Proof first. The rest is
a conversation.

A mid-size private bank had a dozen AI pilots running in parallel and nothing in production. Each business unit was buying its own tools, model spend was climbing, and the board wanted a single answer to one question: where does AI actually pay off here, and in what order.

We ran a value map across the operating model, ranked the use cases by return and feasibility, and killed the ones that were novelty. Then we stood up the delivery discipline behind the top three: clear owners, a shared model-governance layer, and a shipping cadence the teams could hold without us in the room.

A private equity fund was mid-process on a growth-stage SaaS asset. The revenue looked clean, but the deal team needed to know whether the technology and the engineering org could carry the growth plan the model was pricing in, and they needed the read inside a live timeline.

A two-week diligence sprint. We assessed the architecture against the next scale phase, read the codebase for the risks that move a valuation rather than a maturity score, and interviewed the engineering leadership to test whether the team behind the plan could actually execute it.

A venture-backed scale-up had tripled headcount in a year and watched its shipping velocity fall. More engineers, less output. Leadership could feel the org clogging but could not see where, and hiring more people was making it worse.

We mapped the team topology against the actual flow of work, found the dependencies that were serializing everything, and reshaped ownership so teams could ship without waiting on each other. The same playbook used to lead 500-plus engineers at Adidas, cut down to their stage.