---
title: "AI Ops: How We Run RevOps for 30 SaaS Companies at Once"
episode: 100
podcast: "The LeanScale Podcast"
publisher: "LeanScale"
guest: "Jake Toepel"
guest_title: "Chief Technology Officer"
date_published: 2026-08-19
date_modified: 2026-09-02
duration: 00:07:38
word_count: 1293
topics: ["ai-in-gtm", "revenue-operations", "gtm-strategy"]
canonical_url: https://www.leanscale.team/knowledge/podcast/jake-toepel-ai-ops-revops-across-a-portfolio/
source: "LeanScale Knowledge Hub — https://www.leanscale.team/knowledge"
license: "Free to quote and cite with attribution to The LeanScale Podcast."
---

# AI Ops: How We Run RevOps for 30 SaaS Companies at Once — Full Transcript

> Episode 100 of The LeanScale Podcast, with Jake Toepel.
> Published August 19, 2026 · 00:07:38 · 1293 words.
> Machine-transcribed and **not diarized** — speaker attribution is inferred, so verify
> attribution against the audio before quoting a specific person.
> Structured breakdown: https://www.leanscale.team/knowledge/podcast/jake-toepel-ai-ops-revops-across-a-portfolio/

## 00:00 — Running RevOps for a whole portfolio at once

**[0:00]** We run revenue operations for dozens of fast-growing SaaS companies at the same time, different CRMs, different data, different definitions of basically everything, and we do it with a small team and a stack of AI agents.

**[0:15]** Most people have never seen what that actually looks like under the hood.

**[0:19]** AI-native go-to-market run not for one company, but across an entire portfolio at once.

**[0:25]** That's what I'm going to show you today.

**[0:27]** I'm going to show you the exact place we run, and then because none of this comes out of the box, I'm going to show you exactly how it's built, and it all comes down to one thing, the context graph.

**[0:38]** It is the most critical piece of AI-native go-to-market, and it's the reason any of this can be trusted at scale.

**[0:45]** So go through the place with me first, but stay to the end because that's where I break it down.

**[0:51]** Let's get into it.

**[0:52]** Here's the idea that reorganized how we think about all this.

## 00:56 — AI-native GTM does not run on vibes

**[0:56]** Everyone wants AI-native GTM, but AI-native GTM does not run on Vibes.

**[1:01]** It runs on operations.

**[1:03]** RevOps is supposed to be the AI operations function.

**[1:07]** At most companies, it just isn't yet.

## 01:11 — What we mean by AI ops

**[1:11]** So we started calling the thing we actually do AI ops, the connective tissue.

**[1:17]** If you want agents running your go-to-market, somebody has to build and maintain the foundation they run on.

**[1:23]** The definitions, the data model, the skills, the workflows, that is AI ops.

**[1:29]** And that is the difference between AI that demos beautifully and AI you would actually trust to run the business.

**[1:35]** I'm Jake, the CTO at LeanScale, and this is how we do it across a whole book of clients.

**[1:41]** Three plays, here's the first.

## 01:43 — Play 1 — the day-one diagnostic agent

**[1:43]** Play number one, the diagnostic.

**[1:46]** When a new SaaS company comes on board, the old way was weeks of discovery.

**[1:51]** Stakeholder interviews, spreadsheet archaeology, and finally a deck at the end.

**[1:57]** Now on day one, I point our diagnostic agent at their connected data and ask it to find the leaks.

**[2:03]** It runs the whole teardown, funnel conversion by stage, where deals stall, rep coverage, pipeline that is quietly dead.

**[2:11]** And it writes it up against our playbook in our brand voice as a finished diagnostic.

**[2:17]** What used to be three weeks of a senior consultant's time is a first draft in 20 minutes.

**[2:22]** The consultant is still in the loop, but now they're editing judgment, not just assembling data.

**[2:28]** That is how a small team credibly covers dozens of accounts.

## 02:33 — Play 2 — the QBR that answers in the room

**[2:33]** Now play number two is the QBR, and this is where it gets fun because the meeting itself changes.

**[2:39]** It used to be the client asked question we didn't prep for, and we say great question, we'll follow up next week.

**[2:47]** But now the answer happens in the room.

**[2:50]** The client asks, why did net revenue retention dip in the mid market?

**[2:54]** Instead of taking a note, I just ask.

**[2:57]** It pulls the segment, the churned accounts, the reason codes, the expansion that didn't land, live on screen while they watch.

**[3:05]** The QBR stops being a report you present and becomes a conversation you can actually have.

**[3:10]** And every answer is theirs, their definitions, their data, not a generic benchmark I just pulled from somewhere.

## 03:18 — Play 3 — the agents that run the agency

**[3:18]** Play number three, and this is the one I'm most excited about because it is how we run the agency itself.

**[3:24]** Delivering for dozens of clients at once is operationally brutal.

**[3:28]** Every call creates work, every account needs watching, every person needs coaching.

**[3:33]** So we built a layer of agents to run it.

## 03:36 — Project management agents: call transcript to scoped tasks

**[3:36]** First is project management agents.

**[3:38]** Every call transcript that lands, an agent reads it, pulls out the commitments and the action items,

**[3:43]** and turns them into real tasks and projects on our PM board, scoped and assigned.

**[3:49]** The work goes from the call straight to the board with nobody retyping a thing.

## 03:54 — Customer health and team evaluation agents

**[3:54]** Then two more agents run constantly in the background.

**[3:57]** A customer health agent reads every transcript and every Slack channel for the signals that an account is heating up or cooling off,

**[4:05]** long before it would ever surface in a QBR.

**[4:08]** And a team evaluation agent watches our own delivery.

**[4:11]** Who's excelling?

**[4:12]** Where is someone stuck?

**[4:13]** And where can we coach?

## 04:15 — Why the whole system compounds

**[4:15]** And here's the whole thing that makes it compound.

**[4:18]** Everything those agents learn in the field from the transcripts, the Slack channels, the board,

**[4:22]** feeds straight back into our playbooks. Every call we run, every project we deliver,

**[4:27]** the system gets a little bit sharper and the next client gets the benefit.

**[4:31]** The agency is improving itself while we work.

**[4:34]** That is what operationalizing delivery with agents actually looks like.

**[4:39]** Not one assistant, but a whole operating system running the business behind the scenes.

## 04:44 — Why a stack of Claude licenses isn't enough

**[4:44]** Now, everything I just showed you, you cannot do by handing your team a stack of clawed licenses.

**[4:50]** And at our scale, the reason is brutal.

**[4:53]** Watch raw AI on real data.

**[4:55]** Same simple question.

**[4:57]** What is my pipeline coverage ratio?

**[4:59]** Confident, instant, and wrong.

## 05:02 — The four missing pieces: definitions, identity, plans, memory

**[5:02]** Because four things are missing.

**[5:04]** And across dozens of companies, every one of them multiplies.

**[5:08]** The first is shared definitions.

**[5:11]** Every client defines pipeline, qualified, and churned just a little bit differently.

**[5:16]** And raw AI blends them all into mush.

**[5:19]** Identity resolution.

**[5:21]** Billing says this account churned, the CRM says it's wide open.

**[5:24]** Is this the same account across all three systems?

**[5:27]** And then plans.

**[5:29]** What is the actual target each number is measured against?

**[5:33]** Finally is memory.

**[5:35]** The context that makes it this client's answer and not a generic one.

**[5:39]** At one company, it's annoying.

**[5:41]** At 30 companies, it's chaos.

**[5:43]** If we ran just on raw AI, we would be shipping confident, wrong answers to 30 different boards.

**[5:49]** That is not a business. That's a liability.

**[5:52]** So here is how it's actually built.

**[5:54]** The thing I told you to stay for and the part I am genuinely proud of.

## 05:58 — The context graph, built on Vasco

**[5:58]** The foundation is the context graph.

**[6:00]** And for that we use Vasco.

**[6:02]** It is a semantic layer that maps every client's raw data to what it actually means.

**[6:07]** Their definitions, their motion, their plan, and resolves every record to one source of truth across CRM, billing, and product.

**[6:17]** That is the layer that makes the answers trustworthy.

**[6:20]** And then on top of it, we build the skills, the plugins, the workflows, the interfaces our clients actually use day to day.

## 06:28 — A foundation that is true, and the operation on top

**[6:28]** So think of it this way.

**[6:30]** Vasco gives us a foundation that is true.

**[6:33]** We build the operation that runs on top of it.

**[6:36]** A trustworthy context graph plus the operations built on top of it.

**[6:40]** Now that is AI ops.

**[6:42]** That is the thing that lets a small team run go to market for an entire portfolio without it falling apart.

**[6:49]** So whether you're a SaaS company that wants this for yourself, an agency trying to scale past the hours for dollar trap,

**[6:55]** or a fund that wants this across your entire portfolio, the mechanics are the same and they're real.

**[7:01]** And the place to start is the same and honest assessment of where your foundation is today.

**[7:07]** Link is in the description below.

**[7:10]** Now, if you want a deeper dive on all of this, Guillaume from Vasco and I are doing a session on what it actually takes to become an AI native agency.

**[7:18]** That one is linked below.

**[7:19]** Feel free to register now.

**[7:21]** And if you want to see what this looks like for a single SaaS company instead of a whole portfolio,

**[7:26]** I have a separate video that walks through exactly that.

**[7:29]** It'll be in the description as well.

**[7:31]** I'm Jake from Lean Scale.

**[7:33]** Thank you so much for watching and we'll see you in the next one.