Case Study
A go-to-market engine from zero at a Series A AI fintech
PROBLEM
Revenue ran through the founders. There was no defined ICP, no repeatable outbound, and no reliable way to see
what was working. The company needed the commercial operating system built fast, without the nine-month delay
and cost of hiring a full-time revenue leader.
Strategy
What I built
ICP & segmentation Defined the ideal customer across five verticals with qualification criteria that separated high-value, fast-closing segments from noise. Built channel separation between self-serve and enterprise tracks.
Pipeline & CRM Stood up HubSpot as a real system of record: deal stages with exit criteria, velocity reporting, win/loss tracking, and full-funnel visibility. A pipeline leadership could inspect and forecast.
Attribution Diagnosed a broken attribution pipeline and rebuilt it with engineering through a proper data layer, establishing reliable funnel measurement for the first time.
Outbound & enablement Built outbound playbooks and cadence frameworks; coached the team on ICP-validated messaging, objection handling, and call structure.
The Impact
In under six months the company moved from founder-led selling to a measurable, forecastable revenue engine: a defined ICP, a CRM the team trusted, attribution that told the truth, and an outbound motion reps could run without the founders in the room.
The system outlasted the engagement, which is the point of building infrastructure rather than running campaigns.
Why it Transfers?
Different sector, same gap. European sovereign-stack vendors capturing the migration wave face exactly what this fintech did: real demand, no commercial machine to catch it. Systems thinking transfers even when sales cultures don't. If your product has earned demand your go-to-market can't yet capture, that's the problem I solve.
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