or a new agent, get the same answer to "what does Commit mean" no matter which tool they asked? A no on more than one of these means the forecast is running on trust the team hasn't actually earned yet. ## Fix the trail, not the model A required reason on every change, a permanent record of who touched what, a daily snapshot to reconstruct any past state, one reconciled identity behind every account: four habits, not a better model. Together they're what revenue data provenance actually looks like in practice, and they turn a number nobody can defend into one that traces cleanly back to source. The stakes are higher now because agents read and write that data faster than any human can keep up with, and a confident wrong summary doesn't invite the scrutiny a rep's guess would. Start with one change: require a reason before any forecast category can be updated. Everything else here builds on that. Get it in place, and the next time a board member asks why a number moved, the answer is a report, not a reconstruction. ## FAQ ### Why does my sales forecast keep changing? Usually because deals move category without a documented reason attached. When nobody can tell a change driven by a real customer signal from a change driven by a rep managing their own visibility, the forecast will keep shifting in ways nobody can explain, and forecast trust erodes with every unexplained swing. ### How do you improve forecast accuracy? Start with the data feeding the forecast, not the forecasting method sitting on top of it. Require a reason on every category change, keep a permanent record of who changed what and when, and reconcile customer identity across CRM, billing, and product systems. Better models built on top of ungoverned data still produce ungoverned forecasts. ### What is a reason code in sales forecasting? A reason code is a required, specific explanation attached to a forecast category change, chosen from a short list (competitor selected, budget delayed, champion left) instead of left blank or filled in with a vague note. Reason codes make it possible to later check which explanations actually predicted revenue and which were just cover. ### How do you audit a sales forecast? Trace every reported number back to source, check whether category changes carry a documented reason, and compare what a deal's forecast category looked like thirty or sixty days before close against how it actually closed. If any of those checks come up empty, the forecast isn't auditable yet. ### Why do AI-generated forecasts need more governance than manual ones? Agents pull from more sources, faster, and can apply a bad definition or an unreviewed pattern across every report they touch before a human notices. Governance catches that before it reaches the board. ### Why does a category change need a required reason, and why enforce it in the system instead of asking reps to log it? Because a rule people are supposed to follow gets skipped the moment there's deadline pressure. A change that won't save without a reason gets one every time, from every rep, without anyone having to check. ### How accurate are AI agents when they read directly from a CRM? Roughly half the time, on basic revenue questions, when they're connected straight to raw CRM data with no reconciliation or governance underneath. Layer in a data foundation built for agents to reason on, with definitions, source tracing, and change history included, and accuracy on the same questions climbs above 95%. The data underneath the model, not the model itself, drives that gap. --- --- title: Why using GitHub as a context repo makes your AI agents less accurate description: "GitHub is great for documentation, but static docs can't keep pace with a live revenue motion. Here's why it isn't enough for production AI agents." canonical: "https://vasco.app/blog/github-agents-revenue-context-accuracy" date: "2026-07-06T20:25:00.000Z" authors: - Guillaume Jacquet jobTitle: "CEO & co-founder" readingTimeMinutes: 5 contentType: article intent: strategy-insights pillar: "AI in GTM & RevOps" audiences: - revops - cros - founders - fractional - start-ups - scale-ups - enterprise --- # Why using GitHub as a context repo makes your AI agents less accurate _Why most revenue teams are sitting on their best GTM data and doing nothing with it_ There's a phrase making the rounds in RevOps right now: _garbage in, confidently wrong out._ A lot of teams are trying to solve this the obvious way. They’re writing down what they knew and dropping it into GitHub. This is a structured, version-controlled, reasonable first move. **The problem is that documentation ages the moment it's committed, and AI agents can't tell the difference between current context and context that's six months old. **They reason from whatever you give them, and confidently so. One team found this out the hard way when their agent's pipeline summary was off by 46 percentage points. Let’s dig into it. ## Why GitHub is missing context When teams realize their AI agent needs business context to work properly, the first instinct is usually to put it somewhere structured: ✅ Write down the ICP definition ✅ Document the pipeline stages ❌ Drop the RevOps runbook into a repository GitHub feels like the obvious home for this. It's version-controlled, accessible, and already part of the toolchain. Most technical teams are already living in it. So you write down what a qualified lead means, document your lifecycle stages, create a prompt library, point your agent at it, and call it done. ### GitHub solves for organization, not accuracy What your agent is reasoning from isn't context. It's a record of what someone understood about the business at the time they wrote it down. The moment it's committed, it starts going stale. Nobody opens a pull request when a deal slips, a segment shifts, or the ICP evolves. The agent has no way to know any of that happened. What you actually get with a GitHub context repo: - Static documents with no connection to live CRM data. Your pipeline and revenue motion have moved on without them. - Freshness that depends entirely on humans. Nobody updates the wiki when a deal slips or a segment changes. - Relationships between accounts, contacts, and deals buried in prose, not queryable by an AI agent. - Zero awareness of what's actually happening in your GTM motion right now. There's a tell for whether this is happening on your team. Someone has quietly started checking the AI's work before it goes anywhere important. The output looks clean, the summary reads well, but nobody fully trusts it, so a human verifies before the board deck, before the pipeline review, before the forecast goes to the CRO. That manual step is the gap showing up in your workflow. > AI doesn't fix a weak foundation. It amplifies whatever you feed it. ## What actually makes an AI agent accurate The thing separating a useful AI agent from a confidently wrong one isn't the model. It's what the model is reasoning from. A GitHub wiki can't give an agent shared definitions encoded somewhere it can actually read, rather than sitting in last year's planning deck. It can't tell the agent that the same account is showing up as three different entities across HubSpot, Gong, and Stripe. It can't tell the agent what "on track" means relative to your actual number, as opposed to what on track looked like last quarter. ### Input a live data layer ![](https://cdn.sanity.io/images/ys8gstp8/production/322e92f5ffb58737f55ddab1bb04957972a3f2a7-1200x630.png?w=1600&fit=max&auto=format) A live semantic data layer does all of that. Instead of storing documents, it builds a structured, queryable representation of your revenue data sitting directly over your stack, updating in real time. Vasco does this for HubSpot, Gong, Stripe, and the rest. The agent isn't interpreting prose. It's querying a live graph of relationships between accounts, contacts, deals, pipeline stages, and revenue events. **GitHub: DIY context repo** - Static snapshots of past understanding - Unstructured prose, no queryable relationships - Freshness that depends on humans remembering to update it - No causal tracing. You see that revenue moved, not why. **Vasco: live semantic layer** - Reflects what's actually in your CRM and pipeline today - Structured relationships between accounts, contacts, deals, and events - Causally linked. Trace why revenue moved, not just that it did. - Clean, semantically mapped data that cuts hallucinated outputs. One SaaS company ran the same pipeline review question against a markdown-based context store, then against Vasco's context graph. Accuracy went from 52% to 98%. The model was the same. The context was different. **** ![](https://cdn.sanity.io/images/ys8gstp8/production/56832a37a03a5fa013cbeb9e287d60486c08f5b3-1200x630.png?w=1600&fit=max&auto=format) ## The four things that make context agent-ready Four properties separate a layer agents can actually reason from versus one that produces fluent-sounding noise. ![](https://cdn.sanity.io/images/ys8gstp8/production/86ad1663a243dea3800c129cf118897294a39b3e-1200x630.png?w=1600&fit=max&auto=format) ### Live, not static Your ICP from last quarter's planning session isn't the same as the segments closing today. Context has to reflect what's in the system of record right now, not what someone wrote down when they had a free afternoon. ### Structured, not buried in prose When an agent reads through text to understand that Account A is in Enterprise, attached to two open opportunities, with a champion who churned six months ago, it introduces interpretation error at every step. Structured, queryable data removes that chain of guesswork. ### Causally linked Knowing that revenue declined doesn't help anyone act. Knowing which motion broke, which segment slipped, which attribution point went dark: that's what an agent can actually work with. Documents describe outcomes. A semantic layer traces causes. ### Semantically mapped If your agent doesn't know what "qualified opportunity" means in your specific motion, it'll invent a definition. That definition will be wrong. Business rules, lifecycle definitions, and attribution logic need to be encoded in the layer itself, not expected to materialize from reasoning over raw CRM data. None of this lives in a Git repository. ## So where does GitHub actually belong? GitHub is genuinely good at what it was built for: engineering runbooks, prompt templates, internal docs, onboarding guides. When the domain doesn't change with every closed deal or slipped forecast, static files are fine. GTM isn't that domain. Pipeline changes constantly. Revenue motion shifts week to week. The ICP that was accurate in January might not describe your best customers today. Any context layer built for AI reasoning in a commercial environment has to track those changes automatically, not wait for a human to open a pull request. The objection that comes up a lot is: "We have this documented in GitHub, so we're covered." Documentation and a data layer solve different problems. One tells your agent what the business looked like. The other tells it what the business looks like right now. A prototype built on the first can look impressive and demo well. A production agent needs the second. ## Tired of second-guessing your AI's pipeline numbers? Vasco connects to your existing stack, HubSpot, Gong, Stripe, and builds a live context graph your AI agents can reason from accurately. Your current setup stays intact. Run a free diagnostic in minutes and see exactly where your data is breaking before your agents do. Forecast accuracy went from 52% to 98% for one team. Same model, better foundation. [Run a free diagnostic >](https://my.vasco.app/sign-in) ## FAQ ### Can I use Claude with my GitHub-stored context for RevOps? You can, but the accuracy ceiling is low. Claude reasons from whatever you give it. Stale markdown produces stale answers, delivered confidently. Connecting to a live semantic layer over your CRM is what actually raises that ceiling. ### What's the difference between a vector database and a semantic data layer? A vector database retrieves unstructured text based on similarity. A semantic data layer stores structured relationships between revenue entities, accounts, contacts, deals, and events, with your business logic encoded. For GTM agents, the semantic layer is what determines whether the agent understands your pipeline or just finds text that mentions it. ### How does Vasco connect to HubSpot and Stripe? Through MCP (Model Context Protocol) integrations. Vasco reads live data, applies identity resolution and outcome tagging, and exposes a queryable context graph to AI agents. No manual exports, no scheduled syncs. --- --- title: "Call transcripts for AI agents: How to turn sales conversations into accurate GTM intelligence" description: "Every answer your revenue team needs is already in your call recordings. Here's how to actually use it." canonical: "https://vasco.app/blog/call-transcripts-AI-agents" date: "2026-06-16T20:25:00.000Z" authors: - Justin Hudon jobTitle: Head of Sales and Customer Success readingTimeMinutes: 5 contentType: article intent: strategy-insights pillar: "AI in GTM & RevOps" audiences: - revops - cros - founders - fractional - start-ups - scale-ups - enterprise --- # Call transcripts for AI agents: How to turn sales conversations into accurate GTM intelligence _Why most revenue teams are sitting on their best GTM data and doing nothing with it_ Call transcripts become accurate GTM intelligence when each conversation is classified by its position in the customer journey separating acquisition calls from post-sale ones, discovery from close, onboarding from support. Without that classification, AI agents blend all call types together and produce confident, wrong answers on your most important revenue questions. With it? Your recordings become a live, [queryable intelligence layer](https://vasco.app/builders) that explains why metrics move, identifies expansion signals early, and tells you what your buyers actually said. Your team records hundreds of calls a month. Discovery calls, demos, QBRs, onboarding sessions, renewal conversations. All of it gets saved somewhere. Most of it never gets used. Not because nobody cares. Because nobody has built the structure to make it useful. That's what this piece is about. Not just what's possible when you unlock call transcript intelligence for AI agents, but what's actually sitting in your recordings right now, waiting to be surfaced. ## What is call transcript intelligence for AI agents? **Call transcript intelligence** is the practice of structuring recorded sales and customer conversations so that AI agents can query them accurately by journey stage, motion type, and outcome. Most teams think of call transcripts as a documentation tool — something stored in Gong or Fathom for coaching reviews and compliance. Call transcript intelligence treats them as a primary GTM data source: the only objective record of what buyers actually said, what drove decisions, and what signals predict future revenue outcomes. When call transcript intelligence is built correctly, an AI agent can answer questions that no dashboard, CRM report, or intent signal can touch: - "What language do our best-fit prospects use when they first describe their problem?" - "Which objections appear most in deals we lose at the contract stage?" - "Why did pipeline stall last quarter?" and get back a specific answer: competitor X was mentioned in 25 late-stage calls. Jeff Ignacio of RevOps Impact described the shift well in Vasco's 2026 RevOps Trends Report: > "AI brings RevOps closer to the revenue signal. The system finally tells you where to look and what to act on in real time." ## Why is CRM data unreliable for AI GTM agents? CRM data is unreliable for AI GTM agents because most of it is self-reported, entered under time pressure, and not reconciled against what actually happened in conversations. Reps fill in closed lost reasons at the end of a quarter with whatever dropdown option feels closest to true. Pipeline stages reflect optimism more than reality. As one revenue leader I spoke to put it: > "Half of the numbers end up being made up. So what we do is we listen to every single calling conversation and reconstruct the context so that when you plug AI on top of this, you have API level accuracy on the numbers that matter." The downstream consequences are real. When there's no single source of truth for what actually happened in a conversation, reporting is always playing catch-up. Data fragility compounds across systems. Board-level reporting loses credibility. And when you connect an AI agent to that data, it doesn't filter out the noise. It amplifies it. The numbers bear this out. B2B contact data [decays at an average rate of 22.5% per year](https://www.apollo.io/insights/whats-the-average-rate-of-data-decay-in-a-b2b-contact-database-and-how-do-i-address-it), meaning roughly one in four CRM records goes stale every 12 months. [Only 35% of sales](https://www.salesforce.com/sales/state-of-sales/) professionals fully trust the accuracy of their organization's data, and 47% say it's gotten worse in the last year. Call transcripts are the antidote. They're the only objective record of what was actually said, what the prospect actually cared about, and what actually drove the decision. > There's a huge opportunity cost right now on data cleaning that's happening over and over again because there is no single source of truth. Transcripts, placed correctly, become that source. ## How much GTM signal is sitting unused in call recordings? The volume of untapped signal in call recordings is larger than most teams realize. A typical 30-minute sales call produces roughly 4,500 words of transcript. A team running 120 calls per week creates the equivalent of a 700-page novel in raw conversation data every five business days. That data contains things no other source in your revenue stack can tell you: - The exact words buyers use to describe their problem, before any marketing polish - The competitors that keep coming up, and at which stage of the deal - The questions prospects ask when they're close to buying - The early frustrations that predict churn, captured months before a renewal conversation - The feature gaps and capacity constraints that signal expansion readiness Gartner found that [70% of data and analytics leaders believe](https://www.databricks.com/blog/structured-vs-unstructured-data) the most valuable insights in their organizations are trapped in unstructured data. Call transcripts are the single richest category of unstructured data in any revenue stack. Unlike intent scores or web traffic, they aren't signals inferred from behaviour. They're direct, unfiltered buyer reality. The raw material is there. The infrastructure to unlock it usually isn't. ## What does accurate call transcript intelligence actually unlock? ### An ICP built from real buyer language, not quarterly assumptions Ask five people on the same GTM team to describe your top ICP and you'll get five different answers. Every RevOps leader has been in that meeting. Nico Druelle of The Revenue Architects was direct about it in Vasco's 2026 research: "ICP and persona modeling will have to be rebuilt with real product and GTM signals. This should be an always-on GTM brain powering agents and automated workflows, not a static document updated once a quarter." Discovery-stage call transcripts, placed correctly, are exactly those signals. When you cluster customer segments by what you hear in their calls, a clear picture emerges: which segments generate pipeline but never close, which are cash cows, which are rising stars, and the specific reason each one buys. The insight gets specific: > "You've been going after those seven segments of customers, those three bring pipeline, you never close them. Those three are your cash cows, this one is your rising star. Now let's do messaging that actually corresponds to this." That's not an ICP exercise. That's a revenue strategy built from call evidence. ### Win/loss intelligence sourced from actual conversations Win/loss data in CRM fields is mostly fiction. Reps fill in the closed lost reason with whatever option feels closest to true. One useful gut-check: compare what your recording tool picks up in conversations versus what reps are logging as closed lost reasons. The gap between those two numbers tells you exactly how much signal you're losing. When call transcripts are correctly placed by deal stage and outcome, your AI agent can surface which objections appeared in every lost deal this year, which competitor mentions correlate with longer sales cycles, and which pain language in a discovery call predicts a fast close. ### The why behind revenue metrics, not just the what Your revenue dashboard tells you what happened. Conversion rate dropped three points. Average deal size is down. Pipeline velocity slowed in the mid-market segment. [What the dashboard can't tell you](https://vasco.app/blog/product-spotlight-april-2-2026) is why. Call transcripts structured by journey stage and outcome become an explanation engine for metrics. A drop in conversion rate? Your AI agent can surface whether there was a shift in objections at demo stage, a new competitor entering late-stage conversations, or a change in the type of pain prospects were describing. A slowdown in deal velocity? Check whether late-stage calls are showing more stakeholder complexity, more pricing friction, or longer technical evaluation cycles than the previous quarter. This is the connection between call transcript intelligence and the rest of your revenue operations platform. Metrics tell you something changed and transcripts tell you why. That combination quantitative signal explained by qualitative context turns a revenue dashboard from a rearview mirror into something you can act on. ### A shared GTM intelligence layer across every function Right now, marketing starves for sales insights. Sales rarely documents what it learns. CS sits on early churn signals that nobody routes to the right person. But the bottleneck is bandwidth, not willingness. When call transcripts are structured and queryable, that bottleneck disappears. Agents extract objections, pain points, and critical events by persona in real time. Marketing gets live messaging signals from actual buyer conversations. Sales gets cross-deal patterns without anyone writing a summary. CS flags churn risk before the renewal. One data layer serves every GTM function simultaneously and your RevOps team stops cleaning spreadsheets and starts doing what it was hired for. ### A replicable sales coaching playbook Every sales manager knows top performers do something different. Figuring out what that is has historically meant hundreds of hours of manual call reviews. When transcripts are classified by stage and outcome, those patterns surface in minutes. Which questions correlate with shorter sales cycles? What do your top 10% do in the last ten minutes of a late-stage call that everyone else doesn't? > The goal of transcripts is multifaceted: develop playbooks, ensure reps adhere to them, and give leaders the right intel to coach. That stops being aspirational and becomes operational. ### Expansion signals weeks before the customer escalates Post-sale calls are packed with signals that almost nobody acts on not because they don't matter, but because nobody has time to listen to every QBR, onboarding check-in, and success review. When those transcripts are classified and queryable, an AI agent can surface capacity constraint mentions, product gap discussions, and declining engagement signals weeks before they appear in any dashboard. ### Account prep in minutes, not hours Jeff Ignacio of RevOps Impact described the current state plainly: "Account planning feels like homework for sales reps. Hours of scraping websites, gathering data, trying to summarize it manually." When call history is structured and queryable, that prep happens _before_ the rep opens their laptop. Every previous conversation with that account, classified by stage, summarized by what actually mattered, delivered as a brief in seconds. ## What is conversation placement and why does it determine call transcript accuracy? **Conversation placement** is the classification of every recorded call by its position in the customer journey: acquisition or post-sale, discovery or close, onboarding or support escalation. It is the single most important factor in determining whether call transcript data produces accurate AI agent outputs or misleading ones. Without placement, an AI agent querying your call library has no idea that a support escalation from last month and a discovery call from last quarter are completely different types of signal. It searches everything, finds the most frequently discussed topics, and surfaces an answer that is confidently, invisibly wrong. With our own clients, we’ve seen this break in practice: > "If you don't make it right, you can ask things like, 'What is the pain point? Why are people buying my product?' And the agent will say: reset password. Because it will find that in the latest conversation with that account, there was a problem with accessing the application. It wouldn't have the context of no, this is an onboarding call. This is not a call on the acquisition side." This structural gap is what produces 52% accuracy when an AI agent connects to a CRM via raw MCP, versus [98% accuracy when it reasons through a properly structured context graph](https://vasco.app/claude-case-study). It’s the same model, but a completely different output. The difference is the revenue data foundation. There's also a technical dimension worth understanding. When an AI agent is asked about metrics customers won, MQLs generated it pulls from structured tables where accuracy is high and hallucination risk is low. When it's asked about patterns in conversations, that's where the LLM runs through transcripts via the context graph. Keeping these two modes of reasoning separate quantitative metrics on one side, qualitative transcript patterns on the other is what allows you to get precision on both without sacrificing either. In a correctly structured revenue data layer, each call type is a distinct object: - Call type - Journey stage - Motion - What it signals - Discovery call - Pre-opportunity - Acquisition - Buyer pain, ICP fit, competitive landscape - Demo / evaluation - Active opportunity - Acquisition - Objections, decision criteria, stakeholders - Close call - Late-stage opportunity - Acquisition - Deal risk, pricing sensitivity, timeline - Onboarding call - Post-sale - Retention - Implementation gaps, early adoption signals - QBR / success call - Post-sale - Retention / expansion - Health, satisfaction, expansion readiness - Support escalation - Post-sale - Retention risk - Churn signals, product gaps As Nico Druelle put it: > "AI can't work if the data lies to it." Unplaced call transcripts are data that lies. ## What does a structured call transcript data layer require? A structured call transcript data layer requires four components working together: **Stage-aware tagging at ingestion.** Every call gets classified by journey stage and motion type the moment it enters your data layer automatically, at intake. The earlier this happens, the cleaner everything downstream becomes. **Identity resolution across systems.** A call in Gong links to a contact record. That contact may be a duplicate, attached to the wrong account after a rebrand, or simply misspelled. When identity resolution works correctly, your entire call library becomes trustworthy. When it doesn't, your agent inherits years of CRM debt and reasons on it as fact. **Outcome tagging.** Knowing the call type is half the picture. The other half is what happened because of it. Did the discovery call become an opportunity? Did the renewal lead to expansion or churn? Pairing each call with its outcome teaches your agent which conversational signals actually correlate with revenue results. **Journey-aware query logic.** The agent queries within segments, not across everything simultaneously. Late-stage deal objections come from late-stage calls. Onboarding friction comes from onboarding calls. Each query finds the right signal without contamination from the rest. When all four are in place, what Marie-Michèle Caron of Tempo Software described in Vasco's 2026 research becomes real: > "Employees now expect to interact with the data stack. They don't want to run queries. They want to have a conversation with their data, the same way they now interact with consumer LLMs." ## Which call recording tools work with an AI revenue agent? A properly structured revenue data layer can ingest transcripts from Gong, Fathom, Chorus, Otter, and other recording tools. The recording tool matters less than the structure applied when transcripts enter the data layer. One important market context: a growing number of revenue teams are moving away from all-in-one platforms like Gong toward lighter-weight recording tools like Fathom or Grain, paired with a dedicated intelligence layer. As one Vasco team member described: > "We have customers that are leaving Gong to a much more affordable note taker and using that with Vasco. Gong is extremely expensive, especially on a seat model, and you end up paying a very significant amount." The trade-off is getting 80% of the value at a fraction of the cost, with the intelligence layer purpose-built for analysis rather than bolted on to a recording tool. One practical consideration: if your team sells across languages, verify your recording tool handles them properly before standardizing. English-only transcript quality for French, Spanish, or Portuguese calls creates a real blind spot in your call intelligence and most tools don't advertise this limitation clearly. ## How long does it take to build call transcript intelligence for AI agents? Building a production-ready call transcript intelligence layer from scratch takes 12 to 18 months. This includes defining lifecycle stages, building identity resolution logic, creating outcome tagging pipelines, and writing journey-aware query logic. There are faster paths: **Pre-built platform:** 3 to 6 months to configure and deploy a system with the context graph architecture already in place. **Assembled with embedded support:** 30 to 90 days, using a platform with a pre-built context graph and Forward-Deployed RevOps expertise configured to your specific CRM structure, attribution logic, and lifecycle definitions. The compounding effect matters here. Gong's research shows sales reps spend only 30% of their time on revenue-producing work (Gong, cited in Vasco 2026 RevOps Trends Report). When the structure is in place, every call recorded adds to a growing body of intelligence. As one customer put it: "I could definitely see how you could save us some things we have on our roadmap to build internally and help us pivot to what we should be building: more company-specific things." As Victor de Coster added: "When agents handle 80% of the prep, humans focus on what moves outcomes. That's a growth loop. Stack a few, and momentum compounds." Every week without structure is another week of transcripts piling up unused. Another week of confident wrong answers. Another week of signal going to waste. ## How to audit your call transcript data quality: three steps **Step 1: Pull 20 recent calls from your recording tool.** For each one: is it linked to the right account? Is it tagged with the journey stage the customer was actually in? Does that label match what happened downstream? You'll see where placement breaks down, usually within the first five calls. **Step 2: Define your lifecycle stages explicitly, including motion type.** "Opportunity Stage 3" tells an AI agent nothing useful. You need: acquisition vs. post-sale, call type (discovery / demo / close / onboarding / QBR / support), and outcome (what happened because of this specific call?). If five people on your team can't agree on this list in under an hour, that's your diagnosis. **Step 3: Build the context layer before you scale the agent.** Most teams do this backwards deploy the agent first, get weird answers, then try to fix the data. Build the foundation first. Everything built on top of it improves automatically from day one. ## FAQ ### How do call transcripts improve AI agent accuracy in revenue operations? Call transcripts improve AI agent accuracy when each conversation is classified by its position in the customer journey. An agent that can distinguish a discovery call from a support escalation queries the right segment for each question, producing answers grounded in correct context rather than a mix of all call types. Without that classification, the agent surfaces the most frequently discussed topics across all conversations regardless of stage, producing confident but inaccurate outputs. This structural difference is what separates 52% accuracy from 98% accuracy on the same underlying AI model. ### What is a revenue context graph and how does it use call transcripts? A revenue context graph is the structured data layer that sits between raw CRM and call recordings and AI agents. It resolves contact and account identities across systems, classifies every call by journey stage and motion type, enforces shared metric definitions, and tags outcomes so each conversation carries the context an agent needs to reason accurately. Call transcripts ingested through a revenue context graph are queryable by stage, outcome, and persona rather than simply by account or date. ### Why does an AI agent give wrong pipeline answers even with Gong connected? Connecting Gong to an AI agent delivers access to transcripts, not structure. Without stage-aware classification at ingestion, the agent queries all associated calls regardless of type discovery, onboarding, or support. Post-sale conversations contaminate acquisition-stage signals, producing confident answers built on the wrong context. The fix is not a better model. It is a structured data layer that classifies each call before the agent reasons on it. ### What is conversation placement in GTM? Conversation placement is the practice of tagging every recorded sales and customer call with its correct position in the customer journey distinguishing acquisition calls from post-sale ones, early-stage discovery from late-stage closes, and customer success check-ins from support escalations. Without conversation placement, AI agents have no way to distinguish which signals belong to which phase of the customer relationship, producing analysis that blends incompatible contexts into a single misleading output. ### Which call recording tools work with a revenue context graph? A revenue context graph can ingest transcripts from Gong, Fathom, Chorus, Otter, and other recording tools, provided stage classification and identity resolution happen at the point of ingestion rather than as a downstream cleanup step. The recording tool is less important than the structure applied when the transcript enters the data layer. ### How long does it take to structure call transcript data for AI agents? Building the full infrastructure from scratch takes 12 to 18 months. Using a pre-built revenue data platform with embedded RevOps configuration support compresses that to 30 to 90 days, depending on the complexity of your CRM structure and how much legacy data quality debt needs to be resolved first. ### Does transcript volume affect AI agent output quality? Volume matters far less than structure. Ten correctly placed, stage-tagged, outcome-labeled calls produce more reliable AI agent output than 10,000 unplaced transcripts mixed across lifecycle stages. Structure determines the ceiling, not the number of calls in the library. --- --- title: Inside the first wave of GTM-built AI agents description: "After 10 weeks and 180 AI agents built across 37 organizations, the teams getting the most reliable outputs weren't running better models. They were giving their agents better context to reason from." canonical: "https://vasco.app/blog/gtm-teams-Vasco-agents" date: "2026-06-16T20:25:00.000Z" authors: - Guillaume Jacquet jobTitle: "CEO & co-founder" readingTimeMinutes: 5 contentType: article intent: strategy-insights pillar: "AI in GTM & RevOps" audiences: - revops - cros - founders - fractional - start-ups - scale-ups - enterprise --- # Inside the first wave of GTM-built AI agents _These GTM Teams build their own AI agents, and here's what they're actually making_ A pattern is emerging from the first wave of GTM teams building AI agents inside Vasco: teams are moving beyond experimentation and creating systems that monitor, analyze, and improve revenue operations. After launching AI agent capabilities, Vasco analyzed 10 weeks of builder activity across 37 organizations. During that period, teams created roughly 180 custom AI agents. The data reveals two clear behaviors. Most teams start with proven templates, but the teams building the most sophisticated systems create custom agents around problems unique to their revenue motion. ![](https://cdn.sanity.io/images/ys8gstp8/production/3497a619e906f8bf85e186c68d91e9b0af79735f-1200x1500.png?w=1600&fit=max&auto=format) ## Teams start with templates, then build beyond them About 65% of the 180 agents created during this period were based on existing templates, including CRO and CEO briefs, forecast summaries, win/loss analyses, pipeline reviews, and ICP and TAM discovery. Templates work because they solve common GTM problems quickly. Several teams created multiple versions of the same template to support different business units, sales motions, or reporting needs. The remaining 35%, or roughly 65 agents, reveal a different pattern. These agents were built from scratch to solve specific operational gaps that teams could not address with a standard workflow. ## Custom agents reveal where GTM teams need more visibility The custom agents built by teams share a common purpose: they continuously monitor a specific part of the revenue process and alert teams when something requires attention. Teams built agents that validate deal creation, monitor retention risk, detect forecast anomalies, track churn signals, identify past-due opportunities, audit pipeline hygiene, and analyze MQL response times. These agents are not replacing existing reports. They are watching for changes that are easy to miss when teams rely on manual reviews. ## Scheduled agents are becoming part of the operating system The difference between a useful AI agent and a trusted AI agent is whether the team can rely on its output without checking every answer. > “Everyone can build something that looks right in a demo. The hard part is building something that’s right at 7am on a Monday before anyone’s logged in. The teams we see going deepest aren’t asking how to get an agent to run. They’re asking what that agent needs to know to be trustworthy.” – Guillaume Jacquet At least 10 organizations now run agents on recurring schedules. These agents operate before teams start their day and surface issues before they appear in a pipeline review, forecast meeting, or renewal discussion. They go beyond answering questions on-demand to continuously monitor the business and surface changes before teams need to ask. ## The teams building the most agents treat them as infrastructure Agent adoption varies across organizations. Around 8 companies account for most of the 180 agents created during this period. The largest Vasco builder has 13 active agents. The most customized deployment has 6 agents built entirely from scratch, effectively turning Vasco into a custom RevOps monitoring layer. The difference between organizations running two agents and those running thirteen is not access to the technology. It is the decision to treat agents as part of the operating infrastructure. The teams building the most agents start with a specific operational problem, create an agent to solve it, and continue expanding from there. ## AI agents are limited by the context they receive The biggest constraint on AI agents is not the model. It is the information the model has available when making decisions. An agent connected only to CRM data does not understand the meaning behind the fields it reads. It does not know that a “verbal commit” deal has been stalled for 90 days, that an account has changed segments, or that different teams interpret pipeline stages differently. The output can still look polished. The answer can still be wrong. For RevOps teams, hallucination rarely appears as obvious nonsense. It appears as a confident recommendation built on incomplete information. The custom agents built during this period point to the same conclusion: teams need a reliable context layer before they can trust AI agents with revenue decisions. They are defining business rules, connecting related records, reconciling account identities, and giving agents a current view of their revenue motion. The scheduled monitors posting to Slack every morning are valuable because teams trust the information behind them. The investment is not just in building more agents. It is in giving those agents the context required to make accurate decisions. --- --- title: Introducing the revenue agent marketplace description: "Browse pre-built agents for every revenue motion, hire one in a click, and customize from chat. No prompt engineering, no setup time. Your whole team ships agents that actually work." canonical: "https://vasco.app/blog/product-spotlight-June-2026" date: "2026-06-02T00:00:00.000Z" authors: - Guillaume Jacquet jobTitle: "CEO & co-founder" readingTimeMinutes: 1 contentType: product-spotlight intent: foundations pillar: "RevOps systems & architecture" audiences: - enterprise --- # Introducing the revenue agent marketplace _Hire a revenue team that never sleeps_ ## 1. The Agent Hub is open to everyone Pick a template, hire it in one click, and it runs against your actual revenue data. No configuration, no prompt tuning, no waiting on your data team. If you want something custom, describe it in chat and the Agent Creator builds it alongside you. [Introducing the revenue agent marketplace](https://youtu.be/kg195NBnZxE) ### Pre-built agent templates that work out of the box. Pipeline review, WBR generation, ICP discovery, churn prediction, rep coaching. Each one grounded in your context graph from day one. ### Customize from chat. Every template is a starting point. Iterate in conversation and end up with an agent tuned to your specific motion, your definitions, your data. ### Build custom agents from scratch in minutes. Describe what you need. The Agent Creator finds a matching template or walks you through full custom creation, with a live preview building alongside you. ### Your whole fleet in one view. See when each agent last ran, when it runs next, and what it produced. No digging around. ## 2. Why the model your agents run on matters We rebuilt Gama on the Claude Agent SDK. The surface looks the same. Underneath, the model running your agents is now the one that gets it right. Since the migration: no accidental Slack messages sent to general. No mid-session failures. WBRs are more detailed, analysis goes deeper, and the model does not make things up. ### No hallucinations on your pipeline. Claude does not invent numbers. Your WBRs reflect what is actually in the data. ### Deeper analysis, better formatting. Reports go further than before, with references to previously generated artifacts so context carries across sessions. ### Visible reasoning. Watch Gama think through the problem in real time. Useful for debugging agents. Useful for trusting the output. ## Also shipped - **Data Medic: CRM errors that fix themselves.** Radar flags the issue, data Medic explains it in plain language, groups it into actionable fixes, and auto-resolves what it can. All in one click. For anything that needs a human, it tells you exactly where to go and what to change. - **New integrations including Modjo, Fireflies, Fellow, Aircall, and Attio.** Every major call recorder now flows into Vasco. Attio is live as a supported CRM. Activity deduplication ships by default so you get one clean event per real interaction. --- --- title: "RevOps + Claude Cowork: What is the AI context gap?" description: "Context graphs built for RevOps are what make Claude outputs trustworthy. Without context, the data can be clean or dirty, complete or missing, reconciled or contradictory, and Claude will reason on it either way." canonical: "https://vasco.app/blog/claude-cowork-revenue-data-layer" date: "2026-04-03T00:00:00.000Z" authors: - Guillaume Jacquet jobTitle: "CEO & co-founder" readingTimeMinutes: 4 contentType: article intent: foundations pillar: "AI in GTM & RevOps" audiences: - enterprise --- # RevOps + Claude Cowork: What is the AI context gap? _How the right data layer turns pipeline confidence into pipeline accuracy_ Last month, Claude told one of our customers their pipeline was on track. They were missing their target by 21%. Nine deals hadn't been touched in 30 days. A live customer had cancelled their payment method. A Slack thread flagged a competitor displacement, but nobody updated the CRM. Claude reported none of it. This isn't a story about AI being wrong. It's a story about what confident, incomplete answers cost a revenue team that needs to build the infrastructure to support them. Yes, Claude Cowork is genuinely useful. It connects to your CRM, automates pipeline reports, and answers revenue questions in plain language. The productivity gains are real. But Claude doesn't verify. It reasons, and it reasons on whatever you give it. The data can be clean or dirty, complete or missing, reconciled or contradictory. Claude doesn't say "I'm not sure." It says "here's your weekly pipeline summary", and it's wrong in a way that looks exactly like being right. ## The blind spots of Claude Cowork and MCP: Why a CRM connection isn’t enough **Model Context Protocol, or MCP**, is the connector layer that gives Claude access to your CRM. It hands Claude whatever your reps entered. That includes what they skipped, mislabelled, and forgot. It's a pipe, and a good one. **The context gap unfolds because the connection (or pipe) doesn't clean what flows through it.** Here's what HubSpot MCP _does_ give Claude: - Deal records as they exist today, not how they actually progressed - Activity logs, to the extent reps logged them - Stage data, including stages nobody updated when the deal went cold Here's what HubSpot MCP _doesn't_ give Claude: ### It doesn't share what your numbers mean What's an SQL in your business? How do you calculate NRR? What counts as churn versus a downgrade? HubSpot doesn't encode these. Neither does Claude. So it makes its best guess confidently, and without flagging the ambiguity. ### It doesn't share if you're on track Claude can pull your closed-won total. It has no idea what your number is, which motion is supposed to carry it, or whether your current pace puts you ahead or behind. A number without a target isn't a signal. It's noise. ### It doesn't share what's happening outside your CRM Your CRM says the deal is active. Your call recording platform has a transcript where the prospect's leadership called budget a "huge challenge." Your billing platform shows a payment bounce on a live account. A Slack thread between your AE and their manager says the champion just left. Claude, via HubSpot MCP, sees none of this. ### It doesn't share where decisions actually get made Half the decisions that shape a deal happen in Slack. That means the data isn’t attached to a single source of truth. None of it reaches the CRM, including; escalations, pricing approvals, competitive intel, a CSM flagging renewal risk. Claude can't reason about what it can't see. ### It doesn't give reasoning to question what it's given Claude treats all data as ground truth. That includes skipped stages, stale deals, missing close dates. It has no way to know otherwise. This is what a revenue data layer built specifically for RevOps solves. And without it, silent pipeline decay ensues. ## 3 ways Claude Cowork failed our customer’s pipeline review Let’s dig into what happened to our customer last month. They’re a B2B SaaS company, mid-seven-figure ARR, 38 active accounts, running on HubSpot. We’ve anonymised the examples below, but the numbers are real. ### Failure #1: No targets loaded _Claude had no pipeline target context_ The team had a number: $240,000 for the quarter. It lived in a spreadsheet, updated by the RevOps lead every 90 days. **Claude had never seen it.** So when it looked at $180,390 in closed-won ARR and a pipeline that appeared to be moving, it had no frame of reference to tell the difference between on track and 21% behind. ![](https://cdn.sanity.io/images/ys8gstp8/production/5c237fe1d43dc012cdba4682a4d87cd6a424a242-1200x628.png?w=1600&fit=max&auto=format) It reported progress. The team heard confidence. Nobody questioned it because the output looked exactly like outputs they'd acted on before. Two numbers told completely different stories: - **$180,390 new ARR against a $240,000 target, which is a 21% miss.** The CRM couldn't surface this because it didn't know the target. Claude reported the number. Nobody told it what the number meant. - **34% SQL-to-SAL conversion.** The funnel was bottlenecked at qualification. Claude reported the MQL-to-SQL number — 75% — because that's what HubSpot tracks cleanly. The conversion rate that actually mattered was invisible to it. By the time someone ran the numbers manually, two weeks of pipeline calls had already been run on the wrong assumption. ### Failure #2: No one governing the data _Claude trusted what it was given_ Nine deals in their pipeline hadn't been touched in over a month. This includes no calls logged, emails sent, or stage changes. The reps were unsure about the truth, suspecting these deals were cold. Closing them out means admitting a loss, and nobody had been prompted to do it. So they watched as HubSpot and Claude counted further away from accuracy. ![](https://cdn.sanity.io/images/ys8gstp8/production/5f8d219fa92200d410269f889bae4c7f2b2f6b58-1200x628.png?w=1600&fit=max&auto=format) The pipeline looked fuller than it was because the data said it was, and Claude had no way to ask whether the data was telling the truth. One deal bucket summed it up: - **9 deals with zero engagement for 30+ days.** No calls. No emails. No stage changes. HubSpot still showed them as active because no rep had closed them out. Claude reported them as part of a healthy pipeline. It’s a phantom pipeline, where deals that look like opportunities have no real pulse. ### Failure #3: No bridge between systems _Claude couldn’t see outside the CRM_ This is where the story gets costly. While Claude was reporting a stable week, the data it was missing would have changed every conversation in that pipeline review. ![](https://cdn.sanity.io/images/ys8gstp8/production/dfc194ed151563cd9633ab7c087350f2bd9190d9-1200x628.png?w=1600&fit=max&auto=format) Three risks were hiding in plain sight: - **Norden had cancelled their payment method and closed their bank account.** The billing event was in Stripe. The CRM showed a healthy account. Claude never saw it. - **Kepler's leadership flagged budget as a "huge challenge"** on a recorded sales call. The deal was still progressing in the CRM as if that conversation had never happened. - **A Slack thread between the AE and Sales Director flagged a competitor displacement at Meridian.** The AE had written "they're evaluating [Competitor X], we need to move fast." Nobody updated the deal record. Claude reported Meridian as on track. Three active risks, including a churning customer, a stalling deal, a competitive threat, were left unflagged. Why? Because the signals lived in systems Claude couldn't see. Now multiply that first output by every report, every one-on-one prep, every territory review, every board update. The confidence compounds. The inaccuracy compounds with it. ## The solution: A revenue data layer built for RevOps The three failures above share a single problem: Claude had no structured foundation to reason across. No shared definitions, no plan context, no cross-system connections. It made its best guess every time, and the best guess looked exactly like the right answer. The opportunity is straightforward. Build a revenue data layer: a structured, clean representation of how your business actually works, built specifically for AI to reason on. Every agent you deploy gets a single trustworthy version of your revenue reality. Not a data warehouse. Not another CRM field. Three connected layers that together close the gap. ![](https://cdn.sanity.io/images/ys8gstp8/production/312b80a94455f95b0c50dadb2b163b13441fd950-1200x628.png?w=1600&fit=max&auto=format) ## ### Layer 1: Foundational | Your business map This is your ICP tiers, lifecycle stages, channel definitions, and revenue taxonomy. Your foundational layer tells Claude what an SQL means in your business, which motion is supposed to carry the number, and how your funnel is supposed to flow. Without it, Claude reverse-engineers a plausible answer every time you ask it a question. ### Layer 2: Planning | Precise numbers The planning layer includes targets by rep, quota by motion, benchmarks by segment. This is what turned $180,390 from a neutral observation into a 21% miss. Without a planning layer, Claude has no target to compare against, and reports the number as on-track. ### Layer 3: Context graph | The connective tissue With a [context graph](https://vasco.app/blog/context-graphs-for-gtm-teams), deal histories, call transcripts, billing events, and Slack threads become connected, sequenced, and traceable to source. This is what would have surfaced Norden's cancellation, Kepler's budget objection, and the Meridian competitor threat before they became invisible risks in a confident report. As Guillaume, Vasco's CEO, puts it: > _"If you don't have that data layer for AI to reason on, it's going to reverse-engineer a plausible answer every single time you ask it a question. At any company size, that doesn't get you to a production-ready RevOps stack."_ The good news is you don't need to build this from scratch. The right platform gives RevOps teams the structured revenue data layer out of the box. That means you'll be ready to connect agents that run on top of your business's unique pre-configured definitions, lifecycle stages, and revenue taxonomy. ## It’s an AI context gap, not a Claude Cowork gap Same Claude, different data layer. The AI context gap is a readiness problem, and it shows up in every AI workflow deployed before the foundation is in place. Claude didn't reveal a flaw in the tool. It revealed where most RevOps stacks actually stand. Getting Claude Cowork right starts with establishing a revenue data layer underneath it, by employing a revenue context graph that gives Claude a single, trustworthy version of reality to reason across. Get that right and the outputs will drive outcomes. ## FAQ ### Does HubSpot MCP support the cross-object analysis RevOps teams actually need? In practice, HubSpot MCP is a thin API wrapper. It handles record lookups and basic filters, but not the joins, aggregations, or historical stage transitions that real RevOps reporting requires. If you need to know how a deal progressed, not just where it sits today, MCP won't give you that. ### Can MCP surface early warning signals before a deal goes "at risk"? Not reliably. It can't connect a deal to the transcript where budget was flagged, the Slack thread where a competitor was mentioned, the billing event showing a failed payment, or patterns from similar deals that stalled or churned. A revenue context graph surfaces those signals before deals go red. MCP surfaces them after, and only if the rep remembered to log them. ### My team is already getting value from Cowork without a data layer. Is this really a problem? Possibly not yet. The risk isn't that Claude gives obviously wrong answers, it's that it gives subtly wrong ones that look right. If you're using it for drafting and summarisation, the downside is low. If you're using it for pipeline calls, territory planning, or board reporting, the downside is a decision made on a number that was never checked. The gap only becomes visible when someone validates the output against reality. Most teams don't, because the output sounds confident. ### Should I wait until my data is clean before using Claude? No. Claude is useful today for drafting, summarising, and accelerating workflows that don't depend on data accuracy. What you need to solve before trusting it for pipeline reviews, forecasting, and revenue decisions is the data layer underneath it. Use Claude now. Just know where the trust boundary is, and don't cross it until the foundation is ready. ### Who should own the MCP configuration? RevOps. MCP connections inherit whatever permissions the authorising user holds. Treat Claude's data access the same way you'd treat any integration: explicit authorisation, documented scope, named owner. If you don't know who owns it today, that's worth finding out. --- --- title: "AI is the new UI: Vasco connects to Claude via MCP" description: "Your agents are only as good as the data they reason on. Now, every Claude user in your organization can query your verified revenue data: governed, accurate, and grounded in your actual business." canonical: "https://vasco.app/blog/product-spotlight-april-2-2026" date: "2026-04-02T00:00:00.000Z" authors: - Guillaume Jacquet jobTitle: "CEO & co-founder" readingTimeMinutes: 1 contentType: product-spotlight intent: foundations pillar: "RevOps systems & architecture" audiences: - enterprise --- # AI is the new UI: Vasco connects to Claude via MCP _Connect Claude to revenue data it can actually trust_ ## 1. Vasco is now a headless revenue engine We're a fully agentic RevOps platform, and that's been true since we launched [the GTM context graph](https://vasco.app/blog/product-spotlight-april-2026). But this update takes it further: **Vasco can now operate completely headless**, powering Claude and any AI workflow in your stack with the trusted revenue data it needs to reason accurately. Connect via MCP, and your entire GTM context graph (100+ sources reconciled into a single source of truth) flows into every Claude conversation your team has. The UI becomes optional. ![](https://cdn.sanity.io/images/ys8gstp8/production/6faa78616ae8c5984aa9a9426a54814b3d84ab57-1200x630.png?w=1600&fit=max&auto=format) ‎ > **What changes for existing Vasco customers?** Everything. Instead of only working inside Vasco's interface, your data layer now powers any agent, any workflow, any LLM. Your team gets a verified AI companion. Your RevOps function gets control, and the wild west is over. ### Three ways to build, now including Claude via MCP. Use Vasco's pre-built agents out of the box, create your own with the no-code agent builder, or connect Claude (or any LLM of your choice) through MCP. Same data layer, same governance, and your choice of interface. ### Deterministic answers at 98%+ accuracy. Every metric sourced, every definition locked. Ask the same question five times, get the same answer five times, whether you're asking inside Vasco or through Claude in a Slack thread. ### Causality, not just data. Now your team can ask _why_ a metric moved. Vasco surfaces intent buried in emails, call recordings, Slack messages, and support tickets. The answer isn't just the number. It's the story behind it. ## 1. Why connecting Claude alone isn't enough Here's something we say plainly: [just connecting your CRM to Claude via MCP is not going to cut it](https://vasco.app/blog/claude-cowork-revenue-data-layer). You'll get somewhere between 10 and 50% accuracy. It may look impressive, but the answers are made up. And your entire organization starts making decisions on hallucinated numbers. That's not a Claude problem. That's a data problem. And it's exactly the problem Vasco was built to solve. ![](https://cdn.sanity.io/images/ys8gstp8/production/9d561f271c203aae069dcc53e1f76beea919c054-800x1200.png?w=1600&fit=max&auto=format) > With Vasco MCP, your team is querying **your specific metric definitions, your revenue plan, your closing event logic, your data hygiene scores**: everything that makes an answer trustworthy rather than plausible. The RevOps manager who built their first agent last month? They can now walk into a live meeting, get a question they weren't expecting, and answer it accurately, immediately, with the rationale, without waiting a week for a new report. They look like a star. The data earned it. ### Pre-built agents vs. MCP: same data, different surfaces. Our pre-built agents are prompt-optimized for RevOps best practices and run natively inside Vasco. MCP lets you bring that same verified data into Claude, wherever your team already works. It's not either/or. It's however your organization builds. ## 3. Self-serve BI, no more tickets Let's be honest about what's happening in most organizations. Someone in sales wants to know their pipeline coverage. They Slack RevOps. RevOps opens a new ticket. Three days and two report variants later, the number is either wrong or already stale. That loop ends now. Connect Vasco MCP, and every person in your organization gets an AI companion that answers their revenue questions on demand, in their workflow, grounded in the same source of truth your RevOps team built and governs. No dashboards required, because **AI is the new UI.** ![](https://cdn.sanity.io/images/ys8gstp8/production/ee7e9145627d740503731bc4a3ef273f32551f3e-1200x630.png?w=1600&fit=max&auto=format) ‎ ### Governance without gatekeeping. RevOps gives the organization access to Vasco MCP. Anyone using Claude reasons on validated metrics, enforced definitions, and live data. One source of truth. Infinite ways to query it. ### Out of the box, today. You don't have 12 months to wait for your data team to build this. With Vasco, the entire plumbing (ETL, data freshness, metric enforcement, governance) is done. Connect your CRM, let Gama guide you through setup, and get to your first insight in minutes. ### Slack, Zendesk, and 100+ integrations, open to everyone. Every integration in Vasco's GTM stack (call recordings, billing, product usage, support tickets) is now available to all customers. All reconciled and in context. ## Other cool upgrades - **Business Review Prepper.** The pre-built WBR agent auto-generates your weekly business review from live revenue data and produces a shareable artifact your whole org can inspect and act on. No prep needed. - **Account & activity tables.** Fully interactive spreadsheet view with drag, drop, pin, and hide columns. Every column shows its integrity score so you know at a glance where data is missing or out of scope. ![](https://cdn.sanity.io/images/ys8gstp8/production/c636e932b898bf45a3fafdc58292d90d51e69b56-1200x630.png?w=1600&fit=max&auto=format) ‎ ‎ - **Agentic onboarding.** Connect your CRM and Gama walks you through every key configuration step (mapping stages, defining your closing event) automatically or step by step. First insight in minutes. - **Create dimensions on the fly.** Add new custom dimension columns directly from the account table with one-click suggestions. No data team required. --- --- title: "Your revenue data layer, purpose-built for AI agents" description: "Your agents are only as good as the data they reason on. Vasco is now a fully agentic RevOps platform, closing the gap between great AI models and the trustworthy revenue data they need to deliver." canonical: "https://vasco.app/blog/product-spotlight-april-2026" date: "2026-04-02T00:00:00.000Z" authors: - Guillaume Jacquet jobTitle: "CEO & co-founder" readingTimeMinutes: 1 contentType: product-spotlight intent: foundations pillar: "RevOps systems & architecture" audiences: - enterprise --- # Your revenue data layer, purpose-built for AI agents _Introducing Vasco's GTM context graph_ ## 1. The revenue data layer purpose-built for AI agents [https://www.youtube.com/embed/iQWXSPGVhs8?iv_load_policy=3&rel=0&modestbranding=1&playsinline=1&autoplay=0&mute=1](https://www.youtube.com/embed/iQWXSPGVhs8?iv_load_policy=3&rel=0&modestbranding=1&playsinline=1&autoplay=0&mute=1) **Vasco is now a fully agentic RevOps platform.** That means your revenue data is defined, planned, and always in context — so your agents can reason on your actual business, not a fragmented approximation of it. - **GTM Context Graph.** Vasco connects 100+ sources (CRM, billing, call recordings, product usage, Slack, support tickets, etc.) and reconciles everything into a single structured graph. Think of it as your go-to-market operating brain. - **Deterministic answers.** Every metric is sourced, every definition is locked. Ask the same question five times, get the same answer five times. - **Three ways to build.** Use Vasco's pre-built agents out of the box, create your own with the no-code agent builder, or connect Claude and any LLM of your choice via MCP. ## 2. Account & activity tables ![Studio](https://cdn.sanity.io/images/ys8gstp8/production/03825520f59b2a9049727b32583524deeff76b3d-3201x1200.png?w=1600&fit=max&auto=format) **Your revenue data, laid out the way you actually want to explore it.** Accounts and activities are now displayed in a fully interactive spreadsheet view. Easier to navigate, faster to act on. - **Spreadsheet-like experience.** Drag, drop, pin, and hide columns to customize your view. Filter your entire account base without the cluttered navigation. - **Data integrity, in context.** Every column now shows its integrity score, so you can see at a glance where data is missing, incomplete, or out of scope. - **Create dimensions on the fly.** Add new custom dimension columns directly from the table, with one-click suggestions and Gama to guide you through the setup. ## 3. Try Vasco free with your own data ![](https://cdn.sanity.io/images/ys8gstp8/production/d6557a583ec0e9851d4c94c58f375f6834fe20eb-3900x2031.png?w=1600&fit=max&auto=format) **Connect your CRM, let Gama guide you through setup, and get to your first insight in minutes.** - **Agentic onboarding.** Once your CRM is connected, Gama walks you through the key configuration steps — mapping your stages, defining your closing event — either automatically or step by step. - **Start exploring immediately.** See your actuals mapped on the bowtie in Pulse, your data hygiene score, and receive your first daily digest — a snapshot of your revenue engine delivered to your inbox and Slack. - **Go further when you're ready.** Map your revenue plan to compare actuals vs. forecast, connect additional sources, and build your first agent, all at your own pace. ## Other cool upgrades ![](https://cdn.sanity.io/images/ys8gstp8/production/0667eab08db03ae14512d6a7c23f8fe8ec919bf0-3201x1932.png?w=1600&fit=max&auto=format) - **100+ integrations.** Vasco now connects to your entire GTM stack: CRM, billing, call recordings, product usage, Slack, support tickets, and more. All reconciled into a single source of truth. - **Pre-built GTM agents.** A full fleet of agents is available out of the box. The Business Review Prepper auto-generates your WBR from your live revenue data, and produces a shareable artifact your whole org can inspect and act on. --- --- title: "The RevOps roadmap: Pre-seed to Series E" description: "Great RevOps is about having the right foundation at the right moment. Whether you're closing your first deals or managing a 300-person GTM org, this stage-by-stage roadmap shows you exactly where to focus your energy to drive predictable revenue growth." canonical: "https://vasco.app/blog/revops-roadmap-company-stage" date: "2026-03-23T00:00:00.000Z" authors: - Justin Hudon jobTitle: Head of Sales and Customer Success readingTimeMinutes: 3 contentType: article intent: strategy-insights pillar: "RevOps maturity & scaling journeys" --- # The RevOps roadmap: Pre-seed to Series E _Know what to build and who to hire at every phase_ A 15-person startup and a 400-person scale-up need completely different RevOps strategies. Build too much too early and you're maintaining infrastructure nobody can trust. Wait too long, and the technical debt compounds faster than you can fix it. Gartner predicts [75% of the highest-growth companies](https://www.gartner.com/en/sales/topics/revenue-operations) will run a RevOps model by 2026, but keep in mind that adoption isn't the same as effective execution. As Guillaume Jacquet, CEO of Vasco, [puts it](https://www.youtube.com/watch?v=fJhqc9oRFsg): > "Getting from $1M to $10M in ARR is "20% magic and 80% is a paved road.” The paved road is in your control, and building the right infrastructure for your unique GTM plan is what will create more of the magic you need to distinguish your business in the market. Before diving into the stages, let’s cover one prerequisite that runs through all stages, and why most companies only discover it when it’s too late. ## The hidden prerequisite at every stage: Your revenue data layer for AI tools Every stage of RevOps maturity described below shares one underlying requirement of structured, clean data that reflects how your business actually works. Your ICP tiers, lifecycle stages, channel definitions, and revenue taxonomy are the foundation that determines whether AI tools like Claude Cowork can accurately reason about your business or just make the best guess. Guillaume explains: > "If you don't have that data layer for AI to reason on, it's going to reverse-engineer a plausible answer every single time you ask it a question. At any company size, that doesn't get you to a production-ready RevOps stack." ‎ You don't need a full RevOps team to build this foundation right. The right platform gives early-stage founders the structured revenue data layer out of the box: pre-configured ICP tiers, lifecycle stages, and revenue taxonomy that's ready to scale with you. The work you do at each stage builds the layer that makes the next stage possible, and the right tooling means you're not starting from scratch every time. ## The 4 stages of RevOps by company size RevOps evolves across four stages: 1. Foundational (Pre-Seed to Series A) 2. Formalizing (Series B) 3. Scaling (Series C–D) 4. Optimizing (Series E+) ![](https://cdn.sanity.io/images/ys8gstp8/production/dc5f32c64668870d55b5ca28a04ca72fe48421d4-1200x628.png?w=1600&fit=max&auto=format) ‎ At each stage, the focus shifts from building clean foundations, to operationalizing the GTM motion, to eliminating revenue leakage, to running RevOps as a fully strategic, AI-enabled function. ## Stage 1 | Pre-Seed to Series A: Build the foundation _RevOps owner: The founder or Head of Sales (and likely three other jobs)_ In stage one, a CRM exists, but adoption is inconsistent. Marketing and Sales are tracking success with different numbers. Forecasting is a gut feeling with a spreadsheet attached to it. This is a trap most founders fall into at this stage. They assume they can replicate what got them to $1M ARR. They hire a few salespeople, hand them a quota, and expect the same results. It doesn't work, because what got them here was less systems thinking and more founder-led instinct. ### The #1 priority is your data structure How you [set up your CRM](/blog/lifecycle-stages-and-crm-setup), define your lifecycle stages, and name your campaigns today will determine how fast you can scale 18 months from now. Technical debt at this stage is quiet. It doesn't hurt [until it suddenly does](/blog/how-vcs-evaluate-startup-maturity-for-series-a-and-b), all at once, right when you're trying to close a Series B. Three things to do now: 1. **Define your revenue taxonomy**: Before you have 10 reps doing it differently, map out your lifecycle definitions, opportunity stages, channels and what information is relevant at each step of the journey. 2. **Pick one North Star metric that aligns GTM teams**: Choose pipeline coverage, CAC payback or any metric that bridges accountability. Just one number, shared by everyone. 3. **Resist living in too many tools**: Your CRM, spreadsheets and other tools prevent you from end to end analysis of your pipeline data. ### Your data structure is your AI foundation Most early-stage teams assume a revenue data layer is something you earn later, once you have the data volume and the team to build it. It's not. The context you configure now (ex. lifecycle stages, field definitions, a single source of truth) makes a context graph possible from day one. Get this wrong, and AI will reason on your business incorrectly. For seed to Series A companies, this foundation also shapes your understanding of your ICP, including what resonates, at which stages, which questions to ask, and which profiles are worth pursuing. That clarity comes from configuration, not volume. ![](https://cdn.sanity.io/images/ys8gstp8/production/312b80a94455f95b0c50dadb2b163b13441fd950-1200x628.png?w=1600&fit=max&auto=format) ‎ Building [a revenue engine that doesn't depend on you](/blog/the-science-of-scaling-according-to-mark-roberge) starts with getting your pipeline back under control. And when the system runs predictably, you're not stepping back. You're finally stepping into [the role the business actually needs from you](/blog/when-to-stop-being-your-own-best-salesperson). ## Stage 2 | Series A to Series B: Operationalize the GTM motion _RevOps owner: To be determined_ The GTM motion is no longer founder-led, nor is it documented. Leads fall through handoffs, reps run their own process, and the board wants a forecast you can't confidently give. Marketing blames Sales for not following up. Sales blames Marketing for bad leads. But the real problem is nobody has defined what a good lead, a clean handoff, or a qualified opportunity actually looks like. When everyone owns the whole funnel, the urgent always beats the important. ### The #1 priority is making the GTM motion repeatable Getting from $2M to $10M means codifying playbooks, specializing roles, and normalizing processes, so that adding headcount produces predictable output. Conversion rates are known, and every call looks like a variation of the last. **The critical nuance: **[build around more than one ICP](/blog/icp-across-maturity-stages)**.** You'll hit saturation faster than you think. Here are two strategies to make it happen: 1. **Test adjacent ICPs and motions now**, and be sure to keep them strictly segmented. If you blend them into your core data and your unit economics will look like they're falling apart when they're not. 2. **Segment performance data by GTM motion** and ICP so you can tell which new bets to accelerate and which to cut. A repeatable GTM motion means documenting every handoff from first touch to CS, defining SLAs between teams, and building one reporting layer that leadership actually trusts. ### What to look for in your first RevOps hire Look for a strategic generalist. This person should feel confident owning CRM hygiene on Monday and sit in the room when GTM strategy is being set on Tuesday. The biggest mistake at this stage is hiring a systems administrator when you need an operator. Tactics can be learned, but strategic thinking can't be trained in. ### Build for scale, not for show Your core stack at Series B should be CRM + RevOps automation + a CS platform. Every tool you add beyond that needs a documented owner, a clear use case, and a defined integration. Addition without integration is just expensive noise. ### Clean handoff data is what makes AI actionable As you continue to grow, the revenue data layer starts connecting GTM motions, including who touched a lead, when, and what happened next. That handoff data is what allows AI to help your team identify where deals are stalling and where follow-up is falling through. It gives your first RevOps hire high-impact suggestions to protect your pipeline. ## Stage 3 | Series B to C: Prepare for market expansion _RevOps Owner(s): Expands to a team of 2–4 specialists_ Getting from $10M to $50M requires multiple ICPs and motions, and a single one won't carry you there. This is where many companies lose steam. You need segmented data to iterate on new markets and channels without degrading what's already working. Stage three usually sees roles begin to split into specializations. Sales Ops, Marketing Ops, CS Ops begin to tie forecasting to board packages and investor reporting. The question is no longer "are we aligned?" It's "where are we losing revenue, and why?" **Pro tip**: At this stage, most companies discover that their funnel looks healthy at the top and disappointing at the bottom. The delta lives in conversion leakage, where deals stall between stages, poor handoffs eroding CS expansion, and forecast inaccuracy that makes planning unreliable. ### The #1 priority is auditing your funnel for conversion drop-offs Build a structured feedback loop from CS to sales so expansion and retention data flows back into how you qualify and close. This is also the stage where your ICP needs to move from a static definition to something your whole GTM motion is actually running on. The companies that get this right treat ICP as a living system, not a document. Growing ARR is important, but protecting [NRR is what will strengthen your pipeline](/blog/the-bowtie-model). In fact, it’s essential to protect NRR, and aggressively. Ultimately, you can acquire customers all day, but if your existing base is shrinking, then you're filling a leaky bucket. The goal is [net revenue retention above 100%](https://stripe.com/en-ca/resources/more/net-revenue-retention) (and ideally 120–140%), so that a meaningful portion of net new revenue comes from expansion _and_ acquisition. At Series C, investors stop accepting "we think" and start demanding "we know." RevOps is what turns anecdotal pipeline updates into concrete conversion rates, velocity data, and forecast accuracy. ### Structure your team for healthy growth [The right leaders for this stage](/blog/how-to-hire-exceptional-leaders) make everyone around them better. Decide between a centralized RevOps team and embedded ops specialists early. Changing this later is disruptive and expensive. **Pro tip:** This is also the stage where RevOps needs a direct line to Finance. Revenue planning, headcount modeling, and territory design all live at that intersection. ### Don't migrate your CRM…yet This is when CRM migration conversations start happening. In most cases, optimizing your existing CRM is smarter than rebuilding. Migration costs are almost always underestimated. Revenue intelligence tools start to earn their keep here, particularly for forecasting accuracy and pipeline inspection. ### AI surfaces the leakage your funnel reports can't see Conversion leakage rarely shows up cleanly in a dashboard. A structured revenue data layer gives AI the full picture, including deal history, handoff timing, CS health signals. This way, it can identify exactly where revenue is slipping and why, rather than surfacing a drop-off you still have to diagnose manually. ## Stage 4 | Series E+ and Enterprise: Run RevOps as a revenue system RevOps is a fully specialized organization of Forecasting Analysts, Systems Architects, Enablement Program Managers. AI-assisted forecasting and revenue intelligence are standard. RevOps is a strategic peer to Finance, not a downstream support function. ### The #1 priority is moving from reactive reporting to proactive revenue signals The best enterprise RevOps teams are surfacing what's about to happen and giving GTM leaders enough lead time to act on it. Multi-touch attribution across complex, multi-channel motions is expected. Manual reporting is a solved problem. ### Expand your AI infrastructure This is where it gets interesting. AI is reshaping how revenue is planned, modeled, and acted on in real time. The teams investing in AI governance and automation infrastructure now are building a compounding advantage for fundamentally better decision-making loops between data, strategy, and execution. ### AI can model revenue decisions before you make them At this stage, the revenue data layer is deep enough to do something earlier stages can't: run scenarios. Before committing to a new territory, a headcount change, or a pricing move, AI can model the likely revenue impact across every GTM motion. The teams building this capability now are making fewer mistakes and compressing the time between insight and decision. ### The persistent challenge It’s about managing the legacy complexity that accumulated on the way here. Multi-CRM instances, fragmented data models, and entrenched processes that nobody can explain but everyone is afraid to change. Enterprise RevOps leaders spend as much time managing technical and organizational debt as they do building forward. ## What to do right now (TL;DR) Most teams, when they read this honestly, find themselves operating one stage behind where they thought they were. That's not a failure. It's the most useful input you can have. Here's where to start. - **If you're at Stage 1 (Pre-Seed to Series A):** Don't hire before you build the foundation. Audit your CRM setup, define your revenue taxonomy, and establish one shared North Star metric across your GTM team. Every hour you spend getting this right now saves months of painful retrofitting later. The data structure you build today is the ceiling for everything that follows. - **If you're at Stage 2 (Series B):** Map every GTM handoff from first touch to CS onboarding and find where leads are falling through. If you haven’t yet, build a [single reporting layer](/blog/product-spotlight-january-2026), or one dashboard that Marketing, Sales, and CS all look at in the same meeting. If your leadership team is still arguing about whose numbers are right, that's the problem to solve before anything else. - **If you're at Stage 3 (Series C–D):** Run a pipeline velocity audit. Stage by stage, find where deals are slowing down or dropping out and quantify the revenue impact. Then build the CS–Sales feedback loop that most companies skip. The insight that comes back from retention and expansion is some of the most valuable signals you have for improving how you qualify and close new business. - **If you're at Stage 4 (Series E+):** The infrastructure work is largely done. The question now is whether your RevOps function is running reactively or proactively. Evaluate your AI readiness, identify where legacy complexity is degrading forecast accuracy. Finally, ask honestly whether RevOps has a genuine seat at the strategic table, or whether it's still being treated as a reporting and ticketing function. ## FAQ ### What is the difference between RevOps at a startup and at an enterprise? At a startup, RevOps is one person building the foundation. At an enterprise, it's a specialized team of 5–10+ running dedicated functions across process, systems, data, and enablement. The focus also shifts. Early-stage RevOps is about establishing alignment, while enterprise RevOps is about driving predictable revenue and proactive revenue signals. ### How many people should be on a RevOps team? AI is changing this question faster than any benchmark can keep up with. Teams with a well-structured revenue data layer are doing more with fewer people, so headcount alone is the wrong frame. That said, a general rule of thumb is a 50-person GTM team typically needs 2–3 RevOps specialists; a 300-person org needs 5–10+. Structure matters as much as headcount, and the right design depends on whether you centralize or embed ops by function. ### What is the difference between Sales Ops and RevOps? Sales Ops focuses specifically on supporting the sales team with forecasting, territory design, quota setting, and CRM hygiene. RevOps spans the entire revenue function: marketing, sales, and customer success under one operational framework. Sales Ops is often a function within RevOps at scale. At earlier stages, one person typically covers both. ### Should RevOps report to the CRO or the CEO? At Series A–B, RevOps often reports to the Head of Sales or CRO by necessity. But as the function matures, reporting to the CEO or CFO gives RevOps the cross-functional authority it needs. If RevOps reports only to Sales, it struggles to hold Marketing and CS to shared accountability. ### When does RevOps need a direct line to Finance? The org chart matters less than whether RevOps and Finance are working from the same numbers. Reporting lines vary significantly by company, and increasingly RevOps rolls into the CRO as revenue strategy becomes more central to GTM execution. What doesn't change: revenue planning, headcount modeling, and territory design all require Finance alignment at some point, regardless of structure. ### What's the right way to test a new ICP without corrupting existing metrics? Never blend a new ICP or motion into your core GTM data until it has validated unit economics. A practical indicator of when to keep things separate: if dedicated budget is being spent on a specific ICP, it warrants its own pipeline, conversion tracking, and reporting from the start. Blending prematurely makes your core business look like it's deteriorating when it isn't. ### How do you know if your CRM data is good enough to act on? If your team is making decisions based on gut feel rather than pipeline data, the data isn't good enough. Here are some practical tests: Can you pull accurate conversion rates at every funnel stage? Can you forecast within 10–15% accuracy? Can Marketing and Sales agree on the same lead numbers in the same meeting? If not, data quality is the bottleneck. --- --- title: 20 AI revenue agent use cases every GTM team should be running description: "Over the past year we mapped the revenue problems GTM teams bring to AI agents most often: pipeline gaps that hide until two weeks before quarter end, forecasts built on CRM stages nobody updated, churn signals buried in transcripts nobody re-reads. This is the complete reference for all 20 use cases: what each agent analyzes, which metric it actually moves, and what lands in your inbox when it runs." canonical: "https://vasco.app/blog/ai-revenue-agents-gtm-use-cases" date: "2026-03-03T00:00:00.000Z" authors: - Alec Oghassabian jobTitle: RevOps Expert readingTimeMinutes: 7 contentType: article intent: strategy-insights pillar: "AI in GTM & RevOps" audiences: - revops - cros - start-ups - scale-ups - fractional - founders --- # 20 AI revenue agent use cases every GTM team should be running _Most AI agents running on raw CRM data are confidently wrong. Here are 20 revenue agent use cases built on verified data, with cadences, inputs, and real outputs for every one._ Here is what nobody tells you when you connect Claude to your CRM: it does not say "I'm not sure." It says "here is your pipeline summary" — and it is wrong in a way that looks exactly like being right. [We tested it](https://vasco.app/claude-case-study). Claude with raw CRM access answered five standard revenue questions correctly 52% of the time. With a clean context graph underneath it, accuracy jumped to 98%. The gap is not the AI. It is the data the AI is reasoning on. This matters because most revenue teams are now deploying AI agents on top of CRM data that has never been reconciled, against ICP definitions that have not been updated since last year's planning offsite, without plan targets loaded, without outcome history attached. The summaries look clean. The confidence is high. The numbers are fiction. The teams getting this right are not doing anything exotic. They are running agents on a structured foundation — reconciled identities, enforced definitions, loaded targets, outcome memory — and they are running those agents on a cadence. Not as one-off experiments. As operational infrastructure. What follows is the complete map of 20 AI revenue agent use cases we have identified across GTM teams, organized by the job they do. Each entry covers what the agent analyzes, which metric it actually moves, and what it produces when it runs correctly. The use cases are universal. The foundation is what determines whether the output is trustworthy. ## What is an AI revenue agent? An AI revenue agent is an automated analysis system that connects to your CRM, call transcripts, billing data, and other revenue sources to surface insights, flag risks, and produce outputs that would otherwise require hours of manual work. Unlike dashboards, which show you what happened, AI revenue agents tell you what to do about it, and when. The most effective revenue agents run on a structured data layer: a reconciled, definition-aligned foundation that ensures the agent is reasoning on verified numbers rather than raw CRM fields. Without that foundation, even well-designed revenue agents produce confident, detailed, plausible-looking answers that are wrong. ## AI revenue agents for pipeline and forecasting The pipeline agents, forecast agents, and deal intelligence agents that move your number before the quarter ends. ### 01. Pipeline Health and Coverage Agent ![Revenue health agent](https://cdn.sanity.io/images/ys8gstp8/production/2e3ebe8b2cf9e6a254cce08e3df52d0dc2ccf8ae-450x600.png?w=1600&fit=max&auto=format) Your pipeline coverage ratio is probably lying to you. Stale deals that have not moved in months inflate it, creating a false sense of security until someone does the math two weeks before quarter end and finds a gap that cannot be closed in time. This agent does that math every Monday so you are not the last person in the room to know you are exposed. **For:** CRO, RevOps **Cadence:** Weekly **What it analyzes:** Pipeline by stage, revenue targets, average sales cycle length, historical win rates, and close date history. Segments coverage by motion, segment, and time horizon. **Impact on GTM:** Pipeline coverage ratio accuracy. Teams routinely overestimate coverage because stale deals inflate the number. This agent strips pipeline that has aged past the average sales cycle and recalculates real coverage, which changes the urgency of the sourcing conversation entirely. **Example outputs:** A segment-by-segment coverage report showing where you are on track versus exposed. Stale deals flagged and excluded from the ratio. The exact weekly pipeline creation rate needed per motion to hit quarter-end coverage. Next-quarter pipeline gap surfaced with enough runway to act on it. ### 02. Pipeline Review Agent The weekly pipeline meeting takes two hours because nobody did the prep. Deals get reviewed in CRM order instead of urgency order. The same perpetual slippers surface every week without a verdict. Everyone leaves with a vague sense of what needs to happen and nobody with a specific commitment. This AI pipeline review agent does the analytical pre-work so the meeting runs in 20 minutes and ends with an action list that has actual owners on it. **For:** CRO, VP of Sales **Cadence:** Weekly **What it analyzes:** Recent call transcripts, champion status, scheduled next steps, close date history, and CRO targets. Cross-references conversation content against deal stage and urgency. **Impact on GTM:** Sales meeting quality and deal velocity. Pipeline reviews fail when no one has done the analytical pre-work. This agent does it, so the meeting is about decisions, not about catching up. **Example outputs:** Deal cards for the top accounts with health score and current status. A specific discussion question per deal drawn from the actual last conversation, not a generic prompt. A forced verdict on every perpetual slipper: keep and commit, move to next quarter, or mark lost. A named action list with owners and deadlines captured before the call ends. ### 03. Deal Intelligence Agent ![Deal intelligence agent](https://cdn.sanity.io/images/ys8gstp8/production/d1d1cc12405ffe709408d15d40cd8aaf1e2d73bd-450x600.png?w=1600&fit=max&auto=format) Most deals do not die suddenly. They stall gradually, and the warning signs were there weeks before anyone flagged them. By the time the rep updates the CRM, the stage change has already happened and the window to course-correct has closed. This AI deal intelligence agent reads the behavioral signals before the stage changes, while there is still something to do about it. **For:** VP of Sales, Sales reps **Cadence:** Weekly, per deal review **What it analyzes:** Won and lost deal patterns from your own history, call transcripts, activity logs, stakeholder engagement, and stage velocity. Scores every active deal in real time against your historical outcomes. **Impact on GTM:** Win rate and deal slippage prevention. Risk surfaces before a stage change does, not after. The primary output per deal is one specific action for this week, not a health score to file away. **Example outputs:** A ranked deal list by risk score. Deals showing the same behavioral patterns that preceded your own historical stalls, flagged before the rep notices them. Champions who have not been engaged in weeks, with the last conversation topic noted. The specific point in each deal where velocity first diverged from similar won deals. ### 04. Win/Loss Analysis Agent ![Win / Loss Agent](https://cdn.sanity.io/images/ys8gstp8/production/40e234fce9c1a99b91c55c241716eeeef0748456-450x600.png?w=1600&fit=max&auto=format) Most revenue post-mortems are a polite fiction. The deal lost because of "pricing" or "timing" or "they went with a competitor." Those are symptoms, not causes. This win/loss analysis agent synthesizes outcomes, call transcripts, rep behavior, and competitive signals into a quarterly diagnosis that separates what actually drove the result from what everyone assumed drove it — which are rarely the same thing. **For:** CRO, VP of Sales, RevOps **Cadence:** Quarterly **What it analyzes:** Closed Won and Lost outcomes, call transcripts, competitor mentions, rep activity data, and stage drop-off rates. Synthesizes across multiple quarters to surface win/loss patterns at scale. **Impact on GTM:** Win rate improvement and ICP targeting accuracy. A targeting problem and a competitive problem look identical in a quarterly miss and require completely different responses. This agent makes that separation so you fix the right thing. **Example outputs:** Root causes behind win rate changes, separated from surface-level symptoms. Stalled pipeline traced back to specific process gaps running undetected for quarters. Objections classified as handleable (a coaching and enablement opportunity) versus structural (a product, pricing, or positioning problem that needs a different escalation path). Competitive intelligence patterns in lost deals, including emerging competitors appearing for the first time. ### 05. Sales Forecast Agent The sales forecast is wrong because reps are optimists and CRM deal stages do not capture what was actually said on the last call. A deal sits in Commit. The CFO told the rep "we are pausing until Q3." Nobody updated the stage. Nobody adjusted the number. The board gets a forecast built on fiction. This AI forecasting agent reads the transcripts and applies signal to every Commit and Best Case deal before the number gets published. **For:** CRO, RevOps **Cadence:** Weekly **What it analyzes:** Pipeline by stage, call transcripts, historical close rates by rep and segment, deal push history, and revenue plan by motion. Applies conversation intelligence to override optimistic CRM stage entries. **Impact on GTM:** Revenue forecast accuracy. The classic forecast failure mode is a Commit-stage deal where the last conversation contained obvious risk the CRM never captured. This agent reads the transcript, catches the signal, and adjusts. **Example outputs:** Bottom-up revenue forecast split by motion and by new business versus expansion. Commit-stage deals with unresolved objections or negative sentiment automatically downgraded with the evidence cited. Best Case deals with strong champion language promoted to Commit. The specific pipeline risk behind any forecast gap, categorized as a volume problem, a quality problem, or a velocity problem. ## AI revenue agents for demand generation and ICP validation The ICP agents, attribution agents, and GTM alignment agents that fill the top of the funnel with the right accounts. ### 06. ICP and TAM Discovery Agent Ask five people on your revenue team what your ICP is and you will get five different answers. That is not a communication problem. It is a data problem. Most ICP definitions were written in a Notion doc during a planning offsite, blessed by leadership, and never touched again. This ICP discovery agent rebuilds the ideal customer profile from what actually closed over the last four-plus quarters, then sizes the addressable market you are genuinely winning in. **For:** CMO, RevOps **Cadence:** On demand, quarterly **What it analyzes:** Closed Won and Lost data across four or more quarters, firmographic fields, win rates by cohort, ACV by segment, and NRR by customer. Validates the ICP against actual deal outcomes rather than planning assumptions. **Impact on GTM:** Pipeline quality and outbound conversion rate. An ICP built from opinions generates pipeline volume. An ICP built from outcomes generates pipeline that closes. This agent changes what your team targets, scores, and sequences. **Example outputs:** The firmographic profile that most predicts a win in your business, validated against real deal outcomes rather than assumptions. Segments generating high pipeline volume but low win rates, flagged for deprioritisation with the wasted effort quantified. A bootstrapped ICP if none is configured, built from deal data with the gaps made explicit. TAM sized per validated segment so territory planning has a real number to work from. ### 07. Channel Attribution Agent Last-touch attribution is a comfortable lie. It credits whoever was standing closest to the deal when it closed and quietly ignores every channel that built the relationship, educated the buyer, and kept the deal alive through months of evaluation. Those channels keep getting underfunded every planning cycle because they are invisible in the model. This marketing attribution agent runs first-touch, last-touch, and multi-touch simultaneously so you can see where the three models diverge, because that divergence is exactly where the real budget decisions are hiding. **For:** CMO, RevOps **Cadence:** Monthly, quarterly **What it analyzes:** Lead source fields, multi-touch history, win rate by channel, ICP fit per deal, and campaign data. Runs all three attribution models in parallel across every active channel. **Impact on GTM:** Marketing budget allocation accuracy and pipeline source quality. Channels doing meaningful mid-funnel work are systematically undercredited by single-touch attribution models, which means the most important parts of the funnel get cut first. **Example outputs:** A side-by-side comparison of first-touch, last-touch, and multi-touch rankings for every active channel. Channels appearing frequently at Stage 2 and Stage 3 that are completely invisible in last-touch reports. Dark pipeline: deals with no attribution at all, with the specific tracking gaps identified. A ranked channel investment recommendation with win rate and ICP fit attached to each source rather than just volume. ### 08. Sales and Marketing Alignment Agent Marketing says the leads are good. Sales says the leads are bad. Both teams have been having this argument for years and it never resolves, because nobody has clean shared data on who is actually right. This sales and marketing alignment agent ends the argument by separating MQL rejection reasons into two explicit buckets: marketing's problem and sales' problem. No gray area. Just accountability. ** For:** CMO, VP of Sales, RevOps **Cadence:** Weekly **What it analyzes:** MQL rejection codes, lead response times, ICP fit scores of MQLs, pipeline win rate by source, and SLA configuration. Separates the lead quality question into two distinct accountability buckets so no one can claim the other side is the problem. **Impact on GTM:** MQL-to-opportunity conversion rate and sales and marketing alignment. The lead quality debate loops indefinitely when both teams are working from different numbers. This agent gives both teams the same numbers so the conversation can move to solutions. **Example outputs:** MQL rejection reasons separated into marketing's to fix and sales' to fix, no gray area. ICP-fit accounts that received marketing touches but no sales follow-up, with the pipeline opportunity quantified. Reps ranked by SLA violation frequency with the conversion rate impact of late follow-up shown alongside. ### 09. GTM Alignment Audit Agent Every GTM team thinks it is aligned. The strategy deck says one thing. The data shows another. Nobody has the time or the mandate to reconcile them, so the misalignment compounds quietly until it shows up as a revenue miss at the end of the quarter. This GTM alignment agent runs the full audit across five dimensions and gives every finding the same structure: the strategy says X, the data shows Y, the gap is Z, and here is who owns the fix. **For:** CRO, RevOps **Cadence:** Quarterly **What it analyzes:** Org definitions, pipeline by motion, handoff SLA data, rep prospecting logs, and messaging patterns across won and lost deals. Audits across ICP targeting, motion execution, segment coverage, messaging, and handoff quality simultaneously. **Impact on GTM:** Revenue predictability and cross-functional accountability. Misalignment is the most underdiagnosed revenue problem in B2B. By the time it surfaces in a quarterly number, it has usually been running for months. This agent finds it while there is still something to do about it. **Example outputs:** Every finding structured as: the strategy says X, the data shows Y, the gap is Z. Exactly three friction points with a named owner, a specific fix, a timeline, and the leading indicator that will confirm it is working. Missing definitions in the org settings flagged as root causes in their own right, because you cannot align a GTM team on a concept that has never been formally defined. ## AI revenue agents for customer retention and expansion The churn prediction agents, health score agents, and expansion agents that protect and grow the revenue you have already earned. ### 10. Customer Health Score Agent By the time a customer tells you they are unhappy, you have already missed your window. The signals were there weeks earlier in a support ticket nobody escalated, a QBR they declined to attend, a steady drop in product usage, a slightly cooler tone on the last call. This AI customer health score agent reads all five dimensions of account health simultaneously and flags what is drifting before it tips into at-risk, when the save rate is still high and the cost of intervention is still low. **For:** Head of CS **Cadence:** Weekly **What it analyzes:** Product usage data, NPS and CSAT scores, support ticket history, call sentiment, and payment history. Scores every customer account across five health dimensions simultaneously. **Impact on GTM:** Net revenue retention. The teams with the strongest NRR are not the ones with the most CS headcount. They are the ones that catch account drift before it becomes renewal risk. Drifting accounts are where the save rate is highest and the intervention is easiest. **Example outputs:** Every active customer scored across usage, engagement, support, sentiment, and commercial health in one unified view. Drifting accounts surfaced before they reach at-risk status. Renewal risk bucketed by time window with aggregate ARR per bucket so CS prioritization has a clear order. A specific next action per at-risk account. ### 11. Churn Prediction Agent ![Churn prediction agent](https://cdn.sanity.io/images/ys8gstp8/production/b03acfae860bd3ab8adb8afc499a08e40c57dd78-450x600.png?w=1600&fit=max&auto=format) Churn does not announce itself in a renewal conversation. It shows up six months earlier in a throwaway comment on a support call, a billing dispute nobody escalated, or three weeks of silence from a champion who used to respond within the hour. Most teams only discover these signals when CS reviews a transcript during the renewal cycle, which is far too late to change the outcome. This AI churn prediction agent scans every customer-facing communication from the past seven days and catches those signals while there is still time to act. **For:** Head of CS **Cadence:** Weekly **What it analyzes:** Inbound customer emails and call transcripts from the past seven days, billing events, support escalations, and prior week flags. Scans for six churn signal types: cancel intent, billing disputes, product frustration, service frustration, resolution time complaints, and negative sentiment shift. **Impact on GTM:** Logo retention and net revenue retention. Churn signals appear weeks or months before a renewal conversation, but most CS teams only see them when they are already in the renewal cycle. This agent closes that gap. **Example outputs:** Every flagged communication ranked by signal strength and account risk. Repeat flags from the prior week tracked with escalation velocity so accounts moving from moderate to critical risk are visible before they become irreversible. A specific next action per flagged account: what to do, who should do it, and how urgent it is. ### 12. Expansion and Upsell Agent The best expansion opportunities do not announce themselves. A customer quietly crosses a usage threshold. A new department head gets onboarded. A funding round closes. These signals have a short shelf life: catch them at the right moment and the expansion conversation is natural and welcome; miss them by six weeks and the customer has already found a workaround or decided they do not need more. This AI expansion agent ranks every account by expansion readiness and assigns a specific named play before the window closes. **For:** Head of Sales, Head of CS **Cadence:** Monthly **What it analyzes:** Seat utilisation, feature adoption rates, call transcripts, firmographic triggers like funding rounds and headcount growth, and NRR by segment. Ranks every active customer by expansion readiness and assigns the right play to each one. **Impact on GTM:** NRR improvement and expansion pipeline generation. Most expansion conversations happen either too early, before the customer has proven value internally, or too late, when they have already solved the problem another way. This agent surfaces the right accounts at the right moment with a specific motion attached. **Example outputs:** Accounts ranked by expansion readiness with the specific signals driving each ranking made visible. An expansion signal age flag: a signal from six weeks ago is treated differently from one from last week, because timing determines whether the conversation lands well or awkwardly. A named play per account: capacity expansion, feature activation, tier upgrade, or growth play. NRR decomposed by segment to distinguish a volume problem from a motion problem. ### 13. Customer Quote and Ambassador Agent Your best case studies are sitting in call transcripts nobody has read twice. A customer said something genuinely specific about the outcome they got, it went into a recording, and now the CS team is going to struggle to remember it when the sales team asks for a reference next month. This AI ambassador agent finds those moments systematically, ranks accounts by advocacy potential, and gives CS a specific action so the ask actually gets made before the moment has passed. **For:** CMO, Head of CS **Cadence:** Monthly **What it analyzes:** Call transcripts, email threads, outcome language signals, account health status, and escalation history. Surfaces genuine positive sentiment tied to specific business outcomes rather than generic NPS scores. **Impact on GTM:** Reference program quality, case study pipeline volume, and sales cycle velocity. Deals close faster when there is a credible, outcome-specific reference available. Most teams do not have a systematic way to find and activate those references. This agent builds the pipeline. **Example outputs:** Customers ranked by advocacy potential based on the specificity of outcomes mentioned, recency of positive signals, and the unprompted nature of the language. Verbatim quotes tied to business results, ready to use in case studies and sales conversations. A specific follow-up action per high-potential candidate so CS can warm the relationship and make the ask before the signal fades. ### AI revenue agents for rep coaching and enablement The coaching agents, messaging agents, and contribution agents that raise the performance of the entire team. ### 14. Rep Coaching Signal Agent ![](https://cdn.sanity.io/images/ys8gstp8/production/6eff12d042b8fb5ba709a2a2c005d2e6c5ba5f36-450x600.png?w=1600&fit=max&auto=format) The difference between a top-performing rep and a median rep is almost never talent. It is a handful of specific habits: when they multi-thread, how they run discovery, whether they confirm economic buyer engagement before sending a proposal. The problem is those habits are invisible in quota attainment data. They are in the transcripts. This AI sales coaching agent finds them and makes them specific enough to use in a 1:1 without the conversation feeling like a performance review. **For:** Head of Sales **Cadence:** Weekly **What it analyzes:** Call transcripts, win rate by rep, stage progression patterns, activity cadence, and qualification signal quality. Benchmarks every rep against top performer behaviors across multiple dimensions, not just outcome metrics. **Impact on GTM:** Win rate per rep, ramp time reduction, and team-wide performance uplift. The habits that separate top performers from median performers can be identified, documented, and coached. This agent makes that possible at scale. **Example outputs:** Anonymized tier maps showing where each rep sits relative to the team baseline across behavioral dimensions. Rep-level spotlight cards with one specific, evidence-based coaching observation per rep. Activity patterns of top performers that are invisible from quota attainment data alone. The exact behavioral deviation causing Stage 4 losses in reps who look fine on the pipeline report. ### 15. Messaging Coach Agent Your sales messaging was probably written by a product marketer based on what they thought buyers cared about. That is a reasonable starting point, but it is not the same as knowing which messages actually appear in won deals versus lost deals. This AI messaging coach agent scores every value proposition your team uses against real outcomes and tells you which ones to lead with, which to retire, and which objections are a coaching problem versus a product problem. **For:** Head of Sales, Enablement **Cadence:** Monthly **What it analyzes:** Transcripts from won and lost deals, objection patterns by persona and stage, and competitor mentions across the full deal history. Scores every value proposition by net performance: frequency in won deals minus frequency in lost deals. **Impact on GTM:** Win rate improvement and rep messaging consistency across the team. Most sales messaging is built from what the product team wrote. This agent builds it from what actually closes deals. Every verdict comes from outcome data, not opinion. **Example outputs:** A verdict per value proposition: lead with this, support only, or retire this. The discovery questions that most consistently surface urgency and get a champion to self-identify. Objections classified as handleable (coaching will move the needle) versus structural (product, pricing, or positioning problems that need a different escalation path). Value propositions appearing almost exclusively in won deals that were never formally documented or trained on. ### 16. Pipeline Contribution Agent Every QBR has the same argument: marketing says they sourced most of the pipeline, sales says the leads were low quality, and nobody can agree because nobody has win rate data attached to the volume data. This pipeline contribution agent gives every GTM function a defensible number with quality attached to it so the QBR conversation moves from debate to accountability. **For:** CRO, RevOps **Cadence:** Monthly, per QBR **What it analyzes:** Pipeline source fields, win rate by source, ICP fit scores of sourced deals, contribution targets, and AE self-sourcing data. Attaches quality context to every volume claim across every GTM function. **Impact on GTM:** QBR accountability and sourcing efficiency. Volume rankings without quality context mislead every QBR conversation. A channel that sources a lot of pipeline but closes at a low win rate is not a contribution. It is noise. This agent separates the two. **Example outputs:** Every GTM function's pipeline contribution with win rate and ICP fit attached, not just deal count or ARR volume. Reps with zero self-sourced pipeline identified by name with territory coverage context. The exact weekly creation rate required per function to hit quarter-end coverage. ## AI revenue agents for executive reporting and planning The forecast agents, board report agents, and planning agents that translate revenue data into boardroom-ready decisions. ### 17. CRO and CEO Brief Agent ![CRO/CEO Brief agent](https://cdn.sanity.io/images/ys8gstp8/production/5947a0766bc44e294c16866404e531d15846af68-450x600.png?w=1600&fit=max&auto=format) The Monday morning leadership meeting should not start with everyone catching up on what happened last week. It should start with three things that matter, each quantified in ARR dollars, with a clear owner and a specific question to ask. This AI executive brief agent produces that brief before the meeting starts so the first five minutes are spent on decisions and not on context-setting. ** For:** CEO, CRO **Cadence:** Weekly **What it analyzes:** Pipeline pacing data, Commit-stage call transcripts, CRO commitments from the prior week, rep attainment, and the previous week's brief. Identifies the three highest-priority issues by ARR impact. **Impact on GTM:** Executive decision speed and revenue accountability. The Monday morning leadership conversation should start from a shared factual baseline, not from memory and competing reports. This agent creates that baseline before the meeting starts. **Example outputs:** The three things that matter most this week, each quantified in ARR dollars rather than percentages, because percentages are information but dollars are decisions. The deals leadership should ask about by name, with a specific question for each one. CRO commitments versus actuals tracked week over week. A delta showing which flags improved, worsened, or are new since last week. ### 18. Board and Investor Report Agent ![Board and investor agent](https://cdn.sanity.io/images/ys8gstp8/production/19be10cde853232631697e12162549dacfecadbd-450x600.png?w=1600&fit=max&auto=format) Board prep takes weeks, involves too many people, and still produces a deck where someone in the room asks where a number came from and nobody is quite sure. This AI board reporting agent produces the full quarterly board pack in investor-grade language with every number traceable to source, so prep time collapses and the boardroom conversation is about the business rather than about reconciling spreadsheets. **For:** CEO, CRO **Cadence:** Quarterly **What it analyzes:** Full ARR waterfall data, NRR and GRR history across four quarters, win rate trends, customer concentration, and CAC payback data. Synthesizes the full revenue picture into investor-grade narrative. **Impact on GTM:** Board confidence, leadership credibility, and time-to-insight for quarterly reporting. Board prep is one of the highest-cost RevOps activities in time, in headcount, and in the metric inconsistencies it exposes at the worst possible moment. **Example outputs:** Full ARR waterfall with four-quarter trend data on NRR and GRR. Exactly three risks and three opportunities, forced prioritisation that signals management discipline to investors. A maximum three-item Ask of the Board. Every number traceable to source across CRM, billing, and product data so no follow-up spreadsheet is needed after the meeting. ## 19. Revenue target agent Most revenue targets are handed down from above and then disaggregated into components that do not quite add up. Pipeline targets get set without checking historical win rates. Headcount plans get built without accounting for ramp time. The model looks plausible on a slide and breaks down when it meets reality. This AI revenue planning agent reverses the process: it starts from the target and shows exactly what needs to be true across pipeline, conversion rates, and headcount for each of three scenarios. ** For:** CRO, RevOps **Cadence:** Quarterly, on demand **What it analyzes:** Revenue targets, historical win rates, ACV by motion, MQL-to-opportunity conversion rate, and quota per rep. Reverse-engineers the annual number into the specific inputs required to hit it. **Impact on GTM:** Revenue plan credibility and headcount justification. Plans built top-down and disaggregated rarely add up. This agent builds them from the target down with every assumption made visible, which changes the board conversation from "does this number feel right" to "here is what has to be true." **Example outputs:** The pipeline volume, lead volume, conversion rates, and headcount required to hit plan across conservative, base, and aggressive scenarios, with every assumption cited explicitly. The single highest-leverage variable identified so the prioritization conversation has a number to anchor on. ### 20. Capacity and hiring signal agent The most expensive hiring mistake in sales is not a bad hire. It is a late hire. A rep hired in August who needs four months to ramp does not contribute until December, which means Q4 is already compromised before you made the call. Most teams discover the gap in October, when the only options are bad ones. This AI capacity planning agent tells you the latest hire date per role to hit your targets, in May, when you can still do something about it. **For:** CRO, RevOps **Cadence:** Monthly **What it analyzes:** Headcount by ramp status, quota per role, ramp timelines, attrition history, and revenue targets. Models effective selling capacity by separating total headcount from productive headcount. **Impact on GTM:** Revenue predictability and hiring decision timing. A ramping rep contributes at a fraction of full productivity and should never be counted as a full seat in a capacity model. The teams that get capacity planning right treat it as a weekly exercise, not a quarterly one. **Example outputs:** Effective capacity versus total headcount, with ramping reps counted at their actual productivity percentage rather than as full contributors. The latest hire date per role to achieve full productivity by the quarter it is needed. The ARR cost of waiting one additional quarter to hire, expressed as a specific number. Which segment has the largest capacity gap and whether internal territory reallocation could bridge it before a new hire ramps. ## Where to start: browse the agent marketplace The 20 use cases in this guide are not hypothetical. They are the problems every revenue team is already living with: the pipeline review that eats two hours every Monday, the forecast nobody fully trusts, the churn signal buried in a support ticket from month three. The agents built to solve them exist right now, and most teams are still debating whether to start. Before you do, it is worth asking yourself a few honest questions: - Which pipeline, forecast, or retention problem is costing you the most this quarter? - Do you have a working ICP definition that agents can actually reason on, or is it still sitting in a Notion doc from last year's planning offsite? - If you ran one agent on a weekly cadence for a full quarter, which one would produce the finding your leadership team most needs to see? The agents in Vasco's marketplace are pre-built and ready to deploy against your CRM, your call transcripts, and your plan targets. No engineering. No months of setup. The first agent can be live in under 30 minutes. [Browse the agent marketplace](https://vasco.app) > ### ## FAQ ### What is an AI revenue agent? An AI revenue agent is an automated analysis system that connects to CRM, call transcripts, billing data, and other revenue sources to surface insights, flag risks, and produce actionable outputs. Unlike dashboards, revenue agents produce recommendations and specific next actions, not just historical summaries. ### Why do AI revenue agents give wrong answers on raw CRM data? Most CRMs contain unreconciled records, undefined terms, missing attribution, and no connection to plan targets or outcome history. When an AI agent reasons on this data, it produces confident, plausible-looking answers that are built on an inaccurate foundation. Tests comparing Claude on raw CRM data versus a clean context graph show accuracy rates of 52% versus 98% respectively on standard revenue questions. ### What data do AI revenue agents need to work correctly? At minimum: reconciled account and contact records, a defined ICP, consistent lifecycle stage mapping, and historical win/loss data. For transcript-dependent agents, call recordings connected to the relevant deals. For planning agents, loaded revenue targets and quota data. The more outcome history available, the sharper the pattern matching becomes over time. ### How often should you run revenue agents? Cadence depends on the use case. Pipeline Health, Pipeline Review, CRO Brief, and Churn Prediction all benefit from weekly runs. ICP Discovery, Win/Loss Analysis, and GTM Alignment Audit are typically quarterly. Forecast and Deal Intelligence run weekly at minimum. Running agents on a consistent cadence is what builds the outcome history that makes them progressively sharper. ### What is the difference between a revenue agent and a RevOps dashboard? A RevOps dashboard shows you what happened. A revenue agent tells you what to do about it, surfaces risks before they compound, flags the specific deal to ask about in Monday's meeting, and produces a named action list rather than a visualization. The output format is closer to a briefing document than a chart. ### Which AI revenue agent should a small RevOps team deploy first? Start with the agents that run on data you already have: Pipeline Health and Coverage, Pipeline Contribution, and the Sales and Marketing Feedback Loop. These require basic CRM data and produce credible findings on the first run. Build ICP definitions and clean lifecycle stages in week two. Then layer in the transcript-dependent agents — Messaging Coach, Rep Coaching Signal, Deal Intelligence — once the foundation is solid. --- --- title: "Reps vs. RevOps: The Framework for Freedom" description: "Most RevOps teams are built to manage systems. The best ones are built to serve people. This article spotlights a conversation between Justin Hudon (Head of Sales & CS, Vasco), and Steve Dinner (VP of Revenue Operations, Owner), exploring what it actually takes to build systems that bring out the best in sales talent." canonical: "https://vasco.app/blog/revops-empathy-system-design" date: "2026-03-03T00:00:00.000Z" authors: - Justin Hudon jobTitle: Head of Sales and Customer Success readingTimeMinutes: 3 contentType: article intent: strategy-insights pillar: "GTM & RevOps execution" --- # Reps vs. RevOps: The Framework for Freedom _How human-first RevOps scales with the right systems_ Justin Hudon, Head of Sales & CS at[ Vasco](https://vasco.app/), and Steve Dinner, VP of Revenue Operations at[ Owner](https://www.owner.com/) recently explored why RevOps must evolve beyond rigid infrastructure. They argued that when systems are built around people, they become collaborative frameworks. And by designing for the needs of the team, RevOps can uncover the unique revenue potential that only empowered sales talent can deliver. ## The cost of designing from distrust RevOps didn't earn its reputation for saying no by accident. It's what happens when systems are built to assume that people need to be controlled into doing the right thing. Steve Dinner, a former BDR turned RevOps leader, has seen this pattern play out across organizations: > "If your processes aren't empathetic to the reps — if your workflows don't believe that your reps want to do the right thing — then you've kind of already failed from the start." ‎ ‎ ‎ ‎ ‎ ‎The proof is in a simple experiment Steve ran early in his RevOps career. He added one metric to a dashboard his BDR team already checked every day: the percentage of calls that went to tier-one accounts, with no announcement or mandate. **Overnight, the team’s number of tier-one account calls went from 40% to 85% overnight.** All they needed was visibility into the right data. No incentive was needed, and that's the difference controlling systems miss. Trusting people with the right information produces results that mandates never could. ![](https://cdn.sanity.io/images/ys8gstp8/production/0e8f1e822a4f95c90832d831054c61f31e6d302a-5921x3947.jpg?w=1600&fit=max&auto=format) ## Performance problems are often system problems A rep navigating five open tabs, no clear priority, and a CRM built for a VP's reporting needs isn't underperforming out of laziness. Too often RevOps systems ask them to carry too much context before they've made a single call. The solution is to get close to the frontline through ride-alongs, listening sessions, and direct observation. This reveals what reports can't. Yes, the dashboard can show what happened. But what it doesn't show? The workaround someone built at 9am because the right path was too hard. Steve adds: > "You have to believe in the people on the team that you serve. No one's paying you to make beautiful technology or beautiful diagrams. They're paying you to help these folks sit there and call and get told no and help them get results." ‎‎ ‎ Holding the system to the same standard as the rep is what separates empathy-driven RevOps from just piling on processes. ## Freedom comes from standardizing the right things A good framework creates freedom by design. Make anything the system can own seamless or invisible. What remains is what only a human can do: the conversation, the relationship, the judgment call. ![](https://cdn.sanity.io/images/ys8gstp8/production/96a7d7e6fcf1bc2a1a4f9d755693635ba3ac98b6-779x519.png?w=1600&fit=max&auto=format) As Steve puts it: > "I'm looking to create within these frameworks as much room for creativity and personality to flourish." ‎ Once the framework is understood as a tool for protecting time and energy (rather than controlling behavior), resistance turns into ownership. ## Engineer for flow state Not long ago, Steve sat down to make calls himself, something he hadn't done in a minute. He found himself staring at the button: > "It had been a while, and I sat there and stared at that button. I realized it's more than just theory. It's more than just 'why wouldn't you sit here and press this 200 times?'" ‎ That moment is what empathetic system design starts from. So when Owner rebuilt their outbound engine, they asked one question: what does it take to be really successful and fulfilled in this role? The answer shaped everything about how their system was designed to: - **Prepare reps for every call: **Only the right information in the right order - **Maintain momentum: **Create the shortest possible gap between calls - **Improve call performance**: Group leads by similar attributes so reps get into a rhythm and improve faster - **Promote focus**: Have dedicated team calling blocks so everyone is focused at the same time - **Nurture talent**: Have managers in the room for quick coaching right after a hard no Whatever the activity, the question is the same. What does a focused, fulfilling version of this look like, and what does the system need to do to make that the default? ## Coach the process, not just the person An empathy framework also changes what RevOps hands to sales leaders as a coaching tool. When teams assume people want to do well, the first question shifts from "who's underperforming?" to "where is the process letting them down?" **Stage-level data makes the practical insights visible. **Rather than a vague conversation about numbers, leaders can point to exactly where things are breaking down and design around it. ![](https://cdn.sanity.io/images/ys8gstp8/production/43214ebecdb7a22c44831f24a43014d8fbf4ffaa-2626x792.png?w=1600&fit=max&auto=format) Justin describes what that looks like in practice: > "You can really just pinpoint exactly where your focus should be with that rep, or with that team, or with that channel. If your rep is taking longer between stage 2 and stage 3, what can you take from that specific rep and pass along to others?" ‎ When reps get[ visibility into what's working for their peers](/blog/product-spotlight-january-2026), they use it. That's why empathy-driven systems create the conditions for that success to spread. ## AI won't save a system built on distrust The AI boom has given RevOps a lot of new tools. Using them to add more dashboards, more tracking, and more enforcement points in the wrong direction. There are real people making decisions from carefully curated pipelines. RevOps' job is to give them clarity on the next best action, reduce the friction standing between them and that action, and build systems that remember so humans don't have to. Empathy isn't soft. It's the RevOps design principle that helps sales teams thrive. ## FAQ ### What is human-first RevOps? Human-first RevOps is an approach to revenue operations that designs systems around the needs and experience of sales reps rather than trying to control their behavior. Instead of adding friction and mandatory steps, it prioritizes empathy, trust, and removing unnecessary tasks so reps can focus on the work only they can do. ### Why do RevOps systems often fail sales reps? Most RevOps failures trace back to a foundational assumption that people need to be controlled into doing the right thing. This leads to over-engineered systems with required fields, approved bottlenecks, and dashboards built for leadership reporting rather than rep usability. The result is friction that slows reps down and obscures the fundamental performance issues, rather than the system itself. ### How does standardization create freedom for sales reps? When routine tasks are standardized, automated, or eliminated, reps get back the time and mental energy those tasks consumed. A system should own data entry, manual follow-up sequencing, or hunting through a CRM for the right account. The time they gain back is what uplifts the moments where creativity, judgment, and relationship-building happen. ### How should sales leaders use RevOps data for coaching? Rather than using data to identify underperformers, empathy-driven RevOps reframes the first question as "where is the process letting this person down?" Stage-level data lets leaders pinpoint exactly where deals stall, compare how reps move through each stage, and share what's working across the team. This ensures vague numbers in a meeting become a precise, actionable coaching opportunity. --- --- title: The RevOps operating rhythm description: "When each conversation has a clear frequency and scope, teams stop reconstructing what happened and start deciding what to do next. This guide lays out the five cadences every RevOps team needs: what to cover, who to involve, and what each one should produce." canonical: "https://vasco.app/blog/the-revops-operating-rythm" date: "2026-03-03T00:00:00.000Z" authors: - Alec Oghassabian jobTitle: RevOps Expert readingTimeMinutes: 7 contentType: guide intent: playbooks-methods pillar: "GTM & RevOps execution" audiences: - revops - cros - start-ups - scale-ups - fractional - founders --- # The RevOps operating rhythm _From daily signals to annual planning_ ## Get the guide RevOps sits at the intersection of sales, marketing, and CS. But without a **structured reporting cadence**, each function ends up operating on its own rhythm. By the time information reaches the right person, the window to act has usually passed. A well-designed operating cadence gives each conversation a clear purpose, the right audience, and a predictable frequency. The **right visibility, at the right level, at the right time**, so teams spend less time reconstructing what happened and more time deciding what to do next. ![Read the guide](https://cdn.sanity.io/images/ys8gstp8/production/b7b30db8546d0b9ce8eae4a4547b37ff81fc839c-1920x1080.png?w=1600&fit=max&auto=format) ## The five cadences covered in the guide - **Daily Signal** — An automated morning snapshot to stay close to actuals, pipeline movement, and SLA compliance across all GTM functions - **Weekly Business Reviews (WBRs)** — A structured pipeline review to surface what's moving, what's at risk, and what needs a decision before the week is out - **Monthly & Quarterly Business Reviews (MBRs & QBRs)** — A deeper look at full-funnel performance, conversion rates, and team results to measure, adjust, and reset direction - **Board Meetings** — A high-level view of business health, unit economics, and strategic priorities to earn trust and align on direction - **Annual Planning** — The process to build the forecast, set top-down targets, and distribute bottom-up quotas across every GTM function > When everyone sees the same numbers at the same time, you stop spending energy defending your version of the truth and start moving together. --- --- title: Context graphs for GTM teams description: "A context graph is a structured way to represent GTM data that preserves entities, relationships, and the context in which revenue decisions were made. Instead of reporting outcomes in isolation, it links performance to the sequence of events, signals, ownership changes, and conditions that produced them." canonical: "https://vasco.app/blog/context-graphs-for-gtm-teams" date: "2026-02-04T00:00:00.000Z" authors: - Stephane Bischoff jobTitle: Senior software engineer readingTimeMinutes: 6 contentType: article intent: foundations pillar: "AI in GTM & RevOps" audiences: - enterprise --- # Context graphs for GTM teams _Turning fragmented signals into explainable decision trails_ ### For GTM teams, this makes revenue explainable under scrutiny. Context graphs do not replace CRMs or BI tools. They add an explanatory layer that helps teams understand why metrics move, defend forecasts and reviews, and build structured decision trails that both humans and AI systems can reason about over time. > In one sentence: a context graph connects GTM outcomes to the people, events, definitions, and signals that produced them, so teams can explain why performance changed, not just report what changed. ## What is a context graph? A context graph is a structured way to represent GTM data that preserves not only entities (accounts, opportunities, activities, and people) and the relationships between them, but also the context that gives those relationships meaning. Unlike **traditional databases** that store facts, or **CRMs** that store current operational state, **context graphs **preserve how, when, and under what conditions decisions were made and outcomes occurred. Revenue is treated not as a static number tied to a deal, but as the result of a sequence of actions and signals that can be traced and explained. This distinction matters most when numbers must be defended rather than merely reported, such as in WBRs, forecasting calls, or board discussions. Dashboards can show what changed, and CRMs can enumerate the records involved, but the reasoning behind those changes is rarely carried forward with the numbers themselves. As data is aggregated or recalculated, context becomes fragmented across notes, tickets, and conversations, making it difficult to retrieve and reason about under scrutiny. Context graphs address this gap by preserving GTM activity as a sequenced decision trail. Over time, these trails form precedents that show how similar situations unfolded, which signals mattered, and which ones turned out to be noise. This is also what makes context graphs increasingly relevant as AI enters GTM workflows: they provide the structured context needed to explain outcomes and support better judgment. ## How context graphs work in a GTM environment In a GTM environment, context is derived from signals across the stack, including: - **CRM data** (accounts, opportunities, stage changes, ownership) - **Revenue and billing events** (invoices, expansions, renewals, delays) - **Sales and CS activities** (meetings, emails, call recordings) - **Customer communications** (Slack threads, email exchanges) - **Support signals** (tickets, escalations, product usage limits) - **Product and usage data** (feature adoption, activation events) - **Definitions and policies** (forecasting rules, lifecycle stages, approval thresholds) - **Internal Documents** (WBRs, post-mortems) ![](https://cdn.sanity.io/images/ys8gstp8/production/22d2edd6a400bf68555090243513e13d6c7bba0e-3840x2160.jpg?w=1600&fit=max&auto=format) A context graph organizes these signals across four critical dimensions: **Time and sequence.** Context graphs preserve the order in which things happened: when a deal entered a stage, when activity dropped, when ownership changed, or when a forecast was updated. Sequencing helps distinguish cause from coincidence. **Meaning and intent.** Not all signals carry the same weight. Context graphs capture what an event represents in the GTM process, such as a qualification step, a risk signal, a handoff, or a commitment. **Ownership and influence.** Revenue is shaped by people as much as by process. Context graphs track who acted on what and when, making it possible to understand how changes in ownership, coverage, or capacity influenced outcomes. **Provenance and confidence.** GTM data comes from multiple systems with different levels of reliability. Context graphs preserve where a signal originated and how much confidence it should carry, allowing teams to weigh recurring patterns differently from isolated anomalies. Together, these dimensions turn fragmented GTM data into a connected decision trail. ## A concrete example: what a context graph changes in practice Consider a common question: _“Why did pipeline drop 18% in EMEA this month?”_ A dashboard can show the drop. A CRM can list impacted opportunities. But the real answer usually requires stitching together multiple signals across systems and time. With a context graph, teams can trace the decision trail: - **Ownership change:** two enterprise reps moved territories, creating a temporary coverage gap - **Sequence shift:** activity dropped 12 days before stage conversion declined - **Segment composition:** SMB pipeline remained flat, while **enterprise pipeline fell by 31%** - **Support context:** escalations increased in the top 10 accounts, pulling CS capacity into firefighting - **Forecast exception:** a VP-level override excluded 3 deals due to procurement delays - **Confidence:** high, based on consistent signals and a repeated pattern observed in past quarters Instead of concluding that “pipeline dropped,” the narrative becomes: enterprise coverage shifted, activity fell, conversion followed, and forecast definitions excluded delayed deals. This was not a broad demand collapse, but a sequence of operational and definitional factors that interacted over time. That’s the difference between reporting and explanation. ## CRM, BI, and context graphs: different roles CRMs, BI tools, and context graphs serve different purposes for GTM teams. **CRMs are systems of record.** They capture the current operational state of the business and are optimized for execution and coordination. When information is updated or overwritten, much of the decision context that led to that state is lost. **BI tools are systems of reporting. **They aggregate data across sources to show what happened and how performance changed over time. While dashboards are effective for monitoring trends, aggregation flattens sequences and relationships, which limits explanation. **Context graphs act as systems of explanation. **They preserve how GTM signals relate to each other over time, making it possible to connect metrics back to the actions, conditions, and decisions that produced them. > These tools are complementary. CRMs support execution. BI tools support visibility. Context graphs support reasoning. ## Where does a context graph fit in the GTM tech stack? From a technical standpoint, a context graph is not a single tool or product. It can exist as a logical structure rather than a visible system, modeled in a data warehouse, implemented in an internal application, or supported by an external platform. In practice, this often starts with aggregating GTM data from systems such as CRM, billing, product usage, support, or call recordings, and preserving the relationships and timing between signals. Some platforms, including tools like Vasco, operate in this layer by unifying GTM data and making revenue signals easier to analyze and explain, without requiring teams to manage an explicit graph themselves. ![](https://cdn.sanity.io/images/ys8gstp8/production/26b5c8997c90a18a57dad0f6fe87090bf991bd8d-3840x2160.jpg?w=1600&fit=max&auto=format) ## ## Are context graphs realistic for seed to Series B companies? Yes. Context graphs are realistic for Seed to Series B companies when approached as an evolving capability rather than a large, upfront infrastructure project. Early-stage teams benefit from establishing their revenue engine early, but they do not need a custom, engineer-heavy system to take a first step toward a context graph. The initial value comes from structuring GTM data and decisions consistently, not from building a dedicated graph architecture from day one. In practice, this starts with foundational work teams should do early, such as defining lifecycle stages, aligning on core GTM concepts such as the bowtie model, and connecting signals across systems like CRM, billing, product usage, or customer interactions. Platforms like Vasco operate at this level by unifying GTM data and preserving relationships between signals, creating a foundation for contextual reasoning without added complexity**.** This is where starting early matters. As forecasts, Weekly Business Reviews, and post-mortems are reviewed through a shared structure, context compounds. Each decision becomes a precedent, making future reasoning faster and more reliable. Seen this way, context graphs are less about technical maturity and more about operational discipline. ## From explanation to AI support Context graphs do not automate decisions on their own. Their primary value is explanatory: making it possible to trace how GTM outcomes emerged from a sequence of signals and actions. As similar situations repeat, context graphs accumulate **structured precedents**. This gives AI systems a grounded way to recognize familiar situations as they unfold. Using this context, AI agents can: - surface likely drivers behind performance changes - identify which signals historically mattered most in similar situations - suggest responses that previously led to better outcomes - flag early patterns that, in past episodes, preceded risk or opportunity Because these suggestions are anchored in **inspectable decision trails and prior context**, they are easier to evaluate, challenge, and trust. Over time, well-understood and low-risk decisions can move from recommendation to partial or conditional automation. For GTM teams, the real leverage is not fully autonomous decision-making, but AI that reduces repetitive analysis, surfaces relevant precedents, and supports faster, higher-quality decisions — while keeping humans in the loop where judgment matters most. ## Key takeaways - Context graphs explain why GTM metrics move, not just what happened - They add an explanatory layer on top of CRM and BI tools - Time, meaning, ownership, and provenance are critical to GTM reasoning - Context graphs can start narrow and compound value over time - They create the foundation for explainable, trustworthy AI in GTM ## FAQ ### Can a CRM serve as a context graph? Not fully. CRMs store activities (emails, conversation, meetings), current state and outcomes (such as stage, amount, or owner), but they rarely preserve the decision context, exceptions, approvals, and cross-system reasoning needed to explain why outcomes happened. ### How is a context graph different from a data warehouse? A data warehouse centralizes and stores GTM data for analysis and reporting. A context graph preserves how signals, events, and decisions relate over time, making it possible to explain why outcomes occurred, not just analyze aggregated data. ### Is a context graph a type of knowledge graph? No. Knowledge graphs focus on connecting entities and relationships. Context graphs go further by preserving timing, intent, provenance, confidence, and decision sequences, which are critical for reasoning about GTM performance. ### What are the limitations of context graphs? They only capture what is recorded. Informal or offline interactions need to be structured to be included, and the approach requires discipline in data modeling and definitions. --- --- title: How to build revenue agents with Claude x Vasco description: "The gap between a Claude prototype and a production revenue agent is smaller than you think. This guide closes that gap with the exact MCP connection, a 5-component prompt structure you can copy today, and 20 reasoning agents built to compound. For builders who are done experimenting and ready to ship." canonical: "https://vasco.app/blog/build-revenue-agents-Claude-Vasco" date: "2026-02-03T00:00:00.000Z" authors: - Guillaume Jacquet jobTitle: "CEO & co-founder" readingTimeMinutes: 9 contentType: guide intent: playbooks-methods pillar: "AI in GTM & RevOps" audiences: - revops - cros - founders - fractional - start-ups - scale-ups - enterprise --- # How to build revenue agents with Claude x Vasco _The builder’s guide to achieving high-context agents in minutes_ > If you're a RevOps leader, CRO, or founder at a B2B SaaS company who's already deploying agents and wondering why the ROI hasn't shown up yet, this guide was written for you. ## What you’ll find in this guide [This guide](https://share.hsforms.com/2dR0nbJEnQ2-wf4UdFWjgzgdb5kh) is designed to move you from a Claude prototype that demos well to a revenue agent fleet you can trust in a board meeting. - **Why Claude needs context:** What has to be in place before any agent you build will work in production - **How to connect Vasco to Claude:** The exact commands, the right sequence, and what you get that a raw CRM connection doesn't - **Build, run, and scale:** The step-by-step prompt structure, the full agent fleet, and how to go from one running agent to twenty [![](https://cdn.sanity.io/images/ys8gstp8/production/466ed31973259a0c2870f994e9084ac1b7c1d93b-1920x1080.png?w=1600&fit=max&auto=format)](https://share.hsforms.com/2dR0nbJEnQ2-wf4UdFWjgzgdb5kh) ## The 2026 priority: Get out of context debt Most teams are past the "should we use AI" conversation. The new question is harder: how do you build an agent that doesn't just retrieve information but actually reasons on it, connecting a billing signal to a Gong call to a stage change to a quota gap and telling you what it means? The answer isn't a better prompt. It's a foundation that encodes your definitions, resolves your identities, places your conversations correctly, and gives Claude something worth reasoning from. Once that's in place, the quality of what you can build changes entirely. Agents stop summarizing and start thinking. ## The five components every reasoning agent needs Most agent prompts fail in production for the same reason. They skip the structure that makes Claude consistent. Here is what every agent you build needs, in order. ### Component 1: Role and job Tell Claude exactly who it is, what it produces, and what done looks like. Not "you are a helpful assistant." "You are a WBR analyst. Produce the weekly business review for the CRO. Direct. Quantified. No softening." The more specific the role, the more consistent the output across every run. ### Component 2: Organization context Before any analysis runs, the agent calls Vasco's Organization Settings to load your company name, fiscal year, ICP definitions, segment thresholds, and motion definitions. This is the step that makes a generic template produce company-specific output. Without it, Claude reasons from its own defaults, not yours. ### Component 3: Planning guidelines Tell the agent how to sequence its work, what depends on what, and how to handle missing configuration. The rule that matters most: missing config is a note, not a blocker. The agent never stops because a definition is not set. It documents the gap and continues. ### Component 4: The data chain This is the component most builders skip. Before generating a single sentence of output, the agent calls three Vasco tools in sequence: the Metric Analyst for quantitative data, the Context Analyst for qualitative signals from call transcripts and account history, and the Domain Agent for entity-level detail. Every statement in the output traces back to one of these sources. If there’s no source, then there’s no statement. ### Component 5: Output format Specify the exact structure, section order, and delivery method. "Exactly 3 recommendations ranked by revenue impact" is a constraint. "Some recommendations" is not. The specificity of Component 5 is what separates agents that produce the same quality every Monday from agents that surprise you. **** ## A preview of the revenue agent fleet Every agent in this guide runs the same 5-component structure on [Vasco's context graph](https://vasco.app/blog/context-graphs-for-gtm-teams). The foundation is what makes them reasoning agents rather than retrieval tools. ### Start here: the WBR Builder Build this one first. WBR prep drops from 4 hours to 20 minutes, and the first run almost always surfaces a metric that has been quietly wrong for months. When the room sees that, every conversation about trusting the agents changes. ### Then: the Pipeline Analyst Nine deals showing as active in HubSpot with zero engagement for 30 days. All nine flagged before the weekly review. Not because anyone checked, but because the agent connected CRM status, Gong silence, and Stripe signals into a single conclusion. ### Then: the ICP Discoverer Once you trust the reporting and the pipeline monitoring, you can ask the strategic question. The ICP Discoverer reads every won and lost deal and tells you exactly which accounts to chase next, validated against your actual outcomes rather than your original assumptions. ## For teams ready to build something that lasts This is not a framework for thinking about AI. It is a step-by-step manual with the actual commands, the actual prompt structure, and the actual deployment sequence. Every section has something to do, not just read. Start at the connection step and do not skip the data chain. Read the guide > --- --- title: "CRO’s 2026 guide to agentic AI" description: "The AI context gap is the defining challenge for every RevOps team building on agents right now. This guide explains what it is, why it exists in most stacks, and how a revenue context graph closes it. By the end, you'll know exactly where your stack stands and what to do next." canonical: "https://vasco.app/blog/agentic-ai-guide-revops-2026" date: "2026-02-03T00:00:00.000Z" authors: - Guillaume Jacquet jobTitle: "CEO & co-founder" readingTimeMinutes: 9 contentType: guide intent: playbooks-methods pillar: "AI in GTM & RevOps" audiences: - revops - founders - fractional - cros - scale-ups - start-ups --- # CRO’s 2026 guide to agentic AI _How to close the context gap and win fast_ > If you're a RevOps leader, CRO, or founder at a B2B SaaS company who's already deploying agents and wondering why the ROI hasn't shown up yet, this guide was written for you. ## What you’ll find in this guide This [**guide**](https://share.hsforms.com/2V0byAjnqRUiCqsTh9p4DYgdb5kh) is designed to move you from diagnosing the gap to deploying a fleet you can trust. 1. **Why agents need context** – What your agent builder needs to deliver real value 2. **How context powers agents** – Turn your context graph into a fleet of agents 3. **Build, buy, or assemble? **– The context-first deployment guide for GTM teams ![](https://cdn.sanity.io/images/ys8gstp8/production/639689bb5c64fec5ea9c5b477dd53f2b36271a10-1920x1080.png?w=1600&fit=max&auto=format) ## The 2026 priority: Get out of context debt Most RevOps teams think they're set up for AI. The outputs look right, summaries are clean, and nobody pushes back. What you can't see is that the important context is missing from your agent builder. The agent builder is the visible part of your stack. What it reasons on is invisible, and for most RevOps stacks, that foundation was never built. This guide explains what that foundation is, why it has to come first, and what a fleet you can trust actually looks like. ## A preview of context-powered agents No agent in the fleet works without [the context graph](https://vasco.app/blog/context-graphs-for-gtm-teams) underneath it. Without it, the graph and the agent loses the signal that makes it useful. ### Find the accounts worth chasing An ICP Discoverer agent reads every won and lost deal to tell you exactly who to chase next. One customer had 38% of their pipeline chasing $38K deals at 8% win rates. The graph found it and moved 60% of outbound capacity to accounts closing at $62K in 132 days. ### Manage and protect your pipeline Pipeline Analyst and Account Monitor agents connect engagement gaps, billing signals, and cross-tool silence into a single surveillance layer. The agents that would have caught 9 stalled deals, a cancelled payment, and a competitor threat before they became a 21% revenue miss. ### Build for your unique revenue motion A custom agent covers the workflows the core fleet doesn't, such as territory planning, competitive intel, QBR prep, or anything specific to your GTM motion. [**Read the guide**](https://share.hsforms.com/2V0byAjnqRUiCqsTh9p4DYgdb5kh) to access 25+ context-powered agents, purpose-built for RevOps. ## For RevOps leaders who are done guessing This isn't a framework for where AI is heading. It's a diagnostic for where your stack stands right now, and a concrete path to fixing it. Every section is built around a decision you're about to make: whether to trust your agent's next output, whether to deploy another tool, whether to bring this to your CRO. By the end, you'll have the language, the evidence, and the roadmap to make that call with confidence. --- --- title: "GTM & RevOps Execution" description: "Only 4% of SaaS companies ever reach $1M ARR. And the ones that don't make it rarely have a product problem. The gap is almost always in execution; a go-to-market that couldn't scale consistently enough, for long enough." canonical: "https://vasco.app/blog/gtm-revops-execution" date: "2026-02-03T00:00:00.000Z" authors: - Justin Hudon jobTitle: Head of Sales and Customer Success readingTimeMinutes: 3 contentType: article intent: foundations pillar: "GTM & RevOps execution" --- # GTM & RevOps Execution _Where strategy either holds or falls apart_ ## Strategy is abundant. Execution is rare. Revenue Operations exists precisely to close that gap. It's the connective tissue between how a company is built and how it actually runs — day to day, motion by motion, quarter by quarter. GTM execution is the layer where that work happens: how sales, marketing, and customer success activate strategy through concrete motions, shared rhythms, and operational discipline. When it works, growth becomes a system. When it doesn't, it stays a collection of individual efforts. - New to RevOps? Start here: [RevOps 101: What is revenue operations and why does It matter?](https://vasco.app/blog/revops-101) ## What GTM execution means, concretely A go-to-market strategy tells you [who you're selling to](https://vasco.app/blog/icp-across-maturity-stages), through which channels, and at what price. Execution is what happens after that decision is made: how your team shows up, follows up, and scales what works. It covers four interconnected areas: ### 1. Sales motions in practice. Whether you're running inbound, outbound, or a hybrid, the motion only works if it's operationalized. That means clear ownership at every stage, handoff protocols that don't lose deals in the cracks, and SDR teams that are set up to succeed rather than quietly burning out. - [Your SDR team is dying: Here's the fix](/blog/your-sdr-team-is-dying) - [Inbound vs. outbound leads: Why your MQL count looks off](/blog/inbound-vs-outbound-leads) ### 2. Cross-functional alignment. A single source of truth — one shared data model, one set of definitions across sales, marketing, and CS — is what separates teams that move together from teams that spend every meeting defending their own numbers. When that foundation is missing, the problem compounds fast: attribution becomes political, handoffs break down, and revenue gaps stay unresolved because no one agrees on where they came from. - [The Gravitational pull of Daily Digests: How revOps create alignment and earn influence through a shared source of truth](/blog/the-gravitational-pull-of-daily-digests) - [The Bowtie model: Goodbye linear funnel, hello full lifecycle framework](/blog/the-bowtie-model) - [The Hidden revenue killer: Why your product, sales and finance teams need to get on the same page](/blog/the-hidden-revenue-killer) - [Bridging strategy and execution in a shifting GTM landscape: Why RevOps is key to building clarity, accountability, and long-term growth](/blog/bridging-strategy-and-execution-in-a-shifting-gtm-landscape) ### 3. Enablement and workflows. Giving reps better tools and content is only half the equation. The structure around how they use them — the right content at the right stage, coaching grounded in actual call data, workflows that reduce friction instead of adding to it — is what determines whether enablement actually delivers results. AI is starting to reshape what's possible here, but the fundamentals haven't changed. - [Supercharging sales enablement in the AI era: Build the scaffolding before you scale](/blog/supercharging-sales-enablement-in-the-ai-era) - [AI in sales: Workflow wins, real limits, and what's next](/blog/ai-in-sales) ### 4. Operating cadences. Execution without rhythm is just chaos with good intentions. A well-designed cadence — weekly pipeline reviews, monthly business reviews, QBRs, annual planning — gives a GTM team the structure to catch drift early, surface decisions that have been delayed too long, and maintain accountability across functions. The specifics matter: what gets reviewed, who owns it, and what happens when the numbers don't add up. A cadence that gets skipped under pressure is just a wish list. - The RevOps Rhythm Guide] _(coming soon)_ ## The hardest part: making it repeatable Designing a GTM motion is one thing. Making it repeatable is another. Repeatable execution means the system produces consistent results regardless of who's running it. New reps ramp in a predictable window. Deals follow a clear process. Forecasts reflect reality. Handoffs between teams happen without information loss. That level of consistency comes from documented processes, clean data, and a system designed to reduce cognitive load — for example, automated SLA alerts triggered by CRM activity (a new inbound lead, a stalled deal, a missed follow-up) that prompt the right action at the right time without depending on each rep to remember it. This is also where data quality becomes an execution problem. A CRM with inconsistently defined lifecycle stages, missing fields, or stages that don't reflect how deals actually move will corrupt every downstream process that depends on it — forecasting, attribution, pipeline reviews, enablement. Bad data architecture doesn't just create reporting headaches. It makes repeatable execution structurally impossible. - [The Smart guide to acquisition lifecycle stages & CRM setup](/blog/lifecycle-stages-and-crm-setup) - [Don't trust your conversion rates?](https://vasco.app/blog/conversion-rates) - [The Attribution Guide: making sense of what drives revenue](https://vasco.app/blog/the-attribution-guide) ## Where to go from here Every GTM model runs on the same fuel: a team that executes consistently, with the right information, at the right time. The companies pulling ahead are the ones that have turned execution into a system. Vasco's operator content on GTM & RevOps execution covers the full range of the topic — from SDR team design to operating cadences, from sales enablement to cross-functional alignment. Each topic stands on its own, but they're all part of the same system. ## FAQ ### What's the difference between GTM and RevOps? GTM defines the strategy — which channels, which segments, which sales motion. RevOps is the operational system that makes it work in practice: the data, processes, and cross-functional alignment that turn strategy into repeatable execution. ### What is an operating cadence? The rhythm of structured reviews that keeps a GTM team aligned and accountable — weekly pipeline calls, monthly business reviews, QBRs. Each serves a distinct purpose: catching drift early, reviewing performance, and making decisions before small problems compound. ### What does sales enablement mean? The resources, processes, and tools that help reps engage buyers more effectively — content, training, onboarding, and workflows that surface the right information at the right stage. It reduces ramp time and creates consistency across the team. ### What is a single source of truth in RevOps? When every team operates from the same data with the same definitions. Without it, pipeline numbers get debated instead of acted on, and cross-functional meetings become alignment exercises rather than decision-making sessions. ### What does "repeatable execution" mean in GTM? It means the system produces consistent results regardless of who's running it — predictable ramp times, deals that follow a clear process, forecasts that reflect reality. Repeatability is the difference between a GTM motion that scales and one that depends on a handful of key people to hold together. ### How do you know if GTM execution is the problem? Pipeline reviews surface surprises that should have been visible earlier. Ramp times are long and inconsistent. Growth feels dependent on a few key individuals rather than the system. These signals, together, point to an execution layer that hasn't kept pace with the company's growth. --- --- title: "YOUR ICP, ACROSS MATURITY STAGES" description: "In the AI world, ICP sits at the center of GTM execution, powering personalization, prioritization, and automation. This guide explores how ICP evolves with data maturity and GTM complexity, and how to make it operational at every stage of growth." canonical: "https://vasco.app/blog/icp-across-maturity-stages" date: "2026-02-03T00:00:00.000Z" authors: - Guillaume Jacquet jobTitle: "CEO & co-founder" readingTimeMinutes: 9 contentType: guide intent: playbooks-methods pillar: "RevOps maturity & scaling journeys" audiences: - revops - founders - fractional - cros - scale-ups - start-ups --- # YOUR ICP, ACROSS MATURITY STAGES _From early intuition to evidence-backed, living systems_ ## What you’ll find in this guide This [**guide**](https://db5kh.share.hsforms.com/2cbc6uyF-SwGN9bOw0h-1GQ) is structured around three sections, designed to move from foundations to execution: 1. **ICP fundamentals** – what an ICP is made of today and why it’s inherently multidimensional 2. **ICP across maturity phases** – how ICP approaches evolve with scale, data, and GTM complexity 3. **Making ICP operational** – embedding ICP into execution, measurement, and feedback loops ![](https://cdn.sanity.io/images/ys8gstp8/production/4d26881e461b23b7d4635acb36007684b5d10334-1920x1080.png?w=1600&fit=max&auto=format) ## ## The four ICP maturity phases At the core of the guide is a practical framework built around **four common ICP maturity phases**. Each phase reflects a different operating context, data reality, and level of GTM complexity. - **Hypothesis-driven ICP **– Typical of early-stage or high-ACV motions, where ICP is shaped primarily by founder intuition, early deals, qualitative insight, and leading signals rather than validated outcomes. - **Outcome-driven ICP **– As customer volume grows, ICP becomes grounded in post-sale success patterns like retention, expansion, time-to-value, and deal quality, helping teams focus on segments that create durable value. - **ICP as a portfolio **– At scale, multiple ICPs or micro-ICPs coexist. Teams formalize feedback loops, optimize what works, and deliberately explore new segments without losing focus or consistency. - **Adaptive ICP system **– In large, complex organizations, ICP becomes an always-on system that continuously ingests signals, detects shifts, and actively coordinates GTM decisions across motions, segments, and teams. ## A practical guide, not a one-size-fits-all framework The goal isn’t to prescribe a single “best” ICP model, but to help you recognize where you are today and apply the right level of ICP sophistication for your GTM motion. > Used well, ICP becomes a shared system that aligns teams, guides execution, and evolves as your business grows. --- --- title: "Pulse, your new command center" description: "Our recent product releases introduce new ways to see, understand, and act on your revenue data: from a unified command center, to a dedicated analysis workspace, to cleaner plan vs actuals downstream. Together, these updates make it easier to diagnose performance, explain what’s happening, and move forward with confidence." canonical: "https://vasco.app/blog/product-spotlight-january-2026" date: "2026-01-28T00:00:00.000Z" authors: - Guillaume Jacquet jobTitle: "CEO & co-founder" readingTimeMinutes: 1 contentType: product-spotlight intent: foundations pillar: "RevOps systems & architecture" audiences: - enterprise --- # Pulse, your new command center _A real-time view of the entire funnel_ ## 1. Pulse, your new command center [https://www.youtube.com/embed/jKfgOAN-YLw?iv_load_policy=3&rel=0&modestbranding=1&playsinline=1&autoplay=0&mute=1](https://www.youtube.com/embed/jKfgOAN-YLw?iv_load_policy=3&rel=0&modestbranding=1&playsinline=1&autoplay=0&mute=1) ### Diagnose your entire revenue engine in one unified view, powered by real-time data. - Track beginning, net new, and ending ARR in a single snapshot, with clear plan vs actual deltas to ground every discussion in real performance. - Analyze the full funnel from pre-awareness to expansion, switching seamlessly by stage or function to understand how marketing, sales, and customer success contribute to revenue. - Focus on what truly drives (or leaks) revenue with Impact panels highlighting top drivers, conversion and time metrics, ICP efficiency, and instant drilldowns to the underlying data. ## 2. Studio: explore and explain your revenue data ![Studio](https://cdn.sanity.io/images/ys8gstp8/production/9b87187742ee98d6ddd61cf0039a354312d5105f-4407x2376.png?w=1600&fit=max&auto=format) ### Studio is Vasco’s analysis workspace. It lets you explore revenue data, break down KPIs, and drill into results to understand what happened and why — without writing SQL. - Analyze KPIs using **custom dimensions and time-based metrics** - **Drill into any number** to see the accounts, motions, or drivers behind it - Sort, aggregate, and explore data in a **spreadsheet-like experience**, built for RevOps questions ## 3. Plan vs actuals, from Vasco to your warehouse ![](https://cdn.sanity.io/images/ys8gstp8/production/359d054102c246d3a53ef41aa448540c21e09d65-1640x400.png?w=1600&fit=max&auto=format) ### Destinations let you send your revenue data to your data warehouse or BI. Until now, this data included actuals only. Forecasts are now included, so you can analyze plan vs actuals in one place. - Export **actuals and forecasts together**, using a consistent schema - Compare plan vs actuals by **function, motion, channel, position, or employee** - Remove manual reconciliation and build **end-to-end planning analytics** downstream ## Other cool upgrades - **Clearer hub navigation**, making it easier to move between Planning, Execution, and Review - **Stage diagnostics consolidated into Pulse**, reducing navigation friction and duplicate views - **Faster data updates across the platform**, so insights stay fresh and responsive --- --- title: Compound growth description: "Jacco van der Kooij is one of the leading thinkers in modern go-to-market design and the creator of Revenue Architecture. In this article, he shares his perspective on how RevOps will shift in 2026: toward AI-powered, system-driven, and compounding growth models." canonical: "https://vasco.app/blog/from-linear-productivity-to-system-driven-revops" date: "2026-01-08T00:00:00.000Z" authors: - Jacco van der Kooij jobTitle: Founder of Winning by Design readingTimeMinutes: 3 contentType: article intent: strategy-insights pillar: "RevOps systems & architecture" --- # Compound growth _From linear productivity to system-driven RevOps_ ## 2026: the year of compound growth For more than 15 years, SaaS companies optimized people-based funnels — improving CR2, tightening handoffs, and specializing roles — until that model hit a structural ceiling. SaaS reached the limits of human productivity. But AI breaks through it. In this new context, efficiency is measured by the ratio of compounding to cost: whether outputs are feeding inputs faster than marginal cost is rising. Once you see growth as a system to be designed instead of a funnel to be staffed, 2026 marks a true inflection point. Compound growth is not a tactic or an outcome. It is an advanced growth state of a system—one in which outputs reliably feed future inputs faster than marginal cost increases, creating self-reinforcing momentum. ## From linear funnels to compounding systems The question is no longer how much more output can be extracted from the funnel, but how systems can be configured to compound. Traditional go-to-market motions were built as linear systems. They required constant energy input to generate output, and they decayed the moment that input stopped. Compounding systems behave differently. When designed correctly, the output of one cycle becomes the input of the next. This shift from linear productivity to compounding systems sits at the core of how RevOps evolves in 2026. ## The operating layer becomes the breakthrough The biggest RevOps breakthrough in 2026 will not happen in the data stack or in team alignment. It will happen in the operating layer that sits between them. ![The Operator Dashboard](https://cdn.sanity.io/images/ys8gstp8/production/2d020664bbea5acb2ed5114c6ddec58e775acd57-1920x1080.png?w=1600&fit=max&auto=format) This operating layer is built on real-time data and atomic signals that reveal how the system is behaving: velocity, conversion dynamics, cycle-time, and early signs of growth loop decay. It becomes the bridge between infrastructure and financial performance. With this layer in place, AI can generate real-time insights on system behavior — not just analyze what happened last quarter — enabling decisions at the same cadence that growth happens. This shift sets the stage for AI’s deeper role inside RevOps. ## AI turns RevOps into a growth science AI has not simply made RevOps faster. It has fundamentally changed the job. Today’s market is defined by constraints, limited demand, finite resources, and real tradeoffs. In this environment, RevOps moves from capacity planning to growth planning: not “How do we process what’s coming in?” but “What growth is achievable with what we have?” This is where AI becomes transformative. AI can model the system in real time and simulate thousands of growth trajectories. It automates tasks. More importantly, it reveals the mechanics of compounding and decay. > "AI is turning RevOps into a science, not just an operations function." — Jacco van der Kooij ## Why growth loops outperform channels In 2026, the most effective go-to-market “channel” will not be a channel at all. It will be a growth loop. Traditional channels like outbound, paid, and inbound behave as open systems: energy goes in, output comes out, and the motion decays when the input stops. AI-era GTM favors growth loops, where the output of one cycle becomes the input of the next. ![Open vs compounding systems](https://cdn.sanity.io/images/ys8gstp8/production/73beb90ddd862ef6b75aa3aa30ebcc491cb737c8-1920x2160.jpg?w=1600&fit=max&auto=format) Product signals generate opportunities. Opportunities generate users. Users generate data. Data strengthens the product. That loop compounds while channels eventually saturate. A growth loop outperforms any channel because it compounds growth without compounding cost and shortens cycle time — both signatures of 2026 growth engines. > "A growth loop is growth that creates growth." — Jacco van der Kooij ## RevOps becomes the architect of growth RevOps may remain the name of the role, but growth architecture becomes the core of the job. In 2026, RevOps is responsible for architecting how the business grows: the loops, the instrumentation, the cycle-time, the compounding logic, and the health of the system itself. The single capability that differentiates RevOps is the ability to use AI — paired with the methods of [revenue architecture](https://winningbydesign.com/revenue-academy/course-revenue-architecture/) and [growth architecture](https://winningbydesign.com/event/workshop-growth-architecture-primer/) — to run simulations, not forecasts. Simulations reveal the probability of hitting targets, show where growth will actually come from, detect where decay begins, and expose how different levers influence compounding. > "The system tells us where growth will come from — if we are willing to listen." — Jacco van der Kooij Taken together, these shifts fundamentally redefine the RevOps mandate. ## Why forecasting must be rebuilt from scratch If one process must be rebuilt in 2026, it is forecasting. Traditional forecasts assume linearity in a world defined by nonlinear systems. They treat growth as a static funnel instead of a dynamic engine with acceleration, drag, decay, and compounding effects. Forecasting must evolve into growth modeling. Using AI, teams can simulate thousands of system states, understand the probability of hitting targets, identify where compounding is emerging, and detect where decay begins months before it appears in revenue. ### The companies that win in 2026 will not be the ones who predict the future most accurately, but the ones who can model it, monitor it, and redesign their growth engine in real time. ## FAQ ### What does “system‑driven RevOps” mean? System‑driven RevOps means treating growth as a designed system rather than a linear funnel. It’s about configuring outputs to feed inputs faster than marginal cost rises, creating self‑reinforcing momentum and compounding growth. ### How is 2026 different for SaaS companies? 2026 marks a shift from human‑driven productivity to AI‑powered, system‑driven growth. SaaS companies can no longer rely on funnel optimization alone; they must design systems that compound outputs and reduce decay. ### What is the difference between linear funnels and compounding systems? Linear funnels require constant energy input to generate output and decay when input stops. Compounding systems behave differently: the output of one cycle becomes the input of the next, creating self‑reinforcing momentum and sustained growth. ### What is the operating layer in RevOps? The operating layer is the real‑time data and signals that reveal how the system behaves: velocity, conversion dynamics, cycle time, and early signs of growth loop decay. It bridges infrastructure and financial performance, enabling decisions at the same cadence as growth. ### How does AI turn RevOps into a growth science? AI turns RevOps into a growth science by modeling the system in real time and simulating thousands of growth trajectories. It automates tasks and reveals the mechanics of compounding and decay, enabling data‑driven decisions rather than gut feel. ### What is the role of RevOps in 2026? In 2026, RevOps becomes the architect of growth, responsible for designing loops, instrumentation, cycle time, compounding logic, and system health. It uses AI and revenue architecture methods to run simulations, not just forecasts. --- --- title: The economics of AI description: "AI is often framed as a product shift. In reality, it is an economic one. This article builds on insights from the Vasco Trends Report, notably conversations with Michael Litt (CEO, Vidyard) and Guillaume Jacquet (CEO, Vasco)." canonical: "https://vasco.app/blog/the-economics-of-ai" date: "2026-01-08T00:00:00.000Z" authors: - Guillaume Jacquet jobTitle: "CEO & co-founder" readingTimeMinutes: 4 contentType: article intent: strategy-insights pillar: "AI in GTM & RevOps" audiences: - enterprise --- # The economics of AI _Why usage-based pricing is reshaping RevOps_ ### Michael Litt (CEO, [Vidyard](https://www.vidyard.com/)) and Guillaume Jacquet (CEO, [Vasco](https://vasco.app)) point to a structural change that goes far beyond features or workflows: AI is breaking the economic assumptions SaaS has relied on since the rise of the cloud. For RevOps teams, the impact is immediate. Pricing, forecasting, and planning models built for predictable recurring revenue are starting to crack. ## AI reintroduces real marginal costs into software Traditional SaaS was built on a powerful assumption: once software is shipped, serving one more customer costs almost nothing. That assumption enabled unlimited usage plans, stable margins, and clean recurring revenue models. AI changes that equation. Running AI systems comes with real, variable costs: tokens, compute, energy, storage, latency. These costs scale with usage, not licenses or seats. And many frontier models are still subsidizing their true operating costs, meaning today’s prices likely understate the real economics and may rise before efficiencies bring them down. Michael Litt explains why this breaks traditional pricing logic: > _“If you charge someone $20 a month for software, but their AI usage generates massive token consumption, your compute bill can exceed the revenue they’re paying you.”_ At Vidyard, this is not theoretical. Litt shared an example of a customer generating **200,000 AI-powered videos per month** using [avatar technology](https://www.vidyard.com/free-avatar-gallery). Under a traditional subscription model, that customer would quickly become unprofitable. > _“If they were just uploading regular videos, that’s fine. But when they generate AI Avatars at massive scale, the compute costs explode. We have to charge based on usage or outcomes to protect our margins.”_ This is why Litt summarizes the shift with a now widely echoed idea: **“RaaS is the new SaaS." **Results-as-a-Service replaces Software-as-a-Service, and customers pay for outcomes delivered, not for access to software. ## From recurring revenue to re-occurring revenue Usage-based pricing doesn’t eliminate recurrence, but it fundamentally alters its shape. Classic SaaS models rely on contracts, fixed ACV, and relatively smooth month-to-month revenue. In contrast, usage-based revenue expands and contracts based on adoption, seasonality, and actual consumption. ![](https://cdn.sanity.io/images/ys8gstp8/production/dc43305a27c34ba7247d49a1b2080db5c4b0b85e-1920x1080.png?w=1600&fit=max&auto=format) As Guillaume Jacquet puts it: > _“Expansion changes meaning. Investors used to value recurring revenue. Now it’s often re-occurring revenue, and it doesn’t behave the same way.”_ Revenue still comes back, but not on a perfectly predictable schedule. It fluctuates with usage, markets, and customer behavior. That volatility challenges long-standing assumptions embedded in forecasting, board reporting, and valuation models. ## Why companies are landing on hybrid pricing models Despite the momentum behind usage-based pricing, many companies are still hesitant to make a clean break from subscriptions. In practice, we’re seeing many **hybrid models** emerge. These typically combine: - A base subscription fee for access and predictability - Usage-based components tied to consumption, tokens, or outcomes According to Guillaume Jacquet, this is often a pragmatic transition phase: > _“What we see today are mostly hybrid models. Companies keep a subscription layer, but package usage inside it — for example by selling bundles of tokens — to preserve some predictability while aligning costs with consumption.”_ These hybrids reflect a reality: fully variable revenue is still uncomfortable for many operators, customers, and investors. But they also signal that unlimited usage for a flat fee is no longer sustainable in an AI-driven world. ## Why usage-based pricing breaks traditional RevOps planning This is where RevOps feels the disruption most directly. Legacy RevOps models assume: - Revenue is predictable (the same amount is recognized month after month for a given customer) - CRM approximates revenue reality - Forecasting is driven by pipeline and bookings Usage-based pricing breaks all three. When adoption determines revenue, missing usage targets means missing the plan entirely. ACV is no longer fully known at close. Forecasting becomes probabilistic rather than deterministic. As Michael Litt notes: > _“If adoption is how you get paid, and adoption doesn’t happen, you don’t just miss expansion — you miss the plan.”_ RevOps teams are forced to rethink how they forecast, plan capacity, and design compensation in a world where revenue follows usage, not signatures. ## When CRM, billing, and analytics stop lining up In traditional SaaS, CRM was a reasonable proxy for revenue. Usage-based models fundamentally break that assumption. Guillaume Jacquet explains why this creates a structural problem: > _“In a usage-based world, ACV can swing massively based on consumption. The systems that actually know how customers consume value are billing engines and product analytics, not CRM.”_ As a result, teams are increasingly moving their source of truth away from the CRM and toward the data warehouse — the only place where product usage, billing, and financial data can converge. ![](https://cdn.sanity.io/images/ys8gstp8/production/9c472ae68fd4d1f020a4f7f2ac447094b72df6c6-1920x1080.png?w=1600&fit=max&auto=format) This shift, however, introduces significant technical complexity. RevOps teams are forced to rebuild revenue logic from fragmented systems, manage brittle pipelines, and maintain custom models just to answer fundamental questions about growth and performance. As Jacquet sums it up: > _“RevOps ends up stitching everything back together, and that’s incredibly complex.”_ ## The end of cheap scale, not the end of SaaS AI doesn’t kill SaaS. But it does mark the end of cheap, thoughtless scale. The economics of AI force companies to align value, cost, and revenue far more tightly than before. That pressure cascades through pricing models, forecasting logic, and RevOps design. Most companies haven’t fully transitioned to usage-based pricing yet. But the ones making progress are those taking the shift seriously early, experimenting with hybrid models, and rethinking how RevOps supports growth when revenue follows consumption and outcomes rather than contracts. ### The future isn’t about abandoning SaaS, but adapting it to an economic reality where scale is no longer free. --- --- title: "2026 RevOps Trends & Predictions" description: "We asked fifteen of the smartest operators, founders, and advisors we know for their perspective on the year ahead. And their insights genuinely floored us." canonical: "https://vasco.app/blog/2026-revops-trends-predictions" date: "2025-12-15T00:00:00.000Z" authors: - Sophie Geoffrion jobTitle: "Brand & Content Lead" readingTimeMinutes: 12 contentType: guide intent: strategy-insights pillar: "AI in GTM & RevOps" audiences: - revops - cros - start-ups - scale-ups - fractional - founders --- # 2026 RevOps Trends & Predictions _What’s reshaping GTM in 2026 according to the operators building it._ ## Get the report ### If you work anywhere near revenue, it might just be the most compelling read you’ll open this quarter. ![](https://cdn.sanity.io/images/ys8gstp8/production/5510c88307c0b1bd99363d98b615d454adb533e6-3840x2160.jpg?w=1600&fit=max&auto=format) > _"AI is a civilizational-level general-purpose technology, at minimum as impactful as the integrated circuit, and probably bigger. There’s a good chance we’re in a bubble, but like every major technology wave, the metaphorical fiber-optic cables it leaves behind will fuel an incredible expansionary period for decades to come." _— Stuart Watson, Resolve AI ## Voices shaping RevOps in 2026 This report is built in collaboration with 15 founders, operators and GTM leaders shaping the future of RevOps. Their perspectives ground our findings in real-world experience and help surface the trends that will matter most in 2026. Sincere thanks to everyone who participated. ![Jen Igartua, GoNimbly](https://cdn.sanity.io/images/ys8gstp8/production/70b4b07f78d384d978d9d5cb972da5b4f6d9bb9f-3840x1720.jpg?w=1600&fit=max&auto=format) > "_What surprised me most was the polarity of 2026: on one side, breathtaking technological acceleration with AI, agents, programmatic GTM, operating layers; on the other, an equally strong pull toward human channels, trust, and the fundamentals of execution." _— Sophie Geoffrion, Vasco ## The key sections of the guide 1. **The New Definition of Efficient Growth** : From “more with less” to leverage, compounding, and clarity 2. **The Economics of AI: The End of Cheap Scale** : How compute, energy, and data reshape SaaS economics 3. **The Rise of the Operating Layer** : Real-time systems, unified data, and the GTM brain 4. **The Transformation of RevOps** : From service desk to growth architect and GTM CTO 5. **AI and the Future of Work** : The collapse of junior roles and the rise of the curious generalist 6. **Faster or Smarter? The AI Maturity Curve** : 2025 = curious ; 2026 = faster ; 2027 = smarter 7. **GTM Motions in 2026: A Tale of Divergence** : Programmatic ABM, PLG, agentic buying and a resurgence of human channels 8. **The Rebuild Agenda** : Forecasting, attribution, ICP, CRM workflows, and performance systems 9. **The human cost of AI-era GTM** : Technology accelerates. Humans absorb the impact. > _"As the world gets noisier (more emails, more content, more products, more AI-generated noise), what matters most is credibility and trust. Buyers are overwhelmed and don’t know which numbers to believe or which story is true. The GTM motions that still work are the ones where trust naturally exists: brand marketing, influencer networks, events, referrals, and partner-led selling."_ — Kyle Norton, owner.com --- --- title: "ICP Builder & Customer Profiles" description: "We’re closing the year with upgrades that tighten your entire GTM system. From refining your ICP to mapping your journey end-to-end and connecting new data sources, these updates deliver sharper insights, cleaner data, and faster paths to value." canonical: "https://vasco.app/blog/product-spotlight-december-2025" date: "2025-12-15T00:00:00.000Z" authors: - Guillaume Jacquet jobTitle: "CEO & co-founder" readingTimeMinutes: 1 contentType: product-spotlight intent: foundations pillar: "RevOps systems & architecture" audiences: - enterprise --- # ICP Builder & Customer Profiles _Now live in Vasco!_ ## 1. ICP Builder & Customer Profiles [https://www.youtube.com/embed/aqLCpxSDw74?iv_load_policy=3&rel=0&modestbranding=1&playsinline=1&autoplay=0&mute=1](https://www.youtube.com/embed/aqLCpxSDw74?iv_load_policy=3&rel=0&modestbranding=1&playsinline=1&autoplay=0&mute=1) ### Define, compare, and track your ICP directly in your revenue engine, with real data, in real time. - Build explainable customer profiles with rule-based criteria, instant previews of segment size and performance, and AI assistance from Gama. - Compare multiple profiles side by side to understand differences in volume, conversion, and speed through the funnel. - Monitor your ICP mix across every stage and function in Pulse, with drilldowns to the account level for clarity and action. ## 2. Pipedrive integration ![Pipedrive CRM](https://cdn.sanity.io/images/ys8gstp8/production/0859775f2d682fe8477aca421a10b4553abd6e44-1520x640.png?w=1600&fit=max&auto=format) ### Connect Pipedrive to Vasco in minutes and sync your CRM data automatically. - Seamlessly integrate your Pipedrive CRM and bring deals, organizations, people, and activities directly into Vasco. - Start with a complete, real-time view of your pipeline — no exports, no manual uploads. - Keep your customer journey metrics up to date with automatic, continuous syncing. ## 3. Journey Editor ![](https://cdn.sanity.io/images/ys8gstp8/production/e2a7848a5cec3bf48a9f32788bfc2b2acea166b6-3840x2160.png?w=1600&fit=max&auto=format) ### Map every stage, motion, function, and lead source in one unified view, as intuitively as building a journey in Miro. - Build your entire customer journey in a single visual interface, with stages, motions, functions, and lead sources all connected in one place. - Customize metrics, edit components instantly, and use the Library panel to add or update building blocks with total clarity. - Create and manage journey versions effortlessly, so you can evolve processes over time without rebuilding from scratch. ## 4. Other cool upgrades - **Salesforce Custom Objects: **Sync and map your Salesforce custom objects just like standard CRM objects, so Vasco fits your existing architecture without requiring any refactoring. - **Salesforce Calculated Fields: **Use your Salesforce calculated fields in lifecycle mappings and dimensions, ensuring accurate logic and up-to-date GTM signals without any manual work. - **Weekly Digest: **Choose a weekly delivery instead of daily, so you get the same high-value insights without the overload. --- --- title: Supercharging sales enablement in the AI era description: "I studied kinesiology. Trained people to deadlift. Never planned to cold call strangers for a living. When I finally tried sales, I discovered something unexpected: the best reps weren't the loudest or most aggressive. They were the most curious. They asked better questions. They built structure around what worked. They genuinely cared whether someone said yes or no. That realization changed everything." canonical: "https://vasco.app/blog/supercharging-sales-enablement-in-the-ai-era" date: "2025-11-11T00:00:00.000Z" authors: - Sarah Chmielewski readingTimeMinutes: 5 contentType: article intent: playbooks-methods pillar: "GTM & RevOps execution" --- # Supercharging sales enablement in the AI era _Build the scaffolding before you scale_ ### Over the past decade, I've scaled revenue teams through hypergrowth, the kind where something breaks every quarter because you're moving too fast to fix it. And I've learned this: when growth outruns enablement, even your best people hit a wall. ## The invisible bottleneck You scale fast. Activity spikes. Dashboards light up green. Leadership celebrates. But something feels off. Deals take longer to close. Pipeline quality drops. New hires take twice as long to ramp as your first few reps did. A pattern emerges: three top performers carry the entire number while everyone else treads water. You hire more people. The problem gets worse. The instinct is to move faster. More activity, more coaching, more urgency. But speed doesn't fix broken systems. It amplifies them. > The breakthrough comes when you stop asking "who's not performing?" and start asking "what's actually breaking?" Almost always, it's not effort. It's enablement: teams scale, systems don't. ## What good looks like (and how to steal it) Walk into most sales orgs and you'll hear the same thing: "Only Sarah knows how to position that." "Only Eric knows how to sell that product." "Only Jen closes enterprise deals." This is a red flag dressed up as a compliment. Real enablement isn't more training decks or lunch-and-learns. It's the infrastructure that captures what works and makes it repeatable across your entire team, not just your top performers. Here's what changed things for us: ![sales enablement](https://cdn.sanity.io/images/ys8gstp8/production/81b07d70b163f0cbedef2d61624b1abdbafdc94f-1920x540.png?w=1600&fit=max&auto=format) - **Stop teaching. Start codifying.** We didn't create more content. We extracted the frameworks our best reps were already using. Their cold call structure, their discovery questions, their objection responses. Then we enhanced those frameworks and made them simple enough for anyone to follow. - **Make "good" measurable.** We built lightweight rubrics for every critical conversation. Not 47-point evaluation forms. Three to five key behaviors that separated great calls from mediocre ones. Things like: "Did they add business context in the first 90 seconds?" "Do they understand the business problem that needs to be solved?" - **Coach from data, not gut feel.** We stopped relying on intuition and started analyzing actual calls from top performers. What did they say? When did they say it? What happened next? Patterns emerge, and they were often counterintuitive. The result? Conversation-to-meeting rates and pipeline velocity jumped. But the most important insight wasn't what we taught. It was what we finally understood about how deals actually get won. ## The multithreading breakthrough Jim was stuck. Great rep, strong relationships, always in the deal. But his close rate was well below average. When we analyzed his deals, the pattern was obvious: he had one champion in every account. Just one. When that champion got pulled into another priority, the deal stalled. When they left the company, the deal died. We didn't need to teach Jim new skills. We needed to change one behavior: **intentional multithreading from the first call via buyer enablement**. By the next quarter, Jim's close rate jumped by 40%. That's what enablement does when it works: it finds the one thing that amplifies everything else. ## The AI acceleration layer Here's where we hit a new problem: the system worked, but it didn't scale. I couldn't manually review every call. I couldn't spot every pattern. I couldn't coach an entire team with the same depth I could coach two. We needed to capture what was working and distribute it faster than I could do manually. That's where AI stopped being hype and started being useful. We didn't use AI to replace coaching. We used it to make coaching more precise. Here's the exact process: ![sales enablement](https://cdn.sanity.io/images/ys8gstp8/production/a1a2843743ddeb8661dcfe889fc39af2d7f6636e-1920x1080.png?w=1600&fit=max&auto=format) **1. Capture everything.** Every customer call gets recorded. No exceptions. You can't improve what you can't see. **2. Define "good" with receipts.** Pull 10 calls from your top performers. Not the calls they think went well, the calls that actually closed. Listen for shared patterns. What did they all do? **3. Benchmark with AI.** Feed those patterns into AI. Ask it to analyze 100 calls and surface how often those behaviors show up across your team. You'll see the gap instantly. **4. Build your scorecard.** Create a simple rubric with 3 to 5 key behaviors. Test it. Refine it. Make sure it actually predicts outcomes, not just activity. **5. Coach to one thing.** Each rep focuses on improving one behavior per week. Not three. One. AI flags when they nail it. You reinforce why it matters. **6. Update the system.** As new patterns emerge, feed them back in. Your scorecard evolves. Your standards rise. The breakthrough wasn't the technology. It was turning what top performers do instinctively into something the whole team could learn. ## Even perfect systems fail without buy-in Reps can tell when leadership is checking boxes versus solving real problems. They've sat through enough "transformational" training to be skeptical. **Here's what actually works:** - **Start with their goals, not yours.** What are their goals? What's blocking them? What would make their job easier? Build enablement around their answers, not your assumptions. - **Show, don't tell.** Don't pitch AI as magic. Show them one call where it flagged something they missed. One insight that changed a deal. One behavior that's working for someone else on the team. - **Lead with the pacesetters.** Let early adopters prove the value. When the rest of the team sees their peers winning faster, they'll want in. AI should amplify relationships, not replace them. When people feel supported rather than surveilled, adoption follows naturally. ## The new growth equation The old playbook doesn't work anymore: hire fast, sell harder, fix problems later. Modern buyers are too sophisticated and teams are lean. Sustainable growth comes from a different approach: > **Enablement = Making what works visible + shareable + repeatable** Data shows you what's working, coaching makes it shareable, and AI makes it repeatable at scale. But here's the part people get wrong: you can't retrofit this. You can't bolt enablement onto a broken system and expect it to work. You build the scaffolding before you scale, codify success before you hire the next 10 reps. You nail your frameworks before you expand to new segments. You earn the right to grow by proving your system works with the team you have today. That's the difference between companies that scale successfully and companies that just get bigger and messier. Build the scaffolding early. Make every person on your team effective. Give them the clarity, tools, and confidence to win without constant oversight. That's how you scale. Not just revenue, but repeatability. ## FAQ ### What us sales enablement? Sales enablement in the AI era is the infrastructure that captures what works and makes it repeatable across the entire team, not just top performers. It combines data, coaching, and AI to turn instinctive behaviors into measurable, scalable frameworks. ### What does "good" sales enablement look like? Good sales enablement codifies what top performers do instinctively (cold call structure, discovery questions, objection responses) and turns it into simple, repeatable frameworks. It’s measurable, data‑driven, and focused on a few key behaviors that separate great calls from mediocre ones. ### How can I make "good" measurable in sales? Make “good” measurable by building lightweight rubrics for critical conversations. Focus on 3–5 key behaviors, like adding business context in the first 90 seconds or understanding the business problem. These rubrics turn coaching from gut feel into data‑driven feedback. ### What is multithreading? The multithreading breakthrough is changing one behavior that amplifies everything else: intentional multithreading from the first call. Instead of relying on one champion per account, reps build relationships with multiple stakeholders, which improves close rates and deal resilience. ### How to use AI in sales enablement? AI supercharges sales enablement by capturing what works and distributing it faster than humans can manually. It records every call, analyzes patterns from top performers, and flags gaps across the team. Coaches then focus on one behavior per rep per week, making coaching more precise and scalable. --- --- title: New in Vasco description: "This month, we’re doubling down on trust, visibility, and speed. From clean data syncs to deeper pipeline insights and smarter BI filters, these updates make it easier than ever to understand what’s happening, and act on it." canonical: "https://vasco.app/blog/product-spotlight-october-2025" date: "2025-10-15T00:00:00.000Z" authors: - Guillaume Jacquet jobTitle: "CEO & co-founder" readingTimeMinutes: 1 contentType: product-spotlight intent: foundations pillar: "RevOps systems & architecture" audiences: - enterprise --- # New in Vasco _Data destinations, pipeline visibility, and smarter BI filters_ ## Data destinations [https://www.youtube.com/embed/vO90BFPp_SI?iv_load_policy=3&rel=0&modestbranding=1&playsinline=1&autoplay=0&mute=1](https://www.youtube.com/embed/vO90BFPp_SI?iv_load_policy=3&rel=0&modestbranding=1&playsinline=1&autoplay=0&mute=1) ### When your CRM, warehouse, and BI tools rely on inconsistent data, every insight gets distorted. Vasco’s **Data Destinations** acts as a trust checkpoint, making sure only clean, standardized data flows into your stack. - You can now connect your **BigQuery, Snowflake, or Redshift** warehouse in minutes, and Vasco will automatically push refreshed data every day. - And when you need instant answers, **Gama**, our AI copilot, helps you dig into your live data, explore metrics in natural language, and export results directly to Google Sheets. ## ## Active & forecasted pipeline [https://www.youtube.com/embed/amqjmqCBJjI?iv_load_policy=3&rel=0&modestbranding=1&playsinline=1&autoplay=0&mute=1](https://www.youtube.com/embed/amqjmqCBJjI?iv_load_policy=3&rel=0&modestbranding=1&playsinline=1&autoplay=0&mute=1) ### Revenue teams no longer have to choose between visibility and foresight. With **Active Pipeline** and **Forecasted Pipeline**, you get both in one view. See how deals move across stages, inspect stalled opportunities, and understand each rep’s performance with the new _pipeline by owner_ filter. Then, connect your open pipeline to your revenue targets to reveal coverage ratios, weighted forecasts, and future gaps, all with contextual insights powered by **Gama**. ## ## Employee filter & time-based metrics [https://www.youtube.com/embed/syqG8ed5h34?iv_load_policy=3&rel=0&modestbranding=1&playsinline=1&autoplay=0&mute=1](https://www.youtube.com/embed/syqG8ed5h34?iv_load_policy=3&rel=0&modestbranding=1&playsinline=1&autoplay=0&mute=1) ### We’ve made **Explore Hub** even more powerful with two new capabilities. - You can now **filter metrics by employee**, to instantly drill down from team performance to individual contribution. - And with **custom time-based dimensions**, you can analyze metrics like time-to-close or conversion cycles by company size, industry, or region, uncovering the drivers behind your results. > _"From data integrity to performance visibility, these updates are designed to make RevOps simpler, smarter, and more actionable across every stage of your funnel."_ — Guillaume Jacquet, CEO & co-founder of Vasco --- --- title: The science of scaling according to Mark Roberge description: "It still happens. A founder raises a $10 million Series A and a board member says, “Hire 20 reps in November.” The founder is 30 years old, has never hired a rep, and the company barely has a repeatable process. Yet this is still how many startups scale, with no real evidence they’re ready." canonical: "https://vasco.app/blog/the-science-of-scaling-according-to-mark-roberge" date: "2025-10-11T00:00:00.000Z" authors: - Mark Roberge jobTitle: "Co-founder at Stage 2 Capital, prof at HBS, founding CRO at Hubspot" readingTimeMinutes: 3 contentType: article intent: strategy-insights pillar: "RevOps maturity & scaling journeys" --- # The science of scaling according to Mark Roberge _From product-market fit to go-to-market fit, led by retention._ ### After working with hundreds of post-seed companies, I’ve seen the same pattern over and over. The winners treat scaling like a science. The rest confuse early momentum with readiness. So let’s talk about when you’re actually ready to scale. ## Product-market fit is not a feeling Ask a room of founders what product-market fit means, and you’ll get a hundred different answers. Some say $500k in ARR. Others say “six happy customers.” Or a steady stream of inbound leads. Those are good signs, but they measure _market-message fit_, not product-market fit. Being good at sales or marketing can get you contracts, even if customers don’t truly need what you sell. That’s selling ice to Eskimos. If you want a quantifiable definition, it’s simple: **retention.** When customers stay, expand, and get ongoing value, you’ve built something real. I like to see **net dollar retention above 100 %** before declaring product-market fit. Otherwise, you’re just filling a leaky bucket. ## The problem: retention is lagging In early-stage SaaS, you can’t wait twelve months to see who renews. You need a _leading indicator_ of retention, a way to know today if customers will stick tomorrow. Here’s the framework I use: > **P % of customers do E event every T time** Three variables: - **P** = the percentage of customers - **E** = the key event that signals value - **T** = the time window That’s your **Leading Indicator of Retention (LIR).** ## Examples of LIR in action - **Slack:** 70 % of customers send 2,000 messages per month. - **Dropbox:** 85 % of customers back up their device every day. - **HubSpot:** 80% of users adopt 5 or more features in a 25-feature platform. ![](https://cdn.sanity.io/images/ys8gstp8/production/ce4a1059d612c7c1b82367a0f8db8f1bbec2f0df-1920x540.png?w=1600&fit=max&auto=format) Those companies didn’t set revenue goals like “hit $1 million ARR.” They set _usage_ goals like “get 70 % of customers sending 2,000 messages.” That second goal builds a foundation you can actually scale. You don’t need regression analysis to start. Just track, cohort by cohort, what percentage of new customers hit your event in month 1, 2, 3… and keep pushing that number up. When it climbs and stays there, you have real product-market fit. ## From product-market fit to go-to-market fit Even then, you’re not ready to scale yet. Product-market fit proves that customers find value. **Go-to-market fit** proves you can deliver that value _profitably and repeatedly._ The metric for that is **unit economics**: your CAC, LTV, and payback period. But again, those are lagging indicators. You need to _extract_ them into variables you can measure today: average deal size, close rate, sales cycle, cost per lead, rep ramp time.If the algebra behind those numbers works out to a healthy LTV : CAC > 3, you’re on the right path. ![](https://cdn.sanity.io/images/ys8gstp8/production/2d75db2dbbe1f18d3f0fc8c3ab1f63f4aab2b875-1920x1080.png?w=1600&fit=max&auto=format) The key is sequencing. Work on product-market fit first, then go-to-market fit. If you optimize both at once, you risk building a repeatable motion on the wrong market. ## Scaling is a pace, not an event When both fits are in place, the next question is speed. How fast do you scale? Most companies treat it like a light switch: raise capital → hire 20 reps. That’s not scaling, that’s gambling. Think of it as _pacing._ Maybe start with two reps every other month. Watch your leading indicators of retention and unit economics. If both stay green for six months, double the pace. If they turn red, slow down, fix it, then accelerate again. Those metrics become your **speedometer. **Many startups only realize they were going too fast when churn hits nine months later. You’ll know nine months _earlier._ ## How to operationalize the science of scaling 1. Define your LIR (the single behavior that predicts retention). 2. Instrument it in your product logs. 3. Track cohorts monthly until the trend line turns upward. 4. Back-solve your unit economics into present-day controllables. 5. Scale gradually, using your LIR and unit-econ dashboards as the green light. Do that, and you won’t need to argue with your board about whether you’re ready to scale. You’ll have data that speaks for itself. ### Final thought Product-market fit proves you’ve built something people need. Go-to-market fit proves you can deliver it efficiently. Scale only when you have both, and let retention, not revenue, tell you when that day has come. ## FAQ ### What does “the science of scaling” mean? The science of scaling means treating growth as a disciplined, data‑driven process rather than a one‑off hiring spree. It’s about proving product‑market fit, then go‑to‑market fit, and finally scaling at a pace your retention and unit economics can sustain. ### How is product‑market fit different from market‑message fit? Market‑message fit means people respond to your messaging or sales motion; product‑market fit means customers actually stay, expand, and get ongoing value. A quantifiable sign of product‑market fit is net dollar retention above 100%. ### Why is retention the best measure of product‑market fit? Retention is the best measure because it shows whether customers truly need what you sell. If they renew, expand, and keep using the product, you’ve built something real. Revenue alone can be misleading if churn is high. ### What is a Leading Indicator of Retention (LIR)? A Leading Indicator of Retention (LIR) is a key behavior that predicts whether customers will stick. It’s defined as: P% of customers do E event every T time, where P is the percentage, E is the key event, and T is the time window. Track this cohort by cohort to see if it improves over time. ### How can I define my own LIR? Define your LIR by identifying the behavior that signals real value for your product. For example, Slack’s LIR is 70% of customers sending 2,000 messages per month; Dropbox’s is 85% backing up daily. Track this event for new cohorts and push the percentage up. ### What is go‑to‑market fit, and how is it different from product‑market fit? Go‑to‑market fit means you can deliver your product profitably and repeatedly. Product‑market fit proves customers find value; go‑to‑market fit proves you can acquire and serve them efficiently, with healthy unit economics (LTV:CAC > 3). ### How do unit economics support the science of scaling? Unit economics support scaling by turning lagging indicators (CAC, LTV, payback) into present‑day controllables like average deal size, close rate, sales cycle, cost per lead, and rep ramp time. If the math works out, you’re on the right path to scale. --- --- title: "Don’t trust your conversion rates?" description: "Conversion rates are one of the most important metrics in Revenue Operations. They tell you whether your funnel is healthy, where prospects drop off, and how much revenue you can realistically expect from your pipeline. Yet, conversion rates are often missing, inconsistent, or buried in spreadsheets that no one fully trusts." canonical: "https://vasco.app/blog/conversion-rates" date: "2025-10-06T00:00:00.000Z" authors: - Alec Oghassabian jobTitle: RevOps Expert readingTimeMinutes: 2 contentType: article intent: playbooks-methods pillar: "RevOps systems & architecture" --- # Don’t trust your conversion rates? _Methods, pitfalls, and best practices_ The problem is, when the numbers are off, so are the decisions that depend on them: forecasting, budgeting, resource allocation. This article will walk you through why conversion rates matter, how they’re calculated, and why at Vasco we believe you need something stronger than spreadsheets to get them right. ## Why conversion rates matter Conversion rates measure efficiency. They answer the question: > Out of everyone who engaged with us, how many successfully moved to the next stage? That could mean: - Turning a lead into a qualified opportunity, - Moving an opportunity to a closed deal, - Converting a free trial into a paid subscription. When tracked consistently, conversion rates become decision-making power tools. They allow RevOps and GTM leaders to: ![](https://cdn.sanity.io/images/ys8gstp8/production/6ee473c618744d7743bcd953b1c518b33c5075c7-1920x540.png?w=1600&fit=max&auto=format) In short: without reliable conversion rates, you’re flying blind. ## The many ways to calculate conversion rates Here’s where things get tricky. There isn’t just one way to measure a conversion rate. Depending on your funnel structure, sales cycle, and data availability, different methods apply: - **Absolute Conversion Rate:** best for quick snapshots or directional insights. - **Cohorted Conversion Rate:** best for comparing sources or periods over time. - **Reversed Cohort Conversion Rate:** best when sales cycles are long and you need to correct lag. - **Active Based Conversion Rate:** best for short-term performance checks and weekly reviews. - **Ratio Conversion Rate: **best for stabilizing results quickly as deals reach an outcome. - **Aggregated Conversion Rates:** best for high-level reporting across channels or time periods. ## Conversion rate cheat sheet Get the cheat sheet to see how each conversion rate method works, with definitions, formulas, and pros and cons. ![conversion rates](https://cdn.sanity.io/images/ys8gstp8/production/20af6b8b618bd905685ee787a80578fb5337184b-2000x1545.png?w=1600&fit=max&auto=format) > The key insight: **it’s all about the denominator.** Are you counting against new leads, total active pipeline, or accounts that have reached an outcome? Different denominators lead to very different stories. ## Why spreadsheets won’t cut it for conversion rates Technically, you can start with a spreadsheet, and for a while, it works. But as soon as your funnel gets more complex (think multiple channels, varying sales cycles, overlapping cohorts…) the math gets out of control. Problems you’ll run into: - **Time lag distortion**: conversions don’t happen in the same period as acquisition. - **Denominator mismatch**: counting against the wrong population leads to inflated or even impossible numbers (>100%). - **Inconsistent definitions**: each rep or team calculates things differently. - **Manual upkeep**: hours spent cleaning and importing data from multiple tools, with no automatic flow-in. The result: conversion rates are always lagging instead of real-time. > In a spreadsheet, every assumption or formula tweak introduces new risks. And by the time the numbers are ready, they’re already outdated. ## The Vasco approach At Vasco, we believe conversion rates should be: ![](https://cdn.sanity.io/images/ys8gstp8/production/b3b471322c9c4d7fed984cebb8f830567c648a3e-1920x540.png?w=1600&fit=max&auto=format) > With Vasco, conversion rates are always tied to the right methodology, without manual formulas or reconciliations. That means you get conversion rates you can trust. ## Final thought More than just metrics, conversion rates are the lens through which you evaluate efficiency, effectiveness, and momentum. But their usefulness depends entirely on how they’re defined and applied. Get them wrong, and your strategy falters. Get them right, and they unlock predictable, scalable growth. That’s why Vasco makes conversion rates not only accurate, but effortless! ## FAQ ### Why do conversion rates matter in revenue operations? Conversion rates are important because they measure efficiency across the funnel. They show how many prospects move from one stage to the next, like lead to qualified opportunity, opportunity to closed deal, or free trial to paid subscription. Without reliable conversion rates, RevOps and GTM leaders can’t forecast, budget, or allocate resources effectively. ### What do conversion rates tell you about your funnel? Conversion rates tell you whether your funnel is healthy, where prospects drop off, and how much revenue you can realistically expect from your pipeline. They highlight bottlenecks, inefficiencies, and opportunities for optimization, turning raw data into actionable insights. ### What are the different ways to calculate conversion rates? "There are several methods, each suited to different scenarios: Absolute Conversion Rate: Best for quick snapshots or directional insights. Cohorted Conversion Rate: Best for comparing sources or periods over time. Reversed Cohort Conversion Rate: Best for long sales cycles and correcting lag. Active Based Conversion Rate: Best for short‑term performance checks and weekly reviews. Ratio Conversion Rate: Best for stabilizing results quickly as deals reach an outcome. Aggregated Conversion Rates: Best for high‑level reporting across channels or time periods." ### What are common pitfalls when calculating conversion rates? "Common pitfalls include: Time lag distortion: Conversions don’t happen in the same period as acquisition. Denominator mismatch: Counting against the wrong population leads to inflated or impossible numbers (>100%). Inconsistent definitions: Each rep or team calculates things differently. Manual upkeep: Hours spent cleaning and importing data from multiple tools, with no automatic flow‑in." ### How does Vasco improve conversion rate accuracy? Vasco improves conversion rate accuracy by tying them to the right methodology automatically, without manual formulas or reconciliations. It unifies pipeline, forecast, and GTM data so teams can see how their motions translate into growth, with real‑time, trustworthy conversion rates. --- --- title: Reverse-engineering your board target description: Planning season is changing. What used to be a static spreadsheet ritual is becoming a continuous operating system for growth. canonical: "https://vasco.app/blog/planning-playbook" date: "2025-10-06T00:00:00.000Z" authors: - Guillaume Jacquet jobTitle: "CEO & co-founder" readingTimeMinutes: 9 contentType: guide intent: playbooks-methods pillar: "Revenue planning, forecasting & economics" audiences: - revops - cros - start-ups - scale-ups - fractional - founders --- # Reverse-engineering your board target _A practical playbook for planning season_ ## Get the guide And the annual planning grind is back. Built with the [LeanScale](https://leanscale.team/) experts, this guide shows how to turn a board net-ARR goal into a working plan that’s ambitious, realistic for your team’s capacity, and aligned with your CFO’s guardrails. [![](https://cdn.sanity.io/images/ys8gstp8/production/c6e70e73011c0f480289dd92d0b652915e253b20-1920x1080.png?w=1600&fit=max&auto=format)](https://db5kh.share.hsforms.com/2hpCDQIIoT-St2FGkkJ4WDQ) ## The 5 key sections of the guide - Unpacking the growth goal - Top-down planning - Bottom-up planning - Budget and unit economics guardrails - Scenario modelling and reporting to plan > Boards rarely hand you the map. They set a macro growth expectation and expect leadership to reverse-engineer the path. That's why, together with Leanscale, we’re sharing a practical path to go from board target to working plan without vanishing into spreadsheet hell. --- --- title: AI in sales description: "AI tools are reshaping the daily lives of sales reps and GTM leaders, but not without their challenges. In the video below, Justin from Vasco and Jacob from Attention shared their firsthand experiences on how AI is transforming workflows, where it falls short, and what the future might hold." canonical: "https://vasco.app/blog/ai-in-sales" date: "2025-09-02T00:00:00.000Z" authors: - Justin Hudon jobTitle: Head of Sales and Customer Success readingTimeMinutes: 4 contentType: article intent: playbooks-methods pillar: "AI in GTM & RevOps" --- # AI in sales _Workflow wins, real limits, and what’s next_ ## 🎥 Watch the convo Justin Hudon (Vasco) and Jacob Fleisher ([Attention](https://www.attention.com/)) brought frontline perspectives on the promises and pitfalls of AI in sales. **Prefer reading? Scroll down for a text summary.** [https://www.youtube.com/embed/y8YLKjVIcOA?iv_load_policy=3&rel=0&modestbranding=1&playsinline=1&autoplay=0&mute=1](https://www.youtube.com/embed/y8YLKjVIcOA?iv_load_policy=3&rel=0&modestbranding=1&playsinline=1&autoplay=0&mute=1) ## 1. The biggest day-to-day change for reps The most immediate impact of AI is on **efficiency and productivity**. Routine tasks like follow-up emails or CRM updates, traditionally time-consuming and often dreaded, are now handled in minutes rather than hours. AI automates AND enhances: meeting summaries are more accurate, methodologies like SPICED or MEDDICC can be filled directly into CRMs, and reps can focus more on customer conversations instead of admin work. The result? More time back in the day, higher-quality customer interactions, and a smoother onboarding process for both new hires and new customers. > _“A 10 or 15 minute task now takes one or two minutes. Multiply that over five or six meetings a day, five days a week… that’s a lot of time saved, but also a lot more high-quality touch you’re putting in front of prospects.”_ — Jacob ## 2. Workflow wins vs. wrecks Not all AI implementations have been smooth sailing. On the positive side, AI-powered prospecting and task orchestration have improved **targeting and timing**. At the same time, overreliance on tools like ChatGPT can backfire when tasks could be completed faster manually. Prompting, in particular, has become an art, and misuse can slow teams down. Challenges also appear when adoption is left too open-ended. For instance, giving BDRs free rein to build their own account lists with AI can create inconsistency and inefficiency. A **top-down approach to data enrichment and account prioritization** helps ensure reps start with the right accounts and contacts, making AI much more effective at the top of the funnel. > _"I've seen ChatGPT adoption get a little excessive. Sometimes it takes longer to write prompts and refine outputs than just writing the email manually."_ — Justin ## 3. Excited or fatigued? Are reps tired of yet another wave of tools? Not quite. Most land somewhere between **excited and skeptical**. There’s enthusiasm for the productivity gains AI brings, but also hesitancy from top performers who wonder if new workflows really outperform their tried-and-true methods. Adoption tends to work best when AI integrates seamlessly into existing processes, rather than forcing reps into entirely new platforms. > _"They're excited because the benefits of AI are great, but there's hesitancy from top performers — are they really going to rearchitect the way they've worked for 10 years?"_ — Jacob ## 4. The AI wish list When imagining the “impossible” AI features of tomorrow, two pain points stand out: - A **self-updating CRM** that automatically pulls structured data from calls, emails, and contracts, eliminating the need for manual input. - An AI system that delivers a **perfectly prioritized account list**, enriched with context and signals, and even matched to the rep best suited to handle each prospect. The industry isn’t there yet, but that’s the direction things are moving. > _"Imagine an AI that not only builds your TAM but also prioritizes accounts with the right signals and even matches each to the rep best suited to handle them."_ — Jacob ## 5. What AI can’t replace Despite the hype, some parts of sales remain firmly human. **Business acumen and genuine curiosity** during discovery calls are irreplaceable. AI can draft scripts, but it can’t replicate the thoughtful, real-time questioning that top reps use to uncover pain points. Just as importantly, **passion and energy** still carry weight. Buyers respond to conviction, trust, and relationships: human qualities no algorithm can fake. > _"Top performers use real-time questioning to uncover pain points. AI isn’t there"_ — Jacob ## 6. Buying AI tools With new products launched daily, cutting through the noise is a challenge. **Peer recommendations** remain one of the most reliable ways to identify which tools truly add value. The real test is not whether a tool looks impressive, but whether it addresses a pressing business problem. > _"There’s so much noise that I always come back to peer recommendations. If a tool is being used successfully by someone I trust, that cuts through faster than any ad."_ — Justin ## 7. HubSpot’s new ChatGPT plug-in: hit or miss? Even with the hype, not every AI integration is ready for primetime. The new ChatGPT plug-in for HubSpot, for example, shows promise but struggles without the proper business context. Sales processes, lifecycle definitions, and go-to-market structures are often too nuanced for a generic AI layer to interpret accurately. Without clean, structured data, the output risks being more noise than insight. > _"The issue is that ChatGPT doesn’t know your go-to-market structure or what actually defines an MQL or a sales-accepted lead. Without that business context, you’re not necessarily getting valuable output."_ — Justin ## 8. Predictions for 2026 Looking ahead, AI in sales is expected to become far more **predictive and dynamic**. Instead of surfacing generic best practices, tools will tailor insights to specific industries, buyer behaviors, and even rep strengths. AI will both support execution and help orchestrate creative, personalized engagement strategies that drive revenue. > _"Instead of surfacing generic playbook items, it’ll give dynamic, contextual insights. Like, in the last five pharma deals CIOs got involved earlier, so maybe bring in the CIO now. Reps will execute at a much higher level."_ — Jacob ## The takeaway AI is already transforming sales workflows, from admin automation to smarter prospecting. But the real breakthroughs will come when AI systems can prioritize accounts with nuance, deliver predictive insights, and let reps focus on what only humans can do: building trust, asking the right questions, and selling with passion. --- --- title: Time to AI description: "The pressure to implement AI in GTM is real. Budgets are shifting. Competitors are experimenting. And yet, most people still feel overwhelmed." canonical: "https://vasco.app/blog/time-to-ai" date: "2025-09-02T00:00:00.000Z" authors: - "Sébastien Rothlisberger" jobTitle: "CTO & co-founder" readingTimeMinutes: 11 contentType: guide intent: playbooks-methods pillar: "AI in GTM & RevOps" audiences: - revops - cros - start-ups - scale-ups - fractional - founders --- # Time to AI _A stoic guide to AI-powered RevOps_ ## Get the guide Move forward with confidence, knowing when AI can accelerate your GTM workflows, and when it won’t. > This guide strips away the buzzwords, explains what **agentic AI **really does, and shows how GTM teams are experimenting with it today (including where it falls short). ![](https://cdn.sanity.io/images/ys8gstp8/production/da8ce8114d278eb1281ad53b29bf6976507cbb8b-4560x2565.png?w=1600&fit=max&auto=format) ## The three key sections of the guide - **Back to basics**: How agentic AI actually works - **AI isn’t magic:** Core challenges and common myths - **Build, Buy or assemble**: How to activate AI in your GTM > At the end of the day, AI won’t replace your judgment. Used wisely, it can take on the repetitive, dull parts of RevOps so you can focus on **strategy, creativity, and growth**. --- --- title: How VCs evaluate startup maturity for Series A and B description: "For early-stage founders, raising a Series A or B often comes down to one question: are we ready? From the investor side, the question sounds more like: how efficient and mature is this company?" canonical: "https://vasco.app/blog/how-vcs-evaluate-startup-maturity-for-series-a-and-b" date: "2025-08-02T00:00:00.000Z" authors: - David Fontaine jobTitle: "VC @ Framework" readingTimeMinutes: 4 contentType: article intent: strategy-insights pillar: "RevOps maturity & scaling journeys" --- # How VCs evaluate startup maturity for Series A and B _Early-stage GTM signals that drive investor confidence_ ## Maturity goes beyond ARR or growth rate It’s also about structure, clarity, and momentum. Are the foundations in place for scale? Can the team explain what’s working, what’s being tested, and what’s coming next? This article unpacks the signals VCs look for at each stage of early growth to determine whether a new round of funding is well-timed, or premature. ## The path to maturity Early-stage startups move through four stages of growth: ![](https://cdn.sanity.io/images/ys8gstp8/production/3c2cbf8dd9c4b5b60ed97bfd620c9a915128db11-1920x1080.png?w=1600&fit=max&auto=format) ### 1. Problem–solution fit At this first stage, the goal is to validate the problem and confirm that the product being built addresses it. There might be a handful of early adopters, often from the founder’s network. The focus is on shipping, testing, iterating, and learning fast. ### 2. Product–market fit (PMF) The company’s product delivers clear value and customers are coming back. Demand, adoption and retention starts to show consistency and sales efforts feel less like a push from the vendor and more like a pull from the buyer. However, in high velocity and innovation markets, PMF is not a single event, companies can gain and lose it as their markets evolve. 1. Go-to-market fit (GTM fit) This is when [ICP](https://vasco.app/blog/your-icp-operationalized), pricing, and sales motion begin to form a repeatable system. Acceleration starts in hiring, messaging, and pipeline-building. The GTM engine is taking shape, and at least one motion (e.g. outbound) is proving repeatable. ### 4. Scaling The company is doubling down on what works: GTM efficiency metrics and quota attainment data provide comfort for more hires, more channels, more markets. With repeatable motions and strong funnel visibility, the focus shifts to execution. The machine is up and running. It’s time to accelerate. So, what do we, as investors, look for at Series A and B? Here's a closer look. ## Series A: “Show us your foundation” Series A typically happens between PMF and early GTM-fit. It marks the transition from promising traction to early repeatability. At this stage, investors look for signs that the company is starting to build a GTM model that can scale. ### A few indicators tend to stand out: - **Repeatability**: At least one sales motion is active and starting to deliver consistent results, even if it's still founder-led. - **Clear ICP**: The pipeline is structured around a well-defined [ideal customer profile](https://vasco.app/blog/your-icp-operationalized) and buyer personas, not scattered and opportunistic deals. - **Data tracking enablement:** The team knows what to track, and has the tools in place to follow through: dashboards, BI, or ideally, a platform like [Vasco](https://vasco.app/). - **North star clarity:** Leadership is aligned on what success looks like and can articulate the path to get there. At Series A, it’s less about hitting targets and more about having the ability to showcase and measure velocity, momentum and progress towards true GTM-fit. ## Series B: “Predictability kicks in” Series B comes once GTM-fit is proven, and the company is ready to scale. The playbook is clearer, and investors look for signs that the business is operating at steady state. ### What VCs look out for: - **Less founder-drag**: One or two sales motions are delivering, and [no longer solely rely on the founder](https://vasco.app/blog/from-founder-led-sales-to-a-scalable-revenue-engine) or their network to close deals and hit targets. Execution is team-led. - **Scalable hiring**: The company has a firm grip on sales capacity and execution, knows which sales profiles tend to succeed, how to hire them, and how long it takes for them to ramp. - **Codified onboarding**: Sales enablement is structured. New hires follow a repeatable path to productivity, with clear playbooks and systems. - **Performance benchmarks**: Metrics are no longer directional. They’re used to manage performance and guide decisions. This round is typically about acceleration. Investors come in to fuel a recipe that’s already working. ## An example from the field: Zaddons road to Series A ![](https://cdn.sanity.io/images/ys8gstp8/production/4bb9cbcf6b901f81a0d28daae9b8d8b81127f537-1920x540.png?w=1600&fit=max&auto=format) ### [Zaddons](https://www.zaddons.com/) builds specialized solutions that enhance human resource information systems (HRIS), focused on complex bidding and scheduling. Co-founder Sébastien Massicotte had previously led an HRIS consultancy, where he repeatedly saw the same acute pain point, giving the team a head start on problem–solution fit. After raising a Seed+ round in Q2 2024 (with [Framework](https://www.framework.vc/en) participating), the company validated product–market fit through growing founder-led sales. Over the following 12 months, several GTM-fit signals began to emerge: - **Clear ICP** and buyer personas: HR and Ops leaders in mid- to large-sized healthcare, manufacturing, and logistics companies - Strong and accelerating **lead-to-SAL conversions** - Early traction with a **second motion **via partnerships with HRIS vendors and system integrators (SIs) - **Sales hiring playbook** taking shape: defined AE and sales leadership profiles, with onboarding and performance tracking frameworks ready > “We had validated market fit. Demand was accelerating and outpacing our lean sales setup. Scaling became a necessity—not to grow faster, but to stop leaving revenue on the table.” — Louis-Sébastien Laprise, co-founder @ Zaddons Zaddons is now approaching GTM-fit and in good shape to raise Series A, with the right motion, in the right markets, and the systems to scale it. ## Wrapping up: maturity is a signal, not a score Early-stage teams are often experts in the problem they’re solving. But go-to-market is a discipline of its own, and one that can make or break the business. Beyond growth, understanding how to build, test, and scale your GTM strategy allows you to raise capital, allocate resources, and turn a great product into a viable company. Investors are looking for direction and results, of course. But even more so, the ability to generate them again and again. > Maturity is evaluated by the ability to know what matters now, and what to build next. ## FAQ ### What do VCs mean by “startup maturity”? VCs mean more than just ARR or growth rate when they talk about maturity. They’re looking for structure, clarity, and momentum, whether the company has a repeatable GTM model, clear ICP, and the ability to explain what’s working, what’s being tested, and what’s coming next ### What are the four stages of early‑stage growth? "The four stages are: 1. Problem–solution fit: Validate the problem and confirm the product addresses it. 2. Product–market fit (PMF): The product delivers clear value, demand and retention are consistent, and sales feel more like a pull than a push. 3. Go‑to‑market fit (GTM fit): ICP, pricing, and sales motion form a repeatable system, with at least one motion proving repeatable. 4. Scaling: The company doubles down on what works, with strong funnel visibility and execution focus." ### What do VCs look for at Series A? "At Series A, VCs look for: Repeatability: At least one sales motion is active and delivering consistent results, even if founder‑led. Clear ICP: The pipeline is structured around a well‑defined ideal customer profile, not scattered deals. Data tracking enablement: The team knows what to track and has tools (dashboards, BI, or a platform like Vasco) to follow through. North star clarity: Leadership is aligned on success and can articulate the path to get there." ### What do VCs look for at Series B? "At Series B, VCs look for: Less founder‑drag: One or two sales motions deliver without relying solely on the founder or their network. Scalable hiring: The company knows which sales profiles succeed, how to hire them, and how long they take to ramp. Codified onboarding: Sales enablement is structured, with clear playbooks and systems. Performance benchmarks: Metrics are used to manage performance and guide decisions, not just directionally." ### How does maturity differ from ARR or growth rate? Maturity differs because it’s about the quality of growth, not just the quantity. A company with lower ARR but a repeatable GTM model and clear ICP is more mature than one with high growth but scattered deals and no system. ### Why is ICP so important for Series A? ICP is important because it shows the company understands who its best customers are and can focus its GTM efforts there. A clear ICP reduces noise, improves conversion, and makes it easier to scale efficiently. ### How does a revenue architecture platform like Vasco support maturity? Vasco supports maturity by unifying pipeline, forecast, and GTM data so teams can see how their motions translate into growth. It surfaces repeatability signals, tracks ICP‑driven pipeline, and powers Bowtie‑aligned workflows that turn early‑stage signals into scalable revenue engines. --- --- title: Brand Strategy description: "From strategy to implementation, learn when to invest, why it matters, and how to bring your brand to life across your GTM." canonical: "https://vasco.app/blog/brand-strategy-guide" date: "2025-08-01T00:00:00.000Z" authors: - Sophie Geoffrion jobTitle: "Brand & Content Lead" readingTimeMinutes: 9 contentType: guide intent: strategy-insights pillar: "RevOps maturity & scaling journeys" audiences: - revops - cros - start-ups - scale-ups - fractional - founders --- # Brand Strategy _A tactical guide for building and scaling B2B SaaS brands_ ## Get the guide ### Branding in B2B SaaS? It might be your most underrated growth lever. In early-stage companies, branding often gets dismissed as a “nice-to-have.” But when you're trying to stand out, align your team, and connect with buyers who are drowning in options, brand becomes a serious asset. > This guide shows how to turn brand into a GTM multiplier, with a strategy that evolves from PMF to scale. ![](https://cdn.sanity.io/images/ys8gstp8/production/c7d9f3c772c9d2002e5d9eeb45a68ef1f110309c-1920x1080.png?w=1600&fit=max&auto=format) ## The four key sections of the guide - Why and when branding matters in SaaS - Laying the groundwork for a scalable brand - Working with external partners - Operationalizing your brand across your GTM > Brand amplifies your entire GTM engine. Start small, stay consistent, and scale as you grow. --- --- title: "Designing for product-led growth (PLG)" description: "In a product-led motion, your product becomes the front door, the sales call, and the onboarding session all at once. There’s no AE to tailor the pitch, no CSM to unblock the journey. Every screen and interaction in the user journey has the potential to either move them forward or make them drop off." canonical: "https://vasco.app/blog/designing-for-plg" date: "2025-08-01T00:00:00.000Z" authors: - Arthur Monnet jobTitle: Senior Product Designer readingTimeMinutes: 3 contentType: article intent: playbooks-methods pillar: "GTM & RevOps execution" audiences: - enterprise --- # Designing for product-led growth (PLG) _5 strategies to accelerate adoption_ ### For SaaS teams exploring PLG or refining it, here are five product strategies that make adoption smoother and faster, illustrated with real examples from popular tools. ## 1. Make room for self-discovery When users land in your product, they’re expecting **progress**, not a tour. Design should guide them just enough to give a sense of momentum, without making it feel like a lesson. Think missions with **clear goals, quick feedback, and small wins **along the way. This is less about gamification and more about **building confidence** early. Let users explore, experiment, and feel like they’re the ones making things happen. ![Notion](https://cdn.sanity.io/images/ys8gstp8/production/1d75ae47d6fed00a211de735d9ae18dbfd062081-1920x1080.png?w=1600&fit=max&auto=format) > _Take [**Notion**](https://www.notion.com/), for example. You unlock understanding as you go, without ever feeling blocked by a “first, watch this” wall._ ## ## 2. Personalize the experience for different styles and roles Not every user learns the same way. Some are builders who **dive straight into the UI**. Others prefer **structure, explanations, and a clear next step**. Both are valid, and both deserve a path that fits their style. The same goes for **role-based personalization**. A marketer, a sales leader, and a developer won’t explore your product in the same way or look for the same outcomes. Asking just one or two smart questions up front can let you frame the experience with more relevance and less noise. Good design doesn’t assume it knows the user. It adapts to **meet them where they are**. ![](https://cdn.sanity.io/images/ys8gstp8/production/3a2aea3de59cc254ed70dbf91ff27a08bd6667fd-1920x1080.png?w=1600&fit=max&auto=format) ## ## 3. Support collaboration, at the right moment B2B products become more valuable when they’re shared. But inviting teammates too early can backfire. People want to **understand what they’re recommending** before they loop others in. Design for **shareability first**. Let users export graphs, copy insights, or share snapshots with their team. Add a subtle layer of branding when they do. This builds **awareness** inside the company while still respecting the user’s pace. Once value is clearer, make it easy to **bring collaborators in**. This could mean adding teammates in one click, assigning pre-set roles, or automatically inviting others based on usage patterns. The goal is to help adoption grow naturally across the account. ![](https://cdn.sanity.io/images/ys8gstp8/production/8a9354885d66544767cd52ede41923fd7c90187e-1920x1080.png?w=1600&fit=max&auto=format) ## ## 4. Let users experience value before they commit Free trials and freemium plans are core to any PLG motion. They come in different forms, but the goal is always the same: help users **explore the product, see its potential, and build trust** before asking them to pay. - A **free trial** offers access for a limited time, giving users a chance to experience the full value without risk. - A **freemium** model grants ongoing access to a limited version, often restricting usage (like Loom capping the number of videos) or gating advanced features. - Some tools go further by **displaying all features** from day one, even if only a few are usable. The key is to surface value early and let the upsell follow naturally. ![Loom and Hubspot](https://cdn.sanity.io/images/ys8gstp8/production/5d1f385b44f46886cfedb3ac70889ac0e5941011-1920x1080.png?w=1600&fit=max&auto=format) > [**Loom**](https://www.loom.com/) combines freemium and free trial: users can start with a limited free plan and unlock premium AI-powered features for 14 days. [**HubSpot**](https://www.hubspot.com/) shows all its hubs and features in the UI — including gated ones like SMS — encouraging users to explore what's possible before choosing to upgrade. ## ## 5. Track user behavior to uncover and fix drop-offs In a sales-led motion, friction is patched by humans. In PLG, it just becomes churn. **Analytics** should be baked into your product decisions from day one. Track key milestones in the journey — like onboarding completion or feature activation — and monitor where users drop off. This tells us more than any NPS survey ever could. Group behaviors by cohort and role. The **patterns** are there, and it’s crucial to have the right tools in place to uncover them and act on what you find. ![Amplitude](https://cdn.sanity.io/images/ys8gstp8/production/e103e916d98b9898745305b5c4a435742e66af0b-1920x1080.png?w=1600&fit=max&auto=format) > We’ve used tools like [**Amplitude**](https://amplitude.com/) to map user paths and track whether users complete key actions. We also like that it lets you test value with dummy data before committing. ## ## Bonus: When is the right time to implement PLG? PLG isn’t a perfect fit for every product. Or at least, not to the same extent. The lower your **average contract value (ACV)**, the more essential it becomes: users expect to self-serve, and your model needs to scale efficiently with **low costs of acquisition (CAC)**. On the flip side, the higher the ACV, the more prospects expect high-touch support, custom onboarding, and a sales conversation. ![](https://cdn.sanity.io/images/ys8gstp8/production/fd9758bda10ec10aea0d99ba565496005603f63a-1920x1080.png?w=1600&fit=max&auto=format) ### If you're considering PLG, here’s what to look for before you commit: - You have at least one clear, standalone **use case** that delivers value without human help. - Your **activation path** is observable. You know what “successful” users actually do. - You can **personalize early value** without asking users to set up everything from scratch. - You’re ready to **track product usage** and iterate based on what you learn. It works best when your product is ready to sell itself and your team is ready to act on what the data shows. > PLG takes more than a free trial. It requires a product experience designed to deliver early value, adapt to different users, and remove friction at every step. ## FAQ ### What does “designing for PLG” mean? Designing for PLG means structuring your product so users can discover value on their own, without heavy sales or marketing intervention. The product itself drives acquisition, activation, and expansion by guiding users to “aha” moments quickly and intuitively. ### How is PLG different from sales‑led growth? In sales‑led growth, deals are driven by reps and marketing campaigns. In PLG, the product is the primary engine of growth: users try, adopt, and expand through their own experience, with sales stepping in later for larger or more complex accounts. ### How should product experiences be personalized for PLG? Product experiences should be personalized by role, use case, and behavior. For example, a sales rep sees different onboarding and prompts than a RevOps leader, so each user feels the product was built specifically for them, which increases engagement and retention. ### What does a clear expansion path look like in PLG? A clear expansion path means users can easily upgrade, add seats, or unlock features as their needs grow. This could include usage‑based pricing, tiered plans, or in‑product prompts that highlight higher‑value capabilities when users are ready for them. ### How does PLG impact customer acquisition cost (CAC)? PLG typically lowers CAC because the product itself drives signups, trials, and word‑of‑mouth referrals. Instead of relying on expensive outbound campaigns, growth comes from users who discover, adopt, and share the product organically. ### What role does data play in designing for PLG? Data plays a central role by revealing where users get stuck, what features drive activation, and when they’re ready to expand. PLG‑driven teams use behavioral data to refine onboarding, personalize journeys, and optimize the product for growth. ### How does a revenue architecture platform like Vasco support PLG? Vasco supports PLG by unifying product usage, pipeline, and revenue data so teams can see how PLG motions translate into growth. It surfaces activation signals, tracks expansion paths, and powers Bowtie‑aligned workflows that turn product‑led users into revenue‑driven accounts. --- --- title: The attribution guide description: "Vasco's practical guide to choosing the right attribution model, setting it up in HubSpot, using AI to automate the hard parts, and aligning your GTM teams around what truly drives revenue." canonical: "https://vasco.app/blog/the-attribution-guide" date: "2025-06-16T00:00:00.000Z" authors: - Alec Oghassabian jobTitle: RevOps Expert readingTimeMinutes: 8 contentType: guide intent: playbooks-methods pillar: "RevOps systems & architecture" audiences: - revops - cros - start-ups - scale-ups - fractional - founders --- # The attribution guide _Making sense of what drives revenue_ ## Get the guide Without a clear, shared framework to tie activities to outcomes, decisions become subjective. Channels get undervalued. Teams argue over what drives pipeline. And when attribution feeds into things like performance reviews, variable compensation, budget distribution... misalignment quickly turns into mistrust and tensions between teams. **That’s why solving attribution is bigger than just picking the right model**. It’s about giving every team a shared source of truth. And Vasco helps you get there! ![Read the guide](https://cdn.sanity.io/images/ys8gstp8/production/30e6a2647d72c24f6073cc10d88ad24d846fc9cf-1920x1080.jpg?w=1600&fit=max&auto=format) ## The four key sections of the guide 1. **What is attribution?** – A clear definition, why it matters, and how it powers better decisions. 2. **Attribution models **– 8 models explained through a visual customer journey, with pros and cons. 3. **Choosing the right model** – Guidance tailored to your sales cycle and go-to-market approach. 4. **Implementing attribution in HubSpot** – A step-by-step setup guide with AI tool recommendations. > Because robust attribution strengthens how you plan, operate, and grow. --- --- title: The gravitational pull of daily digests description: "Every SaaS company claims to be “data-driven.” Yet, behind closed dashboards, each GTM team crafts its own version of the truth. Sales leans on the CRM, marketing swears by attribution reports, the CRO lives in a board deck… And RevOps? They're caught in the middle, pulled in every direction, trying to make sense of conflicting narratives." canonical: "https://vasco.app/blog/the-gravitational-pull-of-daily-digests" date: "2025-06-16T00:00:00.000Z" authors: - Guillaume Jacquet jobTitle: "CEO & co-founder" readingTimeMinutes: 2 contentType: article intent: playbooks-methods pillar: "RevOps systems & architecture" audiences: - enterprise --- # The gravitational pull of daily digests _How RevOps create alignment and earn influence through a shared source of truth_ **No one’s really aligned. Everyone is just defending their own numbers. But what if RevOps could flip the script?** ## Influence starts with visibility RevOps is often seen as a support function: fixing dashboards, cleaning data, generating last-minute reports. Rarely as a strategic voice. At Vasco, we’re convinced that’s **a missed opportunity**. By owning and sharing a **single source of truth**, RevOps can fundamentally reshape how GTM teams operate. And one of the simplest ways to do that? **A daily digest**. ## Frequency matters: The power of daily pings In most companies, metrics are surfaced once a month: too late to change the story. Sending digests every day flips that dynamic. It creates a steady reminder that keeps everyone engaged, alert, and able to course-correct in real time. Frequency isn’t just a delivery choice; it’s what turns information into influence. The daily digest isn’t a bulky dashboard link or a 60-tab spreadsheet. It’s a short, regular, readable update sent proactively across teams, highlighting the key shifts, the red flags, the wins. It becomes a shared checkpoint, a heartbeat, **a rhythm that anchors the whole company in accountability**. ### How we do it at Vasco You can’t fix what you don’t see. That’s why our clients—and our own GTM teams—rely on** Vasco Digests: a daily pulse **showing what moved, what stalled, and where we stand against plan. Leads, pipeline shifts, risks… everything is visible across the org to track progress toward target and create shared accountability. ![A real example of the daily digest email our team receives](https://cdn.sanity.io/images/ys8gstp8/production/bfcbdd78b2e5e34ba7383a68f9cc086bfb48bbe8-1280x720.png?w=1600&fit=max&auto=format) > We send it by email, but your team might prefer a Slack channel or a push through your go-to workspace. Format doesn’t matter. **What counts is having a shared, recurring touchpoint that keeps everyone aligned.** ## A small habit, a big shift When a daily digest is shared across the entire org (including the executive team), it becomes a quiet but powerful alignment tool. Once people know the CEO sees the numbers every morning, **behaviours shift**. Suddenly, the metrics that matter are no longer buried. They’re out in the open, acted on, and reconciled across teams. People start adjusting their actions to match what’s visible. No one wants to be lagging. Performance improves. Not because of pressure, but because clarity removes excuses. ## The politics of clarity: Influence without authority RevOps doesn’t have formal authority over the CMO or the VP of Sales. But **they can still drive alignment**. A daily digest acts as a steady, persuasive forcing function without triggering conflict or confrontation. It’s not you challenging the numbers. It’s the data, showing up every morning, speaking for itself. And RevOps? They’re no longer the report machine. They’re the ones setting the pace. > Sending a daily digest is simple. But the impact is real: faster decisions, fewer finger-pointing meetings, and growing recognition of **RevOps as a strategic partner**. Want to increase your influence as a RevOps pro? Start hitting send! ## FAQ ### What is Vasco's daily digest feature? The Vasco daily digest is an automated email that shows how your revenue engine is progressing toward target each day. It surfaces actuals vs forecast across key lifecycle stages (leads, MQLs, SQLs, SALs, closed‑won, onboarded, expansion, and more) so everyone sees the same snapshot of performance at a glance. ### How does the daily digest help revenue teams stay aligned? The daily digest keeps revenue teams aligned by giving marketing, sales, customer success, and leadership a shared view of progress. Instead of guessing whether you’re on track, everyone starts the day with the same data, same definitions, and same priorities across the funnel. ### How is the digest segmented by lifecycle stage? The digest is segmented into core lifecycle stages such as leads, MQLs, SQLs, SALs, closed‑won, onboarded, and expansion. For each stage, it shows how much has been achieved vs the daily, weekly, or monthly target, making it easy to spot where the engine is accelerating or stalling. ### How is the digest segmented by team and quota? Each lifecycle stage is further broken down by team or function (e.g., marketing, SDRs, AEs, customer success) and their respective quotas. This shows which teams are ahead or behind target, helping managers prioritize coaching, reallocate resources, or adjust expectations in real time. ### How does the digest reduce noise and overload? The digest reduces noise by consolidating multiple data points (pipeline movement, stage‑level performance, and team‑level attainment) into one concise, structured email. Instead of scattered alerts and dashboards, teams get a single, prioritized view of what matters most that day. --- --- title: The hidden revenue killer description: "Something that keeps hitting me over and over in my work with growing companies: The biggest threat to your revenue isn't your competition or the market - it's the misalignment between your Product, Sales, and Finance teams." canonical: "https://vasco.app/blog/the-hidden-revenue-killer" date: "2025-06-16T00:00:00.000Z" authors: - Aaron Ross jobTitle: Author of Predictable Revenue readingTimeMinutes: 3 contentType: article intent: strategy-insights pillar: "GTM & RevOps execution" --- # The hidden revenue killer _Why your product, sales and finance teams need to get on the same page_ I've seen this movie too many times. Your **Product** team is heads-down building cool features. **Sales **are out there making promises to hit quota. And **Finance** is trying to make sense of it all while keeping the business profitable. Everyone's working hard, but somehow, customers keep slipping away. The problem? These teams are playing different games on different fields. And your customers can feel it. ## The Real Cost of Misalignment Let me paint you a picture I see often: **Sales** closes a deal by promising certain features that **Product** hasn't quite finished yet. The **customer **signs up, excited about solving their problems. But three months in, they're frustrated because what they're getting doesn't match what they were sold. Meanwhile, **Finance** is looking at the numbers and realizing the deal wasn't even profitable given the actual cost of delivering those features. ![Another churned customer, another hit to your revenue, and three teams pointing fingers at each other.](https://cdn.sanity.io/images/ys8gstp8/production/06d9d0f5858dcc6bbbbd7223112530b2a47cba90-1920x540.png?w=1600&fit=max&auto=format) > **Result?** Another churned customer, another hit to your revenue, and three teams pointing fingers at each other. ## Why Alignment Matters More Than Ever In today's subscription world, **customer churn is the silent killer of good businesses**. Every customer who leaves isn't just a lost account - they're a wound in your company's growth story. And here's the truth: most customers don't leave because of your product. They leave because **their experience doesn't match their expectations**. > That gap? It's almost always caused by teams working in silos. ## The Three Pillars of Low-Churn Growth After years of working with companies of all sizes, I've found there are three non-negotiable elements to building a low-churn business: 1. **Product **Has to Know the Real Customer Story 2. **Sales **Has to Sell Reality, Not Dreams 3. **Finance **Has to Be Part of the Conversation Early ![The Three Pillars of Low-Churn Growth](https://cdn.sanity.io/images/ys8gstp8/production/920ac172763a3ca0ed7af49751a007404919bf25-1920x540.png?w=1600&fit=max&auto=format) ## The Technology Gap Here's where things often break down: each team ends up building their own spreadsheets, using different tools, and working from different data. Sales has their CRM, Product has their analytics, and Finance has their forecasts. None of them talk to each other. This is exactly why platforms like Vasco matter. They create **a single source of truth** where all these teams can see: - How customers are actually using the product - What features drive the most value - Where billing and subscription issues pop up - Which customers might be at risk ## Making Alignment Real Want to know if your teams are truly aligned? Ask these questions: 1. Does **Sales** know exactly what features are coming and when? 2. Can **Product** see how pricing affects feature usage? 3. Does **Finance** have real-time visibility into how customers use what they're paying for? If you're answering "no" to any of these, you've got work to do. ## The Path Forward Here's what I recommend: ![](https://cdn.sanity.io/images/ys8gstp8/production/d35489272b3d862f62e1d8a1fabf165e9e9f0b45-1920x540.png?w=1600&fit=max&auto=format) 1. **Start with Regular Cross-Team Meetings** - Weekly syncs between Product, Sales, and Finance - Share what's working, what's not, and what's coming - Make decisions together about pricing and features 2. **Build a Single Source of Truth** - Get everyone working from the same data - Make customer behavior visible to all teams - Track metrics that matter to everyone 3. **Align Incentives** - Tie compensation to customer success, not just sales - Reward Product for features that retain customers - Give Finance visibility into the full customer lifecycle ## Technology as the Bridge Look, I'm not typically a "technology will solve everything" guy. But in this case, having the right platform makes a massive difference. > This is where **Vasco** comes in—it’s built specifically to help Product, Sales, and Finance teams stay in sync around customer data, billing, and compliance. With Vasco, teams can collaborate more effectively because they’re all seeing the same data, allowing: - **Product** to understand how features affect retention - **Sales **to confidently price and package solutions - **Finance** to track and forecast accurately ## The Bottom Line After years of working with companies on revenue growth, I've learned that **alignment **isn't just nice to have—it's essential for survival. The companies that win aren't always the ones with the best product or the biggest sales team. They're the ones where Product, Sales, and Finance work as one unit, focused on delivering real value to customers. > Remember: you can't build predictable revenue on a foundation of misaligned teams. Start fixing this today. Your customers—and your bottom line—will thank you. ## FAQ ### How does misalignment between Product, Sales, and Finance create churn? Misalignment creates churn when Sales sells features that Product hasn’t fully built, Product ships without understanding pricing or profitability, and Finance sees deals that don’t match reality. Customers feel misled, value drops, and churn follows. ### What causes low-churn growth? Low‑churn growth is caused by tight alignment between Product, Sales, and Finance, where Product understands the real customer story, Sales sells what the product can actually deliver, and Finance is involved early to ensure deals are profitable and sustainable. This alignment creates a consistent, reliable experience that matches customer expectations and reduces unnecessary churn. ### What is the biggest revenue killer in subscription businesses? The biggest revenue killer in subscription businesses is customer churn. Because revenue is recurring, every lost customer represents an ongoing loss that compounds over time, making it harder to scale even when acquisition is strong. ### How does technology help fix this hidden revenue killer? Technology helps by creating a single source of truth where Product, Sales, and Finance can see the same data: customer usage, billing, and subscription health. This visibility reduces guesswork, aligns incentives, and makes it easier to spot at‑risk customers early. ### What role does Vasco play in aligning Product, Sales, and Finance? Vasco aligns Product, Sales, and Finance by unifying customer data, billing, and subscription information in one platform. Product sees how features affect retention, Sales can confidently price and package solutions, and Finance tracks and forecasts accurately. ### What are practical steps to improve alignment? "Practical steps include: Regular cross‑team meetings (Product, Sales, Finance); A shared source of truth for customer data and metrics; Incentives tied to customer success and retention, not just sales." ### Why is a single source of truth critical for low‑churn growth? A single source of truth is critical because it ensures everyone is working from the same data and definitions. This reduces finger‑pointing, speeds decision‑making, and helps teams proactively address churn risks before they escalate. --- --- title: Bridging strategy and execution in a shifting GTM landscape description: "In a rapidly evolving GTM landscape, where AI reshapes expectations every quarter, RevOps has emerged as the glue between vision and execution." canonical: "https://vasco.app/blog/bridging-strategy-and-execution-in-a-shifting-gtm-landscape" date: "2025-05-20T00:00:00.000Z" authors: - Jordon Rowse jobTitle: CEO of Set2Close readingTimeMinutes: 3 contentType: video intent: strategy-insights pillar: "GTM & RevOps execution" audiences: - enterprise --- # Bridging strategy and execution in a shifting GTM landscape _Why RevOps is key to building clarity, accountability, and long-term growth_ Our CEO, [Guillaume Jacquet](https://www.linkedin.com/in/guillaume-jacquet-66b38522/), recently sat down with [Jordon Rowse](https://www.linkedin.com/in/jordonrowse/), CEO of [Set2Close](https://set2close.io/)—a certified HubSpot partner helping scaling companies build reliable GTM engines. With a front-row seat to shifting trends and recurring challenges, Jordon brings sharp insight into what’s changing, what’s not, and where RevOps can make the biggest difference. Here are some of the key takeaways both GTM leaders are seeing on the ground. **🎥 Watch the discussion** Prefer reading? Scroll down for a text summary. [https://www.youtube.com/embed/1S2NQokqNU8?iv_load_policy=3&rel=0&modestbranding=1&playsinline=1&autoplay=0&mute=1](https://www.youtube.com/embed/1S2NQokqNU8?iv_load_policy=3&rel=0&modestbranding=1&playsinline=1&autoplay=0&mute=1) ## Pressure is building inside the GTM room While the SaaS market is slowly rebounding, the shift from growth-at-all-costs to operational efficiency is here to stay. Sales cycles are longer, channels are more crowded, and buyers are more selective. GTM teams are expected to do more with less, all while justifying every investment with clear ROI. The most resilient organizations aren’t necessarily the most resourced. They’re the most adaptive. They continuously refine their attribution models, test new channels, and optimize both the front and back of the funnel. Dynamic thinking—and execution—is becoming a competitive advantage. > “What we see is that dynamic companies are winning. That’s a really big [thing]. And what does that mean? When things are going well, they’re still exploring new different channels and options to grow.” — Jordon Rowse ## The Bowtie model is more relevant than ever For many teams, performance conversations still revolve around pipeline and win rates. Yes, closing deals is critical. But long-term growth depends on net revenue retention. The modern GTM engine isn’t linear anymore: it’s a loop. The [bowtie model](https://vasco.app/blog/the-bowtie-model) helps teams operationalize this mindset by mapping both sides of the revenue engine: from demand creation to post-sale expansion. Filling the funnel is only part of the equation, what matters just as much is building systems that capture and compound value over time. ## Short-term pressure, long-term work: the CRO and RevOps paradox One of the core challenges in GTM teams is the tension between short-term performance and long-term system building. **CROs are evaluated based on quarterly results.** RevOps think in infrastructure. And with average CRO tenure under 18 months, the pressure to deliver now often overshadows foundational work. Bridging this disconnect requires shared language, mutual respect, and metrics that resonate across functions. Cross-training is one of the most effective ways to build that understanding. ## People > Processes > Systems At the heart of strong RevOps lies trust. Strong teams begin by **investing in people** before turning to tools or workflows. That means creating a culture of psychological safety, shared accountability, and cross-functional respect. Processes come next. Once the right behaviours and mindsets are in place, teams can define how they work together, test, iterate, and scale. Systems follow to support and automate those processes, not the other way around. Technology is an enabler, but human dynamics are what actually move the needle. Tools like Vasco help reinforce that by **making accountability visible**—not by pointing fingers, but by showing who needs support, when, and why. ## The five roles of RevOps As companies grow, so do the needs inside their RevOps function. What starts as a single operations hire often evolves into five distinct disciplines: ![The five disciplines of RevOps: sales operations, Marketing automation and attribution, Customer success and retention ops, Systems integration and architecture, and RevOps strategy and leadership.](https://cdn.sanity.io/images/ys8gstp8/production/7762baef954cbf82e1f122f226bfceefccd71ee7-1920x540.png?w=1600&fit=max&auto=format) Hiring for all five in-house is rarely feasible early on. That’s why a hybrid model (one internal operator supported by fractional experts) makes sense for most scale-ups. The key is clarity on what to outsource vs. build internally. ## GTM and RevOps engineers: an emerging tandem As the velocity of go-to-market execution increases, a new collaboration is taking shape. **GTM engineers test and launch fast**, often using AI-driven tools like Clay to activate account-based strategies in days, not weeks. They focus on experimentation, iteration, and speed. **RevOps engineers step in to stabilize what works**. They validate results, systematize proven motions, and embed repeatability into the CRM and broader GTM infrastructure. Together, they form a feedback loop that turns quick wins into scalable systems. ## AI is powerful but still incomplete Artificial intelligence has accelerated many aspects of RevOps, from data enrichment to outbound personalization. But **it still lacks nuance**. Contextual judgment, strategic prioritization, and cross-functional interpretation remain deeply human skills. AI can multiply data points. But insight still depends on interpretation. ## Set2Close + Vasco: From insight to execution The **partnership between Set2Close and Vasco** helps close the gap between overwhelming data and meaningful action. While AI can surface more signals, teams still need structure, context, and clarity to** turn those signals into decisions**. By pairing proven frameworks with clean CRM data and built-in orchestration, this collaboration reduces guesswork and accelerates decision-making, so strategy translates into action, faster and with more confidence. > “Clarity plus control equals certainty of outcome.” — Jordon Rowse --- --- title: "What does event success look like in the modern world?" description: "It’s event season in tech. In just a few weeks, the Vasco team went from New Orleans to San Francisco, attending RevOps AF, the Impact Summit by Winning by Design, and SaaStr Annual 2025. And with every flight, every booth, and every hallway conversation, we couldn't help but wonder: What’s the real value of being here?" canonical: "https://vasco.app/blog/event-success-and-roi" date: "2025-05-20T00:00:00.000Z" authors: - Sophie Geoffrion jobTitle: "Brand & Content Lead" readingTimeMinutes: 2 contentType: article intent: strategy-insights pillar: "GTM & RevOps execution" --- # What does event success look like in the modern world? _Insights from SaaStr Annual 2025_ **In a post-pandemic world of AI-generated content, hyper-automated outbound, and Slack threads you can’t keep up with, what role do in-person events still play? How do GTM leaders in SaaS evaluate success when sponsoring or attending a trade show? What does ROI look like beyond the surface metrics?** ## Measuring event ROI: It’s not just pipeline While pipeline generation remains a strong driver (think booked demos, qualified meetings, lead nurture), it’s far from the only reason people show up. In our latest video (see below), we asked SaaStr attendees: _“**How do you measure ROI from this event?**”_ and the answers covered more ground than expected: - **Pipeline activity** – Booked meetings, new leads, or warming up existing ones - **Learning** – Getting a pulse on the market, especially around AI and emerging tools - **Relationships** – Building meaningful connections, reconnecting with customers and peers - **Brand presence** – Increasing awareness and recognition, especially when exhibiting - **Content creation** – For many teams (ours included), events are a goldmine for organic content **🎥 Watch the video to hear how SaaStr 2025 attendees define ROI (in their own words):** [https://www.youtube.com/embed/ZlAfO_QGiWU?iv_load_policy=3&rel=0&modestbranding=1&playsinline=1&autoplay=0&mute=1](https://www.youtube.com/embed/ZlAfO_QGiWU?iv_load_policy=3&rel=0&modestbranding=1&playsinline=1&autoplay=0&mute=1) ### Beyond metrics: Why IRL matters more than ever We’ve all grown numb to automated emails and templated outbound.** They’ve become the wallpaper of SaaS.** In contrast, live events break through. > In real life, we become remarkable again. Offline is the new differentiation. There’s something uniquely energizing about being in a room full of people **building the same future**. Conversations are more honest. Gut feelings sharper. The noise of AI chatter actually fades a bit when you can look someone in the eye and ask what tools they’re actually using and why. Seeing the people behind the logos makes brands more human, more credible, and ultimately, **more memorable**. ## How to make the most of your next event You can go in with badge scan targets and sure, that matters. But don’t stop there. Here’s how to maximize ROI: - **Be curious.** Take advantage of official meetups and networking tools to connect intentionally. - **Do your homework.** Research attendees ahead of time and reach out proactively. - **Participate.** Organize a dinner, share your takeaways on LinkedIn or through your newsletter, interview speakers, start a conversation in the coffee line. > Events create opportunities not only for leads, but for attention, positioning, and trust. If you show up fully, they pay off in more ways than one. **How do _you_ measure success at events like SaaStr? We’d love to hear it!** --- --- title: How growth loops turn users into your best sellers description: "Vasco just came back from the Impact Summit in San Francisco, hosted by Winning by Design. As a certified WbD partner, we had front-row seats to an insight-packed event on how AI is reshaping go-to-market strategies. We asked Dave Boyce, EVP Product at Winning by Design, to share a small slice of thinking that’s bound to impact RevOps teams in the months ahead. Here it is!" canonical: "https://vasco.app/blog/how-growth-loops-turn-users-into-your-best-sellers" date: "2025-05-20T00:00:00.000Z" authors: - Dave Boyce jobTitle: "Executive Chairperson and EVP Product @ Winning by Design" readingTimeMinutes: 2 contentType: article intent: strategy-insights pillar: "AI in GTM & RevOps" --- # How growth loops turn users into your best sellers _AI meets advocacy to reinvent lead generation_ ## The real cost of outbound lead gen today There is much hype today about AI being a potential outbound lead generation killer. Revenue growth rates are down to 16% (from 36% in 2021), and the cost to acquire one dollar of growth is approaching two dollars. So yes, things aren’t looking too rosy when it comes to lead generation. But perhaps AI isn’t the culprit, and instead the savior? ![](https://cdn.sanity.io/images/ys8gstp8/production/8747c5c8db91a265bb58edd9014e443d80dc1309-1920x1080.png?w=1600&fit=max&auto=format) ## An overlooked opportunity: growth loops In the short history of AI to date, there is already an abundance of lead throughput improvement tools powered by AI, and that’s good. But there is also a potentially overlooked opportunity here, whereby AI can actually help with lead input as well. **Enter stage right ([Bowtie](https://vasco.app/blog/the-bowtie-model) right)** the concept of growth loops. ## What if your users became your best sellers? What if we could leverage not just our customers, but **our end users to be advocates** of our service? Through targeted incentive, end users are arguably the best voice to pitch the value of our products and often carry more credibility than even the best sellers in our organization. Incentives can take many forms, such as: ![Incentives can take many forms — from exclusive access to beta releases, to public recognition through testimonials, invitations to community events, or paid referrals equivalent to qualified meetings.](https://cdn.sanity.io/images/ys8gstp8/production/bc3cc0ef39ef71e8f64b2f14a29b62cd09b31f84-1920x540.png?w=1600&fit=max&auto=format) ## Where AI steps in: matching users with the right opportunities Traditionally, customer value has been measured using customer lifetime value. But perhaps we should also be measuring their value in terms of **advocacy impact ($)**. This is where AI comes into play. **Agentic AI (AI capable of making autonomous decisions to achieve goals)** can objectively measure the rational and emotional engagement of an end user, and from this, align them with potential new opportunities—in effect automatically matching users to the perfect advocacy opportunity. ## The compounding effect of growth loops The real magic here is **the mathematics of compounding**. Growth loop activity stems from the highly concentrated right-hand side of the customer journey (retention and expansion), then loops back into new-customer acquisition. For every existing customer, we may have 10 or more users. That multiplier alone has a huge impact on the number of opportunities fed back into the lead generation machine. > In fact, for an $80M ARR SaaS company with an ASP (Average Selling Price) of $50K, converting just 1 in 50 users to an advocate results in a 10% revenue growth impact in year one. Add to that the compounding effect of time, and by year five you could see growth acceleration of over 50%. Yes, 50%. ## A dual-engine model for scalable lead generation The net of this is a dual-piston lead generation machine, lessening the risk and burden on marketing, while driving measurable uplift in sales and revenue. The advocacy from our end-users is 100% human-led, but knowing who will be the most effective and how to incentivize them is all down to AI. ![](https://cdn.sanity.io/images/ys8gstp8/production/675c5545494bbb694a8924644411fc0e276bdd77-1920x1080.png?w=1600&fit=max&auto=format) > The marriage of Artificial Intelligence and Human Intelligence may just mean that in this new world of “more efficient growth,” there is still untapped potential to drive more input as well as better throughput. ## FAQ ### What is a growth loop in a RevOps context? A growth loop in RevOps is a self‑reinforcing cycle where actions on the right‑hand side of the customer journey (retention, expansion, and advocacy) feed back into new‑customer acquisition. Instead of relying only on outbound, you turn existing users into a continuous source of leads and revenue. ### How do growth loops turn users into your best sellers? Growth loops turn users into your best sellers by making advocacy a built‑in part of the experience. When users see clear value and are matched to the right opportunities, they naturally refer, upsell, or expand, acting as highly credible, human‑led sales channels. ### Why are growth loops important for SaaS companies? Growth loops are important because they reduce dependence on expensive outbound lead generation and create compounding, self‑sustaining growth. For an $80M ARR SaaS company, converting just 1 in 50 users to an advocate can drive a 10% revenue uplift in year one, with even stronger acceleration over time. ### How does a revenue architecture platform like Vasco support growth loops? Vasco unifies data across acquisition, retention, and expansion so you can see where growth loops are working and where they’re leaking. It surfaces referral-driven pipeline, tracks Bowtie‑aligned stages, and powers AI‑driven insights. --- --- title: "How to scale your B2B SaaS from $1M to $10M ARR" description: "Reaching $1M ARR is a strong signal: you’ve found early product-market fit, and your hustle as a founder has paid off. But crossing that milestone also marks a shift — from intuition to structure." canonical: "https://vasco.app/blog/from-founder-led-sales-to-a-scalable-revenue-engine" date: "2025-04-14T00:00:00.000Z" authors: - Guillaume Jacquet jobTitle: "CEO & co-founder" readingTimeMinutes: 3 contentType: video intent: strategy-insights pillar: "RevOps maturity & scaling journeys" audiences: - start-ups - founders --- # How to scale your B2B SaaS from $1M to $10M ARR _From founder-led sales to a scalable revenue engine_ **🎥 Watch the interview** Prefer reading? Scroll down for the text version. [https://www.youtube.com/embed/fJhqc9oRFsg?iv_load_policy=3&rel=0&modestbranding=1&playsinline=1&autoplay=0&mute=1](https://www.youtube.com/embed/fJhqc9oRFsg?iv_load_policy=3&rel=0&modestbranding=1&playsinline=1&autoplay=0&mute=1) **Around $1M in ARR, many founders try to scale by doing more of the same: hiring a few sales reps, handing over the pitch, and hoping revenue follows. But that rarely works.** > “You realize the magic you brought to your first deals can’t be replicated.” Scaling from $1M to $10M isn’t about doing more. It’s about turning your intuition into systems, and your personal edge into repeatable motion. 20% of the journey is still about timing, vision, and luck — the kind of founder magic that can’t be taught. But the other 80%? That part follows a **clear, proven path**. The challenge is knowing when — and how — to step out of the day-to-day and start building a machine that can scale without you. ## Start with customer success, not sales It’s tempting to focus immediately on top-of-funnel growth. After all, new revenue feels like momentum. But without strong retention, you're just pouring leads into a leaky bucket. > “You fought hard to win those early customers — make sure they stay. Your first hire should be someone who wakes up every day thinking about retention.” Before hiring more AEs or investing in demand gen, build a solid onboarding and success motion. Not only does this reduce churn, it also gives you **referenceable logos, case studies, and word of mouth** — all essential growth levers at this stage. ## Specialize or stall One of the most common mistakes founders make? Expecting early sales hires to be a clone of themselves. Someone who can generate pipeline, close deals, onboard customers and handle renewals. It doesn’t work. > “You need mini versions of yourself — specialized by stage of the customer journey.” As long as one person owns the entire funnel, it’s impossible to scale or measure anything reliably. The key is **segmentation**: inbound SDRs focus on MQLs, outbound reps target cold leads, AEs close, CS handles onboarding and retention. One role, one metric, one mission. ## Build predictable pipeline before hiring closers It’s tempting to hire closers right away. But without a steady stream of leads, even the best AEs will struggle. It creates pressure, frustration — and underperformance. The real unlock is upstream: build **repeatable lead generation** first. > “If you can generate pipeline predictably, everything else becomes easier. Even if you're not the best at closing yet.” Once you know your ICP and what channels work, you can start generating pipeline consistently. That’s when your first AE can thrive — because they’re set up to succeed, not left chasing scraps. ## Turn instincts into process Getting to $1M often means selling to anyone who’ll buy. But now, it’s time to look back, analyze your wins, and focus. Who churned? Who stayed? Who saw value fast? > “Find the customers who got to value quickly and never looked back. That’s your blueprint.” Use that data to **define your ICP, codify your messaging, and set pipeline benchmarks**. When you know what good looks like — in terms of close rates, velocity, and retention — you can scale with confidence and give your team real targets. ## Prepare for the next ceiling Eventually, growth slows. You’ve saturated your early market, and the old playbook starts delivering diminishing returns. That’s normal. To break through the $10M ceiling, two levers matter: - **Expand your ICP** (new segments, industries, geographies) - **Reach buyers earlier** (invest in brand, education, thought leadership) > “From $10M to $50M, you’re not just selling to active buyers anymore. You’re shaping demand.” This phase requires a new level of go-to-market maturity. But it only works if your initial engine — from onboarding to pipeline to close — is already running smoothly. ## The founder’s role: from doer to orchestrator Scaling is about gradually removing yourself from each part of the journey — first CS, then pipeline generation, then closing. It’s not about letting go completely, but about building systems others can run better than you. > “It feels like surfing. Once the engine works, your job is to ride the wave — not to paddle all the time.” Founders who scale don’t do more. They build machines that do more, predictably and repeatably. That’s how you go from $1M to $10M — and keep going. ## Final thought – Scaling is a system, not a sprint Scaling from $1M to $10M means stepping out of the weeds and **turning your early instincts into a system** others can run. The path is clear: ![Start by securing retention, build predictable pipeline, separate roles across the funnel, codify what good looks like, and expand once the engine runs smoothly.](https://cdn.sanity.io/images/ys8gstp8/production/d81761e9ca456a5479e0179e30e9b3e6ff76ed7c-1920x386.png?w=1600&fit=max&auto=format) > “Scaling starts when you remove yourself — first from CS, then from pipeline, then from closing.” It’s not about doing less. It’s about making sure each part of the engine runs better without you. That’s how you move from chasing deals… to compounding growth. --- --- title: How to Hire Exceptional Leaders description: "Hiring great leaders is both an art and a science. The right leader can elevate an organization, inspire teams, and drive success. But the wrong hire? It can be a costly mistake, setting your organization back significantly." canonical: "https://vasco.app/blog/how-to-hire-exceptional-leaders" date: "2025-04-14T00:00:00.000Z" authors: - JD Saint-Martin jobTitle: Ex-Lightspeed President readingTimeMinutes: 3 contentType: article intent: strategy-insights pillar: "RevOps maturity & scaling journeys" --- # How to Hire Exceptional Leaders _A Comprehensive Guide_ Here’s a streamlined guide to finding and hiring exceptional leaders who will make a lasting impact. Benchmarking against the following principles when building your leadership team will increase your odds of success: ## 1. Assess Loyalty, Longevity, and Cultural Fit A candidate’s work history tells a story. Look for signs of **loyalty and longevity** over excessive job-hopping. Leaders with a track record of staying power have likely navigated challenges and seen initiatives through to completion. **Cultural fit** is equally critical. Ask yourself: would you enjoy sitting next to this person on a 10-hour flight? If you’d dread the experience, they might not be the right fit. Leaders spend significant time with their teams, and their interpersonal dynamics can make or break relationships. ## 2. Seek Role-Relevant Experience and Data-Driven Mindset > Great leaders have walked a mile in the shoes of the role you’re hiring for. They’ve faced the challenges, navigated the complexities, and know what success looks like. This lived experience enables them to lead with empathy, clarity, and confidence. Additionally, look for leaders who understand the value of a **data-driven approach**. Great leaders know you cannot manage what you cannot measure. They bring a clear understanding of what great looks like and identify the right key performance indicators (KPIs) to track progress, ensuring measurable success. ## 3. Find Leaders Who Raise the Bar Exceptional leaders elevate everyone around them. They inspire teams to **aim higher, think bigger, and achieve more**. Assess whether the candidate has a history of bringing top talent with them to the table—former colleagues or proteges are strong indicators of leadership success. Do they already have great people in their network that will join the journey? Look for signs that they will challenge the status quo, see problems as opportunities, and push for innovation and growth. ## 4. Look for Adaptable Problem-Solvers with a Customer-First Approach > Avoid leaders who come in “guns blazing” with a pre-baked playbook. Every organization is unique, and leaders must adapt to the specific context and challenges they’ll face. Great leaders ask thoughtful, probing questions and approach challenges with **curiosity and creativity**. Sales leaders, in particular, must invest in understanding your product and customer base. Audit whether they’re demonstrating a **commitment** to learning the product, analyzing the customer experience, and understanding the pain points your organization solves. Leaders who immerse themselves in the customer journey are better equipped to drive meaningful results. ## 5. Balance Internal Promotions and External Hires Striking the right balance between promoting from within and hiring externally is critical. A good rule of thumb: aim for 60% internal promotions and 40% external hires. > The devil you know is often better than the devil you don’t know. Internal candidates bring institutional knowledge and cultural alignment. An organization that fosters internal development and that recognizes and grows talent from within will increase its longevity. ![Striking the right balance between promoting from within and hiring externally is critical. A good rule of thumb: aim for 60% internal promotions and 40% external hires.](https://cdn.sanity.io/images/ys8gstp8/production/301dcc9571cd20444e8fd245f282c081810ac3e6-1920x720.png?w=1600&fit=max&auto=format) On the other side, external hires offer fresh perspectives and challenge entrenched norms. Be aware, however, that external hires carry risks—it is not unusual to see 50%+ that don't work out. Invest significant time and energy in vetting external candidates to minimize this risk and ensure you’re hiring the right person. Incorporating case studies into the interview process will reveal how candidates think, problem-solve, and approach real-world challenges. Seasoned executives are exceptional at selling themselves. Find ways to catch them off guard to see the true nature of what they bring to the table. ## 6. Avoid Leaders With Massive Corporate Mindsets Leaders from massive organizations like Google or Microsoft may struggle in smaller, resource-constrained environments. > These candidates often rely on vast support systems and may lack the hands-on experience required to "roll up their sleeves" and execute in leaner organizations. Seek leaders who are comfortable working in "less is more" environments and thrive on resourcefulness. ## 7. Guard Against the Cost of a Bad Hire A bad hire can be detrimental, draining resources, morale, and momentum. Prioritize getting the right person over filling the role quickly. > The wrong leader can set your organization back significantly, so take the time to evaluate candidates thoroughly. By aligning your hiring process with these principles, you’ll position your organization to secure leaders who inspire, innovate, and deliver measurable results. **Remember: great leaders don’t just fit into your organization—they make it better.** ## FAQ ### What's the right balance between internal promotions and external hires? A good rule of thumb is about 60% internal promotions and 40% external hires. Internal candidates bring institutional knowledge and cultural alignment; external hires bring fresh perspectives but carry higher risk, so invest heavily in vetting them. ### How can I reduce the risk of a bad leadership hire? Reduce risk by taking time to evaluate candidates thoroughly, using case studies and scenario‑based interviews, and involving key stakeholders. Prioritize getting the right person over filling the role quickly; a bad hire can drain resources, morale, and momentum. ### How does a revenue architecture platform like Vasco support great leaders? A revenue architecture platform like Vasco gives leaders a unified view of data, stages, and metrics across the customer lifecycle. It surfaces bottlenecks, supports Bowtie‑aligned reporting, and empowers leaders to drive alignment and measurable impact at scale. --- --- title: The board meeting playbook for scaling SaaS companies description: "Scaling a SaaS startup is already a balancing act. Add investor expectations, board meetings, and growth metrics into the mix, and it’s easy to feel overwhelmed." canonical: "https://vasco.app/blog/the-board-meeting-playbook-for-scaling-saas-companies" date: "2025-04-14T00:00:00.000Z" authors: - Guillaume Jacquet jobTitle: "CEO & co-founder" readingTimeMinutes: 9 contentType: guide intent: strategy-insights pillar: "Revenue planning, forecasting & economics" audiences: - start-ups - enterprise - founders - scale-ups - cros --- # The board meeting playbook for scaling SaaS companies _Free resource_ ## Get the guide **The Board Meeting Playbook** is a tactical guide to help founders and operators make every board meeting a growth accelerator, not just a reporting session. ![](https://cdn.sanity.io/images/ys8gstp8/production/b9c286f88947e41291250e806495b71466d9aeb4-1920x1080.png?w=1600&fit=max&auto=format) > **What you will learn** > - The key VC expectations and metrics they look for at each stage - How to structure a board deck that supports strategic discussion - The operator mindset that turns board meetings into growth levers Whether you’re a founder, GTM leader, or finance operator, you’ll find frameworks, templates, and tips to: - Focus on the metrics that you and your VCs should care about - Structure your board report to spark the right discussions - Show up with the mindset of a leader who’s building something big --- --- title: When to Stop Being Your Own Best Salesperson description: "Here's a counterintuitive truth about startup growth: your biggest strength might actually be your biggest problem." canonical: "https://vasco.app/blog/when-to-stop-being-your-own-best-salesperson" date: "2025-04-14T00:00:00.000Z" authors: - Aaron Ross jobTitle: Author of Predictable Revenue readingTimeMinutes: 4 contentType: article intent: strategy-insights pillar: "RevOps maturity & scaling journeys" --- # When to Stop Being Your Own Best Salesperson _Letting go to level up_ Being an amazing salesperson as a founder has been crucial to getting where you are - you've built momentum, closed key deals, earned customer trust. But in today's market, that personal sales magic can become the very thing blocking your company's next phase. ## The Hidden Cost of Being Amazing at Sales It’s true. It sounds counterintuitive, but being too good at sales as a founder can actually impede your company’s **scalable growth**. Early on, your ability to personally close deals is a superpower. You know every product detail, make on-the-spot decisions, and engage customers with authentic passion. But as your business grows, this personal touch can transform into a bottleneck. You’ll know you’ve hit this wall when: ![You’re juggling too many follow-ups, leaving some unresolved. Strategic decisions are delayed because you’re bogged down in back-to-back sales calls. Your team needs constant input on every deal. Despite working harder, growth starts to plateau.](https://cdn.sanity.io/images/ys8gstp8/production/098dfe6f3f57bb50c6c8be0855bdbcaacf00a9bc-1920x540.png?w=1600&fit=max&auto=format) These aren’t just minor inconveniences—they’re clear signs that your founder-led approach is no longer sustainable for **scalable growth**. ## Why Most Founders Struggle with This Transition Let's be honest—this isn't just about hiring some salespeople and calling it a day. > The real challenge is psychological. I've seen founders almost physically recoil at the idea of letting someone else handle their carefully nurtured customer relationships. And you know what? That's completely normal. But here's what I've learned, especially while growing my own businesses while raising a family of ten: you can't do everything yourself forever. At some point, you need to build systems that can run without you being involved in every detail. ## Building Your Sales Engine: What Actually Works Let's get practical about this transition. First, forget about writing some massive sales playbook that no one will read. Instead, [start recording your sales calls](https://buddycrm.com/sales/the-pros-and-cons-of-implementing-sales-call-recordings/) (with permission, of course). Have your first hire shadow you during these calls. Pay attention to the questions that come up most often, and document the stories that consistently resonate with customers. This organic approach captures the real magic of your sales process, not just the theory. > When it comes to hiring, the perfect candidate often isn't who you'd expect. Rather than focusing purely on sales experience, look for people with learning agility and emotional intelligence. You need someone who can roll with the constant changes of a growing company, who brings problem-solving creativity to the table, and who shows genuine curiosity about your space. Experience is great, but adaptability is essential. And when you're ready to [hire your first leadership roles](/blog/how-to-hire-exceptional-leaders)? Make sure they're leaders who truly raise the bar. ## Creating Systems That Scale This is where platforms like [Vasco](/) become crucial to your growth. You need infrastructure that takes care of the mechanical aspects of sales operations automatically. Think about subscription management, billing cycles, and compliance requirements. These aren't just administrative tasks—they're the foundation that allows your sales team to focus on what matters: building relationships and closing deals. The transition itself needs to be gradual and intentional. Start by having your new team handle incoming leads while you maintain relationships with existing accounts. Over time, you can begin transitioning some of your established relationships, keeping only the most strategic ones under your direct care. > Remember, the key is to move at a pace that allows your team to build confidence while maintaining the quality of customer relationships. ## The Power of Modern Technology The right RevOps technology stack, like Vasco, can make this transition smoother and more efficient. These platforms don’t just handle basic operations—they provide a foundation for **scalable growth**. By managing complexities like [subscription lifecycles](https://www.sfgnetwork.com/blog/customer-care/what-is-subscription-management-and-why-is-it-important/), customer health, and compliance in the background, your sales team can focus on what they do best: selling. Modern tech platforms automate repetitive tasks, like managing recurring billing, tracking customer interactions, and ensuring compliance, freeing up valuable time and resources. This gives your team more space to build relationships and close deals, instead of getting bogged down by administrative tasks. With the right tech in place, the friction that often comes with scaling starts to disappear, enabling you to grow faster while maintaining high-quality customer interactions. It’s about creating an environment where your team can thrive, and your business can scale seamlessly. ## Your Evolution as a Founder As your business grows, your role as a founder has to evolve. It may feel like you're losing control when you step back from hands-on selling, but trust me, it's actually a strategic move that unlocks scalable growth. 🎥 [**Watch interview "From founder-led sales to a scalable revenue engine"**](https://vasco.app/blog/from-founder-led-sales-to-a-scalable-revenue-engine) Instead of getting bogged down by every single deal, you can focus on what really drives long-term success: leading your team, innovating, and building systems that keep the growth machine running—without relying on your personal involvement for every step. ![Stepping back doesn’t diminish your role; it transforms it. Shift your focus. Become a leader, not just the "hero". Step into bigger opportunities. Steer the ship, don’t row it.](https://cdn.sanity.io/images/ys8gstp8/production/38d31fc25675cc0b674b762c3329f89ae0337984-1920x720.png?w=1600&fit=max&auto=format) ## The Path Forward I've learned this lesson many times over: trying to do everything yourself isn't just unsustainable—it's a recipe for burnout. Just like in my journey of balancing business growth with a large family, you need to build systems that can run without your constant attention. > The goal isn't to remove yourself completely from sales. It's to build an organization that can generate predictable revenue without being dependent on your personal heroics. With the right people, processes, and platforms like Vasco handling the operational heavy lifting, you can focus on growing your business in ways that weren't possible when you were doing all the selling yourself. I get it—this transition is hard. You've poured your heart and soul into every deal. But trust me on this: the founders who make this transition successfully are the ones who build systems and processes that can scale, while keeping that founder magic that made their early sales so successful. **Remember: You're not giving up control; you're gaining leverage. And in today's fast-moving market, that's exactly what you need to build a truly scalable business.** ## FAQ ### When should a founder stop being the main closer? You should stop being your own best salesperson when your personal involvement is the bottleneck to scaling. If every big deal must run through you, your pipeline, coaching, and strategy time all suffer—and growth stalls no matter how good you are at closing. ### Why is being your own best closer a problem? Being your own best closer creates a fragile, founder‑dependent sales engine. It limits pipeline capacity, slows onboarding of new reps, and prevents you from focusing on strategy, systems, and long‑term growth levers that matter most at scale. ### What does "predictable revenue" mean for founders? Predictable revenue means deals close consistently through repeatable systems and processes, not because the founder personally charms every prospect. It’s revenue that can be forecasted, scaled, and replicated across a team, not tied to one person’s calendar. ### What are the risks of staying the main closer too long as a founder? Staying the main closer too long risks burnout, inconsistent ramp‑up for new reps, and fragile growth that collapses if you’re unavailable. It also slows product‑market fit iteration because you’re buried in tactical deals instead of strategic decisions. --- --- title: Inbound vs. Outbound Leads description: "Struggling with low MQL actuals? Your lead qualification process might be the culprit. Discover how inbound and outbound leads follow different paths—and how using the SPICED framework can help your RevOps team refine lifecycle stages for a more accurate pipeline. Learn the manual and automated strategies to bridge the gap and fix MQL tracking in your CRM." canonical: "https://vasco.app/blog/inbound-vs-outbound-leads" date: "2025-03-15T00:00:00.000Z" authors: - Sophie Geoffrion - Justin Hudon readingTimeMinutes: 3 contentType: article intent: playbooks-methods pillar: "GTM & RevOps execution" --- # Inbound vs. Outbound Leads _Why Your MQL Count Looks Off—And How to Fix It_ ## Your MQL Count Looks Off—And the CRM Might Be to Blame **Growthly** is a fast-growing SaaS startup that just closed its Series A. With pressure to scale efficiently, their GTM leaders are laser-focused on pipeline health. But there’s a problem. Their MQL count looks **way too low**. The VP of Sales is worried: “If we don’t have enough MQLs, how do we hit our targets?” The Lead Gen Manager is confused: “I have plenty of leads with booked calls, but they’re not showing up as MQLs.” Enter the **RevOps Engineer**. Unlike sales and marketing, they have [**full visibility into how data flows across the bowtie**](/blog/the-bowtie-model). After digging into the CRM, they uncover the issue: Growthly’s definition of an MQL is **too rigid**. Leads are only counted as MQLs **after** their first call—ignoring those who already showed buying intent. The result? An artificially weak pipeline. ## How RevOps Solves the MQL Puzzle Using SPICED ### Let’s Pause for a Minute: What is SPICED? The **SPICED framework** provides a structured approach to lead qualification, ensuring that leads are assessed based on: - **Situation** → What’s their context? (Company size, industry, tech stack) - **Pain** → What’s frustrating them? (Challenges that brought them here) - **Impact** → Why does it matter? (Revenue, cost, or experience impact) - **Critical Event** → Why now? (Trigger forcing action) - **Decision** → How will they choose? (Buying process, decision-makers) For Growthly, **MQLs should meet at least the first two criteria: Situation and Pain.** ![The SPICED framework provides a structured approach to lead qualification, ensuring that leads are assessed based on their situation, their pain, the impact, the critical event and the decision.](https://cdn.sanity.io/images/ys8gstp8/production/83d65c30eb7d1960795ee6e467bd03a87fe5a77e-958x768.png?w=1600&fit=max&auto=format) SPICED helps Growthly refine its lifecycle stages, ensuring that inbound and outbound leads are qualified consistently. By structuring lead data around Situation and Pain, SPICED acts as a framework for better pipeline visibility—essentially serving as one of Growthly’s key sales and marketing alignment tools. P.S. We have a tactical guide on how to implement SPICED directly in your HubSpot. Read [The Smart Guide to Acquisition Lifecycle Stages & CRM Setup here](/blog/lifecycle-stages-and-crm-setup), written by Vasco's very own Head of Sales & CS, Justin Hudon. ### Inbound vs. outbound: same same, but different The key difference between **inbound** and **outbound** leads? **Which SPICED criteria are known at acquisition:** - **Inbound leads** engage with Growthly (e.g., download content, attend webinars). This suggests they **have a Pain (P)**—but their **Situation (S) is unknown**. - **Outbound leads** are proactively sourced based on ICP criteria (industry, size, CRM). Their **Situation (S) is known**, but their **Pain (P) is unclear**. To transition to **MQL**, Growthly must **fill in the missing piece** for each type of lead. ## Bridging the Gap: Manual vs. Automated Lead Qualification **For Small Teams: A Hands-On Approach** When the pipeline is manageable, the GTM team actively fills the gaps: - **Inbound leads** → Research firmographics (LinkedIn, Clearbit, lead forms). - **Outbound leads** → Use personalized outreach or discovery calls to validate pain. - **Once S & P are identified** → Convert to MQL, enroll in nurture, notify sales. **For Scaling Teams: Automating Qualification** As lead volume grows, manual work won’t scale. Larger teams can: - **Enrich inbound leads automatically** (Clearbit, Apollo, 6sense, progressive profiling). - **Detect outbound pain signals** via intent data, engagement scoring, AI-driven outreach. - **Automate lifecycle transitions** in CRM, triggering nurture workflows at MQL. Whether manual or automated, **the goal remains the same**: ensure every MQL meets **both S & P criteria** before moving forward. > **Want more granularity?** In your CRM, leverage **Lead Status** to track how leads interact with your activities—whether inbound or outbound. This helps gauge their engagement level and proximity to becoming an MQL, ensuring a more data-driven qualification process. If you're scaling your automated outreach, try a dedicated partner like [Growth Today](http://www.growthtoday.co/). Their proven tactics and email automations will help you grow your outreach as quickly as your sales team can keep up. ## Wrapping It Up: A Healthier Pipeline The **RevOps Engineer updates the CRM**, and sends a quick Slack update follows in **#gtm-team**: _"Hey team—MQL tracking is now fixed! Inbound leads move forward once we confirm their Situation, outbound leads once we validate their Pain. No more waiting for a call. Your pipeline just got a whole lot more accurate. _🚀_"_ A collective sigh of relief. The VP of Sales sees a healthier pipeline, the Lead Gen Manager gets credit for their work, and the GTM team breathes easier. But in RevOps, optimization is never truly "done"—as Growthly evolves, so will its lifecycle definitions. A well-structured bowtie isn’t static; it adapts to the company’s reality, ensuring that lead qualification always reflects the business’s growth and strategy. ## FAQ ### What is the difference between inbound and outbound leads? Inbound leads come to you through content, webinars, or organic channels, signaling interest or pain. Outbound leads are proactively sourced based on firmographic criteria like industry or company size. ### What is the SPICED framework for lead qualification? SPICED stands for Situation, Pain, Impact, Critical Event, Decision. It helps qualify leads by asking: what’s their context, what problem do they have, why it matters, what triggered them now, and how they’ll decide. For MQLs, teams often require at least Situation and Pain to be known. ### How should inbound leads be qualified? Inbound leads typically show Pain through engagement (downloads, webinars, content views). To qualify them as MQLs, you need to fill in the missing Situation (company size, industry, tech stack) via lead forms, enrichment tools, or light research before moving them to the next stage. ### How should outbound leads be qualified? Outbound leads start with known Situation (ICP fit) but unclear Pain. Qualification means validating their problem through personalized outreach, discovery calls, or intent data. Once both Situation and Pain are confirmed, the lead can transition to MQL and enter nurture or handoff workflows. ### Should inbound and outbound leads follow the same lifecycle? Yes, inbound and outbound leads should follow the same lifecycle stages and definitions. Using a shared SPICED‑based model ensures consistent qualification, so MQLs reflect real buying intent regardless of channel and avoids double‑counting or ghost pipeline. ### Why is lead status important for inbound vs. outbound? Lead status helps track how inbound and outbound leads interact with your activities, showing engagement level and proximity to becoming MQLs. A clear status model makes it easier to spot bottlenecks, adjust messaging, and ensure both channels contribute to an accurate, healthy pipeline. ### How does this apply to a revenue architecture platform like Vasco? A revenue architecture platform like Vasco enforces consistent lifecycle stages, and supports SPICED‑based qualification. It surfaces when Situation or Pain is missing, automates transitions, and gives RevOps a single view of pipeline health across channels. --- --- title: "The Smart Guide to Acquisition Lifecycle Stages & CRM Setup" description: "A strong RevOps strategy starts with a well-structured lifecycle stage framework in your CRM. But between lead qualification, marketing-to-sales alignment, and automation workflows, setting up the right system can quickly become overwhelming." canonical: "https://vasco.app/blog/lifecycle-stages-and-crm-setup" date: "2025-03-03T00:00:00.000Z" authors: - Justin Hudon jobTitle: Head of Sales and Customer Success readingTimeMinutes: 9 contentType: guide intent: playbooks-methods pillar: "RevOps systems & architecture" audiences: - start-ups - revops - cros - fractional --- # The Smart Guide to Acquisition Lifecycle Stages & CRM Setup _Free resource_ ## Get the guide That’s why we created **The Smart Guide to Acquisition Lifecycle Stages & CRM Setup**—a comprehensive ebook that gives you the blueprint to turn your CRM into a revenue engine. ![](https://cdn.sanity.io/images/ys8gstp8/production/1317181b727b3e6a083c22028e5854096aecf091-1920x1080.png?w=1600&fit=max&auto=format) > **What you will learn** > - Why defining your lifecycle stages is key to building a **predictable and scalable** pipeline - How to implement a **three-level framework**—built around **the buyer’s journey, key events, and the SPICED methodology**—for smarter lead management. - Best practices for **automating and optimizing your CRM**, with a deep dive into HubSpot Whether you’re a RevOps pro or revamping your CRM setup, this guide will help you improve **data accuracy, conversion rates, and funnel efficiency**. --- --- title: RevOps 101 description: "Maybe you remember walking into a store, facing your fear of human interaction, and buying a physical software package with a stack of floppy disks. So vintage, right?" canonical: "https://vasco.app/blog/revops-101" date: "2025-02-24T00:00:00.000Z" authors: - Sophie Geoffrion jobTitle: "Brand & Content Lead" readingTimeMinutes: 2 contentType: article intent: foundations pillar: "RevOps systems & architecture" --- # RevOps 101 _What is Revenue Operations and Why Does It Matter?_ That all changed in the late 2000s with the rise of cloud computing. As companies moved online, the **SaaS model (Software as a Service)** took over, replacing one-time software purchases with recurring subscriptions. No more upfront fees, just a steady stream of monthly or yearly payments. Enter **Revenue Operations (RevOps)**: a smarter way to manage recurring revenue, align teams, and drive predictable growth. More and more companies are adopting a RevOps platform to drive revenue growth and efficiency. Why not yours? Keep reading to see how it could help your business. ## What is Revenue Operations (RevOps)? Simply put, [**RevOps is the science of sustainable revenue growth**](https://www.youtube.com/watch?v=k204wIknczU). It’s a data-driven approach that aligns your GTM (go-to-market) teams—from marketing and sales to customer success—by breaking down silos and creating a unified revenue strategy. By leveraging standardized, replicable, and measurable processes, RevOps streamlines the entire customer journey, improving lead conversion, retention, and expansion. When executed correctly, it turns revenue into a predictable, scalable engine for long-term growth. ## Key Benefits of RevOps Think of RevOps as a multivitamin for your business—it strengthens every part of your revenue engine. Here’s why it matters: - **Better team alignment 🤝** Marketing, sales, and customer success work as one, ensuring seamless handoffs and fewer leaks in the funnel. Want to know the _true_ cost of misalignment? Read [The Hidden Revenue Killer](/blog/the-hidden-revenue-killer) by Aaron Ross, author of Predictable Revenue. - **More accurate revenue forecasting** **📊** No more guesswork. RevOps provides up-to-date visibility into performance, helping teams track progress against targets and make smarter decisions. - **Improved customer retention & expansion **👌 A smoother onboarding process and proactive upsell opportunities maximize customer lifetime value. - **Greater operational efficiency** **⚙️** Automating key processes reduces manual work, minimizes errors, and allows teams to focus on high-impact activities. > RevOps doesn’t just generate revenue—it optimizes it at every stage. By improving visibility and coordination across teams, it creates a frictionless experience that keeps customers engaged and revenue flowing.How to Get Started with RevOps? ## How to Get Started with RevOps? “So RevOps is just what my CRM does, right?” Well… not exactly. A CRM like HubSpot or Salesforce **stores** your data, but RevOps is what makes that data **actionable**—turning insights into growth strategies. [📥 **Free playbook : CRM lifecycle stages and CRM setup**](https://vasco.app/blog/lifecycle-stages-and-crm-setup) At Vasco, we see RevOps as a continuous cycle built around data-driven decision-making: ![Diagnose, plan, and execute. At Vasco, we see RevOps as a continuous cycle built around data-driven decision-making.](https://cdn.sanity.io/images/ys8gstp8/production/90fc4a3441547188050b7b52cbb3f62e3c92feb2-1280x720.png?w=1600&fit=max&auto=format) By following this cycle, companies build a **predictable, scalable revenue engine**. ## FAQ ### What is Revenue Operations (RevOps)? Revenue Operations (RevOps) is the practice of aligning marketing, sales, and customer success around a single revenue strategy. It uses standardized, data‑driven processes to improve lead conversion, retention, and expansion while turning revenue into a predictable, scalable engine for growth. ### Why is RevOps important for SaaS companies? RevOps matters for SaaS because recurring revenue depends on predictable, repeatable processes. By aligning GTM teams, standardizing data, and automating key workflows, RevOps improves forecasting accuracy, reduces churn, and increases expansion revenue over time. ### What are the main benefits of implementing RevOps? Key RevOps benefits include better team alignment, more accurate forecasting, improved retention and expansion, and greater operational efficiency. It reduces manual work, minimizes errors, and creates a smoother customer journey from lead to renewal. ### How does RevOps improve revenue forecasting? RevOps improves forecasting by centralizing data, standardizing pipeline stages, and applying consistent rules across teams. This gives leaders up‑to‑date visibility into performance, reduces guesswork, and supports more accurate top‑down and bottom‑up planning. ### How do you get started with RevOps? To get started with RevOps, define clear revenue goals, audit your current systems and data, and align stakeholders on shared metrics. Then build a roadmap that prioritizes lifecycle stages, data hygiene, and automation before layering in advanced forecasting and analytics. ### What is the “bowtie” framework in RevOps? The bowtie framework is a RevOps model that visualizes the customer journey from lead to renewal, with expansion loops in the middle. It helps teams design aligned workflows for acquisition, onboarding, and expansion so revenue becomes repeatable and scalable. ### How does RevOps improve customer retention and expansion? RevOps improves retention and expansion by standardizing handoffs, tracking health signals, and surfacing upsell opportunities early. Proactive insights and clear ownership across teams make it easier to renew customers and grow accounts over time. --- --- title: The Bowtie Model description: "Just like a bridge that only takes you halfway across the river, the traditional funnel has its limits. In a recurring revenue business model, growth doesn’t stop when you close a deal. It’s just getting started. Enter the Bowtie Model." canonical: "https://vasco.app/blog/the-bowtie-model" date: "2025-02-24T00:00:00.000Z" authors: - Sophie Geoffrion jobTitle: "Brand & Content Lead" readingTimeMinutes: 3 contentType: article intent: foundations pillar: "RevOps systems & architecture" --- # The Bowtie Model _Goodbye linear funnel, hello full lifecycle framework_ ## What is the Bowtie Model? Think of your classic sales funnel: leads enter at the top, deals close at the bottom, and… that’s it? Not in **B2B SaaS**. Unlike traditional sales, where revenue is realized upfront, in SaaS, revenue is spread over the long term. Customers typically pay on a monthly or annual basis, and it may take months—or even years—before the revenue they generate covers your acquisition costs and starts turning a profit. Now, take the old-school funnel, rotate it 90 degrees counterclockwise, and extend it to the right. You’ve got the **Bowtie Model**—where revenue is focused on acquisition, but also on post-sale retention and expansion throughout the entire Customer Lifecycle. ## The Bowtie Breakdown: key lifecycle stages and metrics At Vasco, the Bowtie Model is more than a concept—it’s the foundation for predictable, scalable revenue. Inspired by [Winning by Design](https://www.youtube.com/watch?v=XCRtEVsTXsc&t=283s), it ensures alignment between GTM (Go-to-Market) teams and facilitates a seamless transition of leads across lifecycle stages. ![](https://cdn.sanity.io/images/ys8gstp8/production/178a25f8b92b3edfc6ae2e13c7b24ab2291dab8d-1280x720.png?w=1600&fit=max&auto=format) ### Acquisition (from Lead to Closing) On the **left side** of the bowtie, your goal is to **attract, qualify, and convert** leads into paying customers. Every stage serves as a **checkpoint** in their buying journey: 1. **Lead → MQL (Marketing Qualified Lead) **The lead is aware of a challenge and starts researching solutions. It’s a perfect match for marketing engagement, but they’re not yet sales-ready. 2. **MQL → SQL (Sales Qualified Lead) **At this stage, the lead has deepened their research and is comparing different approaches. They’re now a fit for both marketing nurturing and sales outreach. 3. **SQL → SAL (Sales Accepted Lead) **Decision time! They’re shortlisting vendors and making their pick. Leads book demos, request trials, and dive into product capabilities. 4. **SAL → **Customer Contract signed, deal closed! 🎉 Time to celebrate… briefly. > **Key Metrics to Track (Acquisition Phase)** > In addition to conversion rates between each stage, keep an eye on these key metrics: > - Sales Cycle Length: How long it takes to close a deal from initial contact. - Pipeline velocity: How fast leads move through the stages. - ARR growth: Annual Recurring Revenue, or new revenue added. - Funnel Leakage: Where leads ghost you and fall out of the process. ### Expansion (post-sale) On the **right side** of the bowtie, the **real game begins**—keeping customers and **maximizing** their value. 1. **Customer → Active User: **The onboarding phase kicks off, with Customer Success (CS) guiding the way. Customers start using the product consistently and continue to pay their subscription. 2. **Active User → Retention or Churn: **When renewal time comes, customers either stay (retention) or leave (churn). 3. **Renewed Customer → Expansion (Upsell/Cross-sell): **Happy customers expand usage: adding seats, upgrading, or purchasing more features. > **Key Metrics to Track (Expansion Phase)** > In addition to conversion rates between each stage, key metrics to monitor include: > - Time to First Value (TTFV): How quickly customers experience value from the product. - Product Adoption Rate: How effectively customers are using the product post-onboarding. - Gross Retention Rate (GRR): The percentage of customers retained over a period. - Churn Rate: The rate at which customers leave the service. - Net Revenue Retention (NRR): The percentage of recurring revenue retained, including expansion revenue. - Customer Lifetime Value (CLV): The total revenue a customer generates throughout their relationship with the company. Successful RevOps teams understand that Customer Retention and reducing Churn are crucial to creating long-term value. ## RevOps Tech Stack for the Bowtie Model Mastering the Bowtie Model means mastering your data. Without the right **Tech Stack for RevOps**, you're flying blind. This is where an **[all-in-one RevOps platform](/) like Vasco**, integrated with **CRMs like HubSpot or Salesforce**, changes the game. Vasco equips RevOps teams with: - **Revenue Architecture**: A science-based blueprint for scaling your Go-To-Market (GTM) strategy. - **Radar:** Data health monitoring to ensure your insights are built on reliable, accurate data. - **Business Intelligence**: Bowtie-based and custom reporting for full-funnel clarity, so you can see the bigger picture. - **Gama AI**: Your AI-powered revenue architect copilot, because manual work won’t scale growth. With unified data, automated processes, and AI-driven insights, Vasco eliminates guesswork and turns RevOps into a **predictable, scalable growth engine**. ## FAQ ### What is the Bowtie Model in RevOps? The Bowtie Model is a RevOps framework that extends the traditional sales funnel into a full customer lifecycle. It shows revenue as a continuous loop: acquisition on the left, conversion in the middle, and retention and expansion on the right, making it ideal for SaaS and recurring‑revenue businesses. ### How is the Bowtie Model different from a sales funnel? Why is the Bowtie Model important for SaaS? ### The Bowtie Model matters for SaaS because revenue is recurring, not one‑off. It forces teams to invest in onboarding, health, and expansion, so they can grow revenue even without constant new acquisition and improve net revenue retention (NRR). How does the Bowtie Model improve alignment between teams? ### The Bowtie Model improves alignment by giving marketing, sales, and customer success shared stages and metrics across the entire lifecycle. Everyone sees how their work contributes to acquisition, retention, and expansion, not just their own siloed KPIs. What metrics should I track with the Bowtie Model? ### Key metrics include conversion rates between each Bowtie stage, time‑to‑value, churn, renewal rate, and net revenue retention (NRR). Tracking these across the full lifecycle helps identify leaks and opportunities on both the acquisition and expansion sides. How do I start implementing the Bowtie Model in my CRM? ### To start, map your current stages to the Bowtie lifecycle (Lead → Customer → Active User → Renewal → Expansion). Then align GTM teams on definitions, set up stage‑based reporting, and gradually layer in Bowtie‑specific metrics like time‑to‑value and NRR. How can a revenue architecture platform like Vasco support the Bowtie Model? ### How can a revenue architecture platform like Vasco support the Bowtie Model? A revenue architecture platform like Vasco unifies data across acquisition and expansion, enforces Bowtie‑based lifecycle stages, and powers Bowtie‑aligned dashboards and reports. It surfaces bottlenecks, monitors data health, and surfaces velocity so RevOps can optimize the full lifecycle. --- --- title: Your SDR Team is Dying description: "When I built my first outbound sales team from scratch in 2002, I thought I had the blueprint for perfect sales alignment figured out. We had our systems, our processes, our metrics. In short, everything seemed perfect on paper. But something wasn't clicking." canonical: "https://vasco.app/blog/your-sdr-team-is-dying" date: "2025-02-24T00:00:00.000Z" authors: - Aaron Ross jobTitle: Author of Predictable Revenue readingTimeMinutes: 5 contentType: article intent: playbooks-methods pillar: "GTM & RevOps execution" --- # Your SDR Team is Dying _Here's the Fix_ Despite our best efforts, there was this persistent disconnect between what marketing was promising, what sales were selling, and what customers were actually getting. Fast forward to today, and I'm seeing this same misalignment everywhere, but at a much larger scale. The problem isn't just between sales and marketing anymore; it's across entire GTM organizations. Companies with strong sales and marketing alignment achieve [24% faster growth rates and 27% faster profit growth](https://www.b2brocket.ai/blog-posts/how-sales-marketing-misalignment-affects-lead-generation?) over a three-year period. So in today's market, where every deal counts and customer expectations are higher than ever, misalignment isn't just inconvenient; it’s devastating to your revenue growth. ## The Three Deadly Sins of GTM Misalignment Let me be direct: Your GTM teams are probably operating like separate businesses under one roof. I see it all the time—marketing creates their campaigns in isolation, sales development teams work from their own playbook, and account executives have a completely different view of what success looks like. Meanwhile, customer success is left trying to deliver on promises they never made. ![Misalignment between marketing, sales development, and customer success teams.](https://cdn.sanity.io/images/ys8gstp8/production/e2777a0832fc3a08a70e6c23392c8778336b1473-1280x720.png?w=1600&fit=max&auto=format) 1. The first deadly sin is what I call "**The Metric Mirage**." Each team is chasing their own numbers without understanding how they impact the bigger picture. Marketing celebrates MQL volumes while sales complains about lead quality. Sales development teams focus on meeting activity quotas while account executives grumble about poor qualification. It's like everyone's running their own race on different tracks. 2. The second sin is "**The Handoff Hemorrhage**." This is where value and context get lost every time a customer moves from one team to another. Marketing captures valuable intent data that never makes it to sales. Sales development uncovers crucial pain points that don't get communicated to account executives. And by the time customer success enters the picture, they're starting from scratch understanding the customer's needs. 3. The third sin is what I've started calling "**The Customer Confusion Crisis**." With each team telling their own story, customers end up hearing different value propositions, different promises, and different expectations at each stage of their journey. No wonder they're getting harder to close—we're confusing them! ## The Real Cost of Misalignment Here's something that keeps me up at night: the cost of this misalignment is far bigger than most leaders realize. We're not just talking about lost deals or inefficient processes. The real cost comes in three forms that compound over time. - First, there's **the trust deficit**. Every time a customer experiences disconnection between what they were promised and what they receive, it erodes trust. Inconsistent messaging from misaligned teams erodes trust and can drive potential customers to competitors. In today's market, where buyers are more skeptical than ever, we can't afford to keep breaking trust through misalignment. - Second, there's **the motivation drain**. Your teams aren't blind - they see the disconnects and feel the friction. Over time, this creates frustration, reduces motivation, and leads to higher turnover. I've seen entire sales development teams implode because they felt disconnected from the larger revenue mission. [More than half (52.2%) of sales professionals](https://blog.hubspot.com/sales/stats-that-prove-the-power-of-smarketing-slideshare) report that the most significant consequence of misalignment between sales and marketing teams is a loss in sales and revenue. - Finally, there's **the opportunity cost**. When your teams aren't aligned, you're not just losing deals—you're missing opportunities to create value at every stage of the customer journey. Every misaligned interaction is a missed chance to deepen customer understanding and create more value. ## The Revenue Architecture Solution This is where **Revenue Architecture** comes in. But let me be clear - I'm not talking about another fancy framework that looks good in PowerPoint presentations. I'm talking about a fundamental reimagining of how your GTM teams work together. Think of Revenue Architecture like the blueprint for a house. You wouldn't build a house by having different contractors work independently without a master plan. Yet that's exactly how many companies approach their revenue operations. True Revenue Architecture starts with mapping your entire **customer journey**—not from your perspective, but from your customer's. This means understanding how value is created, communicated, and delivered at every stage. It means aligning your teams around a single, coherent story that evolves with the customer's journey. Highly aligned organizations have seen a [32% YoY revenue growth](https://www.invoca.com/blog/10-stats-that-will-drive-your-sales-marketing-alignment?), while less aligned competitors saw a 7% decrease in revenue in 2023. Not sure how to map your customer journey or where to begin? See our [Smart Guide to Acquisition Lifecycle Stages & CRM Setup.](/blog/lifecycle-stages-and-crm-setup) ## Implementing Team Alignment Through Revenue Architecture The key to successful implementation is what I call "**The Three C's of Revenue Architecture": Context, Continuity, and Coordination**: - **Context **means ensuring every team understands not just their role, but how it fits into the bigger picture. Your sales development team shouldn't just know their activity targets - they should understand how their work impacts the entire customer journey. - **Continuity** is about creating seamless transitions between teams. This means developing shared processes, metrics, and handoff protocols that preserve and build upon customer context at every stage. - **Coordination** is about creating the infrastructure for teams to work together effectively. This means shared systems, shared data, and shared goals that encourage collaboration rather than competition. ## Making the Shift I know what you're thinking—this sounds great in theory, but how do we actually make it happen? The answer is **systematic**, but it doesn't have to be complicated. - Start by creating a **single source of truth** for your customer journey. Map out every touchpoint, every handoff, and every decision point. Identify where value is created, where it's at risk, and where it's potentially lost. - Next, align your metrics around **customer value creation** rather than departmental activities. This means moving beyond basic activity metrics to measures that reflect the quality of customer progression through their journey. - Finally, invest in the technology and processes that enable seamless collaboration. Your teams need more than just CRM - they need a [**complete revenue architecture platform**](/)that gives them the visibility, tools, and insights to work together effectively. ## The Path Forward The market isn't getting any easier. Buyers are more demanding, sales cycles are getting longer, and competition is increasing. The companies that will thrive are those that can create a seamless, value-driven experience for their customers—and that only happens when your GTM teams are **truly aligned**. This is why I'm excited about platforms like **Vasco** that are tackling this challenge head-on. By providing a comprehensive **revenue architecture platform** that brings together all aspects of your GTM motion—from planning and execution to measurement and optimization - they're making it possible to achieve the kind of **alignment** that today's market demands. ## Taking Action If you're ready to address the **GTM alignment challenge** in your organization, start by asking yourself these questions: - Do all your teams share the same understanding of your customer journey? - Are your handoff processes preserving and building upon customer context? - Are your metrics driving collaboration or competition? The answers might be uncomfortable, but they're the first step toward building a **truly aligned revenue organization**. ## FAQ ### What are the main reasons SDR teams fail? Common reasons SDR teams fail include misaligned metrics (MQLs vs. SQLs vs. revenue), poor handoffs between marketing and AE, and inconsistent messaging that confuses buyers. Without clear alignment, SDRs become a bottleneck instead of a growth engine. ### How does GTM misalignment hurt SDR teams? Misalignment hurts SDR teams by feeding them low‑quality or poorly qualified leads, stripping away context at handoffs, and making it harder to close deals. This leads to frustration, burnout, and higher turnover, which compounds revenue loss over time. ### What is Revenue Architecture and how does it help SDRs? Revenue Architecture is a system that aligns marketing, SDRs, AEs, and customer success around a single customer journey and set of metrics. It gives SDRs clearer targeting, better‑qualified leads, and smoother handoffs, so they can focus on value‑driven conversations instead of chasing activity quotas. ### How to fix misalignment between marketing and SDRs? To fix misalignment, align both teams around shared definitions (ICP, lead stages, handoff criteria) and shared metrics that reflect customer value, not just activity. Map the journey together, clarify who owns what, and design handoff processes that pass context, not just records. ### What metrics should SDR teams track instead of just activity? Instead of only tracking calls and emails, SDR teams should track: qualified meetings held, pipeline generated, and revenue influenced. Pair these with quality signals like fit, intent, and progression through stages so you reward value creation, not just motion. --- --- title: How Fabriq Aligned Its Revenue Teams Around a Best-in-Class GTM Framework company: Fabriq description: How Fabriq Aligned Its Revenue Teams Around a Best-in-Class GTM Framework canonical: "https://vasco.app/customer-stories/fabriq" industry: Industrial SaaS stage: Scale-up stack: - Salesforce topFeatures: - "Top-Down & Bottom-Up Planning" - Pulse - Weekly Digest --- # How Fabriq Aligned Its Revenue Teams Around a Best-in-Class GTM Framework _How Fabriq Aligned Its Revenue Teams Around a Best-in-Class GTM Framework_ ## The Challenge **Alban wasn't looking to replace his tool stack. What he needed was a foundation to build the right revenue system — one that could give the whole company a shared, structured view of GTM performance.** Fabriq is the all-in-one platform for operational excellence in manufacturing plants. By digitalizing lean management and daily performance routines, it helps factory teams respond faster to performance gaps, solve problems at the right level, and drive continuous improvement on the shop floor — with customers across Europe and North America. Alban van Rijsewijk joined Fabriq as one of its first five employees and grew into the Head of RevOps, overseeing a team of four. His ambition was clear: build a best-in-class GTM operation grounded in the Winning by Design framework. But with a small team and no dedicated data function, achieving that vision meant doing everything himself, from CRM administration to data analysis to pipeline reporting. He knew the complexity would only grow over time, and wanted to future-proof his RevOps infrastructure before it became a problem. On top of that, with Fabriq’s complex, non-standard go-to-market motion — landing a single factory, then expanding to the full group over time — and a data stack that wasn't designed for that kind of analysis, getting there was easier said than done. - **Time-to-insights was too slow.** Getting a clear view of sales cycle length, conversion rates between funnel stages, or any other GTM metric required significant manual effort. - **The Winning by Design bowtie was impossible to operationalize.** Fabriq's land-and-expand motion didn't map neatly onto standard SaaS frameworks, making it hard to track performance coherently across acquisition and expansion. ## Our Approach Alban came across Vasco through a recommendation from a RevOps consulting firm that saw in the product a way to bring his vision to life at speed. Implementation had some challenges — not because of the product itself, but because of the complexity of mapping Fabriq's unique go-to-market onto a structured set of best practices. The Vasco team rose to the challenge. _"The team was very dedicated to the success of the project. There are very smart people working at Vasco. They have a clear understanding of the go-to-market complexity in terms of systems and data. The discussions we had helped me a lot to move forward with my vision."_ Vasco was implemented as Fabriq's GTM operating layer in 2025 and was quickly adopted by the CEO, VP Sales, and sales managers for their weekly cadence. By January 2026, planning and targets were built directly in Vasco, making it the primary source of truth for GTM performance. Key features driving adoption: - **Top-Down & Bottom-Up Planning:** Alban uses this to set revenue targets and cascade them into top-of-funnel goals across markets, with full alignment across sales and marketing. - **Pulse:** The VP Sales uses the Pulse in every sales meeting, giving the full team a live, high-level view of bowtie performance and surfacing where to investigate. - **Weekly Digest:** Every Tuesday, a digest lands in the inboxes of the CEO, VP Sales, sales managers, and the CFO — keeping leadership oriented around the same numbers without requiring anyone to log in. A key part of Vasco's value, Alban explains, is structural: _"RevOps best practices are built into the product, which made it possible to implement the Winning by Design framework at Fabriq. Using Vasco encourages you to deploy the standard in your systems."_ > "The team was very dedicated to the success of the project. There are very smart people working at Vasco. They have a clear understanding of the go-to-market complexity in terms of systems and data. The discussions we had helped me a lot to move forward with my vision." > "RevOps best practices are built into the product, which made it possible to implement the Winning by Design framework at Fabriq. Using Vasco encourages you to deploy the standard in your systems." ## Results ### TIME SAVED ACROSS THE BOARD Converting revenue targets into top-of-funnel targets now takes 70% less time. Monthly reporting 80% automated. And roughly 50% of investor reporting now covered by a Vasco dashboard. Across the board, time that used to go into assembling data now goes into using it. ### FASTER, PROACTIVE REACTIONS TO PIPELINE SIGNALS When Vasco flagged a pipeline creation shortfall in late January — visible in real time — the team mobilized immediately: pushing reps to qualify stalled opportunities, generate new meetings, and rethink outbound in the U.S. Two to three weeks later, pipeline was trending in the right direction. ### A SINGLE SOURCE OF TRUTH Vasco anchors the data culture across sales, marketing, and customer success. With everyone working from the same KPIs and definitions, alignment that used to require constant back-and-forth now happens naturally. > If you're a RevOps person trying to build a mature, scalable go-to-market system, Vasco is definitely worth it. What it gives you isn't just visibility. It makes you and your team strategic, because you stop spending your time chasing data and start spending it on decisions that matter. > > — Alban van Rijsewijk, Head of RevOps --- --- title: How elia gained full Visibility and Control of its Sales Pipeline With Vasco company: elia description: Visibility and Control of its Sales Pipeline With Vasco canonical: "https://vasco.app/customer-stories/elia" industry: SAAS stage: Seed solution: Start-ups stack: - HubSpot topFeatures: - Daily digest - "Top-down & bottom-up planning" - Unit economics tracking --- # How elia gained full Visibility and Control of its Sales Pipeline With Vasco _Visibility and Control of its Sales Pipeline With Vasco_ ## The Challenge **After raising a seed round in late 2024, elia knew it was time to put the right systems in place. HubSpot replaced their previous CRM, but for revenue forecasting and unit economics, they needed more.** [elia, a workplace platform launched](https://www.elia.io/) in 2023 by Anthony Blais and his team at [GPHY](https://www.gphy.ca/), helps companies optimize their office space, improve employee experience, and streamline operations. Targeting organizations with 200+ employees, elia quickly gained traction—growing to more than 150 customers across Canada and the U.S. within its first year. But growth brought complexity. As Anthony put it: _“Our biggest problem was we didn’t know where our leads were coming from or what our sales cycles really looked like. We couldn’t make investment decisions because we didn’t have the visibility.”_ - In 2023, the team relied on an unfit CRM, plus manual Excel spreadsheets to track leads and sales performance. - Sales cycles and lead sources were unclear, making it impossible to decide where to allocate budget: marketing, sales hires, or outbound? - There was a recurring disconnect between the CEO’s forward-looking view of sales and the CFO’s accounting-driven metrics. - Every board meeting required hours of manual data consolidation, slowing decision-making. ## Our Approach At first, Anthony was hesitant. HubSpot already provided dashboards, and the team could always stitch together reports in Excel. Vasco seemed like a “nice to have” rather than a must. But once the team realized Vasco could combine pipeline forecasts with costs, unit economics, and financial data in real time, it clicked. elia implemented Vasco in early 2025, connecting directly to HubSpot. Key features quickly became part of their daily rhythm: - **Daily Digest**: Anthony starts every morning by refreshing his inbox to see Vasco’s email summary of leads and pipeline progress. - **Forecasting tools**: The team uses top-down and bottom-up planning features to set realistic revenue goals and align resources. - **Unit economics visibility**: By blending HubSpot pipeline data with costs, elia now understands CAC, payback, and magic number by channel, geography, and customer type. Vasco also gave both CEO and CFO a common source of truth: sales metrics for forward-looking decisions, while financial reporting remained anchored in accounting. > Just as importantly, Anthony knew he would be supported by Vasco’s revenue operations experts bringing best practices and customer success specialists to guide implementation, a factor that gave him confidence to move forward. > "Thanks to Vasco, I know my numbers by heart. I feel in control, and when my investors call, I have answers in two clicks." — Anthony Blais ## Results ### Decisions backed by data Lead sources, conversion rates, and CAC are now tracked with precision, guiding where to invest in marketing and sales. ### Board meeting prep time cut by 4–5x Instead of waiting for month-end Excel reports, Anthony uses Vasco’s built-in board deck template to update investors. ### A stronger leadership dynamic Sales, marketing, finance, and leadership now work from the same numbers, reducing internal friction and aligning on growth targets. > If you’re scaling and want to know where your growth is really coming from, Vasco gives you clarity and control. For me, it’s part of my morning routine. I wouldn’t want to run elia without it. > > — Anthony Blais, Founder & CEO [Start-ups](/solutions/startups) --- --- title: How Leanscale scaled its fractional RevOps services company: Leanscale description: fractional RevOps services canonical: "https://vasco.app/customer-stories/leanscale" industry: GTM Agency stage: GTM Firm for Series A-D startups solution: Fractional stack: - HubSpot - Salesforce - Stripe topFeatures: - "Plan (top-down)" - Actuals vs. forecast - Stage diagnostic --- # How Leanscale scaled its fractional RevOps services _fractional RevOps services_ ## The Challenge **Without a reliable system, it was hard for Leanscale to standardize operations, drive efficiency across client engagements, and scale services.** [Leanscale](https://leanscale.team/) is an embedded** GTM Ops agency **based in Arizona. The team supports hypergrowth startups such as [Clio](https://www.clio.com/), [Chainguard](https://www.chainguard.dev/), [Mistral](https://mistral.ai/), and [Moonvalley](https://www.moonvalley.com/). As Partner and COO, **Joseph Zaghloul** draws on his CRO background and firsthand experience with the challenges of scaling revenue operations to help clients engineer and execute ambitious growth plans with velocity. Working alongside teams building groundbreaking technologies is exciting, and a constant reminder that in these VC-backed environments, speed is mission critical. Yet, relying on generic BI tools like Looker created friction for the Leanscale team. These platforms weren’t designed for revenue operations, and fell short in four key ways: - Inconsistent reporting to plan: Packs had to be built manually each time, which made reporting inefficient and lacked a uniform structure. - Inefficient manual processes: Too much time spent creating and maintaining reports, making it hard to scale. - Slow to insights: Without the right visualizations, consultants struggled to deliver accurate insights quickly, making client alignment harder. - Team pressure: Heavy coordination burdens weighed on morale and pulled focus away from high-value client work. ## Our Approach By transitioning to Vasco, **Leanscale was able to operationalize its growth model and flywheel within a single, centralized tool.** Key changes included: - **Establishing a single source of truth**: Replacing manual, static reports with Vasco’s dynamic dashboards to centralize information and ensure reliability. - **Making the methodology actionable**: Embedding Leanscale’s approach directly in Vasco so it could be applied consistently. - **Embedding Vasco in training**: every new hire now learns how to operationalize the growth model using Vasco from day one. - **Driving adoption with clients**: consultants began introducing Vasco into client processes, aligning priorities and decisions around the same operating plan used by boards and investors. > Onboarding was smooth and supported by Vasco’s “white glove” approach, with constant guidance from Vasco experts on how to scale operations, paired with client-facing sessions to onboard accounts and build alignment with clients. > Vasco quickly became a cornerstone of Leanscale’s methodology. It has also become a competitive advantage in sales conversations, as Vasco is now positioned as a default part of Leanscale’s value proposition. ## Results ### Time savings At least 5 hours per month per consultant saved on reporting packs, while reducing the burden of manual coordination ### Scalability Centralized systems replaced ad hoc dashboards, making processes repeatable and efficient. ### More time for strategic work Consultants can now focus on advising clients and making high-leverage decisions instead of assembling data. > The way that CROs, CMOs and GTM consultants need to prove their value has to be tied back to the growth model. You can’t just build reports and dashboards that don’t address the real issues. With Vasco, we prioritize what matters most, make high-leverage decisions, and avoid side quests that don’t move the needle. > > — Joseph Zaghloul, Partner & COO [Fractional](/solutions/fractional) --- --- title: "How Alvéole Eliminated 200+ Hours of Spreadsheet Reporting And Gained GTM Clarity" company: "Alvéole" description: Hours of Spreadsheet Reporting And Gained GTM Clarity canonical: "https://vasco.app/customer-stories/alveole" industry: B2B SaaS stage: Scale-up solution: Revops stack: - HubSpot topFeatures: - Forecast scenario modelling - Conversion tracking - Channel attribution drilldown --- # How Alvéole Eliminated 200+ Hours of Spreadsheet Reporting And Gained GTM Clarity _Hours of Spreadsheet Reporting And Gained GTM Clarity_ ## The Challenge **“When analyzing and testing new dimensions, it had to be built manually,” Steven explained. “As the sheet grew and grew, troubleshooting became increasingly time-consuming and frustrating.”** Alvéole brings nature back to cities through urban beekeeping, helping commercial real estate properties create meaningful, measurable sustainability experiences for tenants and communities. Their GTM execution had grown at immense speed, developing new channels and expanding across international markets. But like many rapid growth companies, Alvéole’s revenue operations relied on legacy tools, manual processes, and one RevOps team holding it all together. As RevOps Manager, Steven Parth had built an intricate reporting system that integrated CRM data with Google Sheets for top-down and bottom-up planning, along with a sophisticated scenario modelling formula. The spreadsheets were powerful and deeply customized. But as Alvéole entered a new stage of growth, three key limitations stood in their way. What Alvéole needed was the ability to rapidly analyze GTM performance and validate new strategies to help support their new stage of growth, without the time commitment it took from RevOps. - Reporting complexity outpaced what spreadsheets could handle - Maintaining and updating the models became too time-consuming - GTM teams couldn’t easily access or explore performance data ## Our Approach Alvéole adopted Vasco to move beyond static, manual spreadsheets and into real-time, self-serve forecasting and performance reporting. Using Vasco’s Planning Hub, they replaced their spreadsheet model with a live, filterable dashboard synced directly from their HubSpot data. Conversion rates and funnel math were prebuilt and easily adjustable, eliminating the need for manual calculations. Now, GTM leaders could explore their own scenarios, test growth plans, and dive into performance data without needing Steven to generate or validate the numbers. Not only did Vasco centralize GTM performance reporting, it also gave Alvéole the diagnostic capabilities to identify performance gaps, allowing Steven to quickly share strategic insights to the team to optimize performance. > “With Vasco, it’s easy to test assumptions and scenarios by changing one cell and seeing the impact across the board,” Steven explained. > “It’s made it easier to discuss what’s working and what isn’t by diving deeper into the data behind every lifecycle stage,” Steven explained. “We can quickly see our performance in one place, which makes planning and decision-making much simpler.” ## Results ### 200+ hours saved By automating what was once constant spreadsheet maintenance, Steven reclaimed more than 200 hours annually. Go-to-market leaders no longer depend on RevOps for metrics. They access accurate, real-time data themselves. ### A single source of truth Vasco has become the trusted source of clean, reliable data across teams. “When team members doubt channel performance, we are able to confidently validate the numbers in Vasco. It’s become our single source of truth for GTM performance,” Steven shared. This consistency has enabled more productive discussions focused on optimizing overall GTM performance. ### Stronger alignment with leadership RevOps and the CRO now operate from the same metrics and definitions. “The entire team is far more aligned on the numbers that matter most and how we define them,” Steven explained. Instead of spending time diagnosing broken reporting, he can now focus on advising the business on where to improve performance. > In RevOps, data is the foundation for trust and alignment. When everyone can analyze the numbers, it’s much easier to make informed decisions together. > > — Steven Parth, RevOps Manager @ Alvéole [Revops](/solutions/revops) --- --- title: How Lightspeed Built a Predictable Growth Engine with Vasco company: Lightspeed description: a Predictable Growth Engine with Vasco canonical: "https://vasco.app/customer-stories/lightspeed" industry: SaaS stage: IPO solution: Enterprise stack: - HubSpot topFeatures: - Advanced BI Reports - Granular Forecasting - Unified Source of Truth --- # How Lightspeed Built a Predictable Growth Engine with Vasco _a Predictable Growth Engine with Vasco_ ## The Challenge **Vasco has been a critical part of our journey, providing the structure and insights necessary to support Lightspeed’s rapid growth. The platform’s ability to bring clarity and accountability to our teams has been a game-changer** As a leader in cloud-based commerce solutions, Lightspeed Commerce was growing rapidly. They needed to build a more predictable growth engine by: - aligning their sales and marketing teams - improving forecasting accuracy - creating a unified process for better accountability and collaboration ## Our Approach > Lightspeed Commerce, under the leadership of JD St-Martin - President, turned to Vasco to implement a robust revenue architecture. > By leveraging Vasco’s advanced features, they enhanced their revenue operations, leading to more predictable growth and scalable processes. ## Results ### Enhanced Forecast Accuracy With Vasco’s detailed reporting and forecasting capabilities, Lightspeed significantly improved the accuracy of their revenue projections. ### Better Team Alignment Vasco fostered better collaboration and accountability between sales and marketing, enhancing overall team performance. ### Predictable and Scalable Growth By adopting Vasco’s revenue architecture, Lightspeed established a blueprint for sustained and predictable growth. > Vasco has been a critical part of our journey, providing the structure and insights necessary to support Lightspeed’s rapid growth. The platform’s ability to bring clarity and accountability to our teams has been a game-changer > > — JD St-Martin, President, Lightspeed Commerce [Enterprise](/solutions/enterprise) --- --- title: How Vessel Found Revenue Architecture Clarity with Vasco company: Vessel description: Revenue Architecture Clarity with Vasco canonical: "https://vasco.app/customer-stories/vessel" industry: SaaS stage: Seed solution: Start-ups stack: - HubSpot topFeatures: - Actuals vs Forecast - Daily Digest - Planning --- # How Vessel Found Revenue Architecture Clarity with Vasco _Revenue Architecture Clarity with Vasco_ ## The Challenge **"Without visibility into GTM performance, our sales process felt like throwing darts in the dark."** Vessel was founded by former institutional investors with deep conviction and early proof that their product was resonating with fund managers. The team had strong instincts and a growing pipeline, especially from conferences and outbound, but struggled to translate momentum into repeatable success. - Pipeline metrics were complex, data integrity was challenged, and like most high-velocity teams, performance tracking was cobbled together from manual HubSpot exports. - Thierry Ajaltouni, Co-Founder of Vessel was seeing market pull but the sales process felt like “throwing darts in the dark” without a consistent way to track inputs and outcomes. - The team needed structure: a clear GTM motion, fast feedback loops, and reporting they could trust. ## Our Approach Vessel implemented Vasco across their GTM motion, setting weekly targets to hold teams accountable around a shared goal and tracking performance against them. The team now had a clear view of where follow-ups were falling off and which channels were underperforming. Instead of asking “how did we do,” Thierry and his team started every week knowing what needed to happen: how many leads, what conversion rates, and where to focus. Vessel implemented Vasco across their GTM motion, setting weekly targets to hold teams accountable around a shared goal and tracking performance against them. The team now had a clear view of where follow-ups were falling off and which channels were underperforming. > “With Vasco, I feel like we’re no longer running blind. It’s our secret weapon for maintaining focus, achieving predictable sales, and presenting data with confidence to investors.” > Instead of asking “how did we do,” Thierry and his team start every week knowing what needs to happen: how many leads, what conversion rates, and where to focus. ## Results ### Structured pipeline generation Vasco helped Vessel shift from reactive outreach to a weekly, trackable GTM rhythm based on real conversion data. ### Credible reporting that builds investor trust The team used Vasco to present clear, accurate data with a strong narrative to their investors, reinforcing clarity and control. ### Faster decisions, fewer misses With clear benchmarks and live performance tracking, Vessel reduced missed follow-ups and made better calls, faster. > With Vasco, I feel like we're no longer running blind. It’s our secret weapon for maintaining focus, achieving predictable sales, and presenting data with confidence to investors. > > — Thierry Ajaltouni, Co-Founder, Vessel.co [Start-ups](/solutions/startups)