Most people have never seen what AI-native go-to-market actually looks like under the hood — and almost nobody has seen it run across an entire portfolio at once.

LeanScale runs revenue operations for dozens of fast-growing SaaS companies simultaneously. Different CRM, different data, different definitions of basically everything. Small team, and a stack of AI agents doing the work. In this video, CTO Jake Toepel opens the hood on the exact plays — the day-one diagnostic that compresses three weeks of senior consulting into a 20-minute first draft, the QBR where the answer happens live in the room, and the layer of agents that runs the agency itself.

Then he does the part nobody does: he shows you why none of it works out of the box. Point raw AI at real revenue data, ask it a simple question like "what is my pipeline coverage ratio," and you get an answer that is confident, instant, and wrong — because four things are missing. Shared definitions. Identity resolution. The plan. Memory. At one company that's annoying. At 30, it's a liability. The fix is the context graph, and Jake breaks down exactly how it's built.

WHAT YOU'LL LEARNWhy AI-native GTM runs on operations, not on vibes — and what "AI ops" actually meansThe day-one diagnostic agent: three weeks of senior consultant time to a first draft in 20 minutesHow the QBR changes when the answer happens live in the room instead of "we'll follow up next week"The three background agents running the agency: project management, customer health, team evaluationHow field learning feeds back into the playbooks so the system compounds with every callWhy handing your team a stack of Claude licenses does not get you any of thisThe four things missing from raw AI on real data: definitions, identity resolution, plans, memoryWhy one company's messy definitions become 30 boards' worth of confident wrong answersWhat a context graph is, and how a semantic layer resolves one source of truth across CRM, billing, and productThe build order: a foundation that is true first, then the skills, plugins, workflows, and interfaces on top

CHAPTERS00:00 — Running RevOps for a whole portfolio at once01:12 — AI-native GTM does not run on vibes01:45 — What we mean by AI ops02:10 — Play 1 — the day-one diagnostic agent03:15 — Play 2 — the QBR that answers in the room04:15 — Play 3 — the agents that run the agency04:55 — Project management agents: call transcript to scoped tasks05:25 — Customer health and team evaluation agents05:50 — Why the whole system compounds06:05 — Why a stack of Claude licenses isn't enough06:40 — The four missing pieces: definitions, identity, plans, memory07:30 — The context graph, built on Vasco08:15 — A foundation that is true, and the operation on top08:40 — Where to start: an honest assessment of your foundation

ABOUT THE GUESTJake Toepel is the Chief Technology Officer at LeanScale, where he builds and runs the AI operations layer behind the firm's revenue operations delivery across dozens of fast-growing B2B SaaS companies. His work spans the semantic layer that resolves each client's data into a single source of truth, the agent fleet that turns calls into scoped delivery work, and the skills, plugins, and workflows that clients use day to day.

ABOUT LEANSCALELeanScale is a tech-enabled revenue operations firm building the GTM infrastructure behind the fastest-growing B2B SaaS companies. This channel goes under the hood on how AI-native go-to-market is actually built and operated — no hype, just the systems.

▶ Learn more: https://www.leanscale.team▶ Get an honest assessment of your GTM foundation: [ASSESSMENT-LINK]▶ Follow Jake on LinkedIn

#AIOps #RevOps #GTM #GoToMarket #AI #AIAgents #AINativeGTM #ContextGraph #SemanticLayer #RevenueOperations #B2BSaaS #EnterpriseSaaS #GTMEngineering #Vasco #LeanScale

AI Ops: How We Run RevOps for 30 SaaS Companies at Once | Jake Toepel, CTO at LeanScale

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AI Ops: How We Run RevOps for 30 SaaS Companies at Once | Jake Toepel, CTO at LeanScale