How LeanScale cut diagnostic time by 8x while helping clients go AI-native
LeanScale automated the data work with Vasco's context graph, then used the time it freed to build the 11 agents they now run inside every client's go-to-market — work only their own GTM expertise could design.
The challenge
LeanScale's growth was capped by how many senior people it could hire, and most of their week was going into assembling data rather than interpreting it.
LeanScale is an embedded GTM Ops agency based in Arizona, supporting hypergrowth startups including Clio, Chainguard, Mistral, and Moonvalley. As CTO, Jake Toepel leads the systems and data side of delivery across the client book.
LeanScale calls its senior operators GTM architects. Each one owns a client's go-to-market operations end to end: growth planning, CRM implementation, data cleanup, revenue reporting. The work is senior and the people are expensive, which is exactly what made the reporting load so costly.
Every client arrived with a standing reporting obligation that only a senior person could meet. Close rates, pipeline generation and attainment tracking lived in Excel, assembled into board reports by hand.
And every new engagement opened with archaeology rather than advice.
"The first question we ask is one they can't answer from their own systems." — Jake Toepel, CTO, LeanScale
LeanScale already knew where this was going: scaling past a few dozen clients meant putting more of this work onto AI agents. The first attempt didn't hold up — point a model at raw CRM and billing data and it produces something that looks like a diagnostic, but is usually wrong, because nobody has reconciled what's underneath it. Same bottleneck, one step downstream: now the hours went into fixing data instead of building the deck.
Both problems had the same shape:
- Senior leadership’s time went into assembling reports, not just analysis and advice.
- Eight hours of senior time was spent before a client heard any recommendations.
- Capacity was capped at three to four accounts per architect.
- Automating it directly didn't fix it either; pointing a model straight at a client's data returned answers that were instant, confident and wrong.
Our approach
LeanScale did not start by building agents. They started by encoding their own playbook — the definitions, the plan, the judgment calls that come from running GTM for dozens of companies — into something an agent could read.
First, a context graph per client, built on their playbook. Vasco's context graph and MCP give LeanScale the infrastructure; what goes into it (the definitions, the identity rules, the plan, the institutional memory) is LeanScale's own expertise, specific to each client.
That's the step most teams skip. Ask a raw model for pipeline coverage and it blends expansion with new business, can't tell if Acme Inc. and Acme Co. are the same company, and has no plan or memory to check against. Annoying at one company, but it compounds across a book of them, which is exactly what LeanScale hit the first time they tried it themselves.
"An agent will always give you an answer. Whether it's the right one depends on what's underneath it." — Jake Toepel, CTO, LeanScale
Then, a diagnostic agent on top of it. Connect a new client's data and it runs the full teardown (funnel conversion, stalled deals, rep coverage, dead pipeline) and writes it up against LeanScale's own playbook, in LeanScale's voice, as a finished diagnostic.
Eight hours of senior time became one.
"The consultant is still in the loop, but now they're spending more time advising and strategizing, not just assembling data." — Jake Toepel, CTO, LeanScale
The rest of the delivery model followed the same pattern. QBRs where questions get answered live, in the client's own definitions.
"Every engagement teaches the playbook something. The next client starts further along." — Jake Toepel, CTO, LeanScale
Automate, not audit
Most of what agencies sell is an audit: someone pulls the data together and hands over a deck — accurate on delivery day, stale a month later. LeanScale didn't set out to produce audits faster. They set out to stop selling audits at all.
Because the context graph stays current, the diagnostic agent doesn't run once and disappear: it watches continuously for pipeline stalling, deals going quiet, coverage gaps opening. A one-time deliverable became a standing capability. The infrastructure keeps it current; what it's watching for, and what it does about it, is still LeanScale's playbook. They're LeanScale's expertise, running continuously instead of delivered once a quarter.
And then the part that changed the business. With capacity freed and a context graph already running per client, LeanScale built something they'd never had room for: a squad of 11 agents deployed inside each client's go-to-market, doing the work of making those clients AI-native too.
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.
The results
“Freeing up our architects is what let us build the agent squad we now run for every client.”
8x faster client diagnostics
From eight hours of senior time to one. The architect now edits a draft rather than building one from raw exports, which is a different job and a much shorter one.
Twice the client load per architect
Architects went from three or four accounts to six to eight, with no hiring round. The constraint was never how many clients a person could advise. It was how many they could assemble data for.
11 agents running inside every client's GTM
The recovered capacity did not go into running leaner. It went into building a squad Leanscale now deploys per client.
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