Capabilities

AI has executive attention and no owner, or your engineering team is fully consumed by the existing roadmap.

One or more engineers work inside your environment and your standups. They're expected to talk to stakeholders, decide what's worth building, build it, and put it in front of users. Not staff augmentation — the engineer owns the outcome, including the part where someone has to be convinced to change how they work.

Systems running in production, documentation, and your team trained to extend them.

Access to the people doing the work, a decision-maker who can unblock in days, and an honest definition of what "better" means.

You need proof before committing budget or headcount — and the function has enough volume that experiments produce real signal fast. Usually go-to-market.

Ideate → build → observe → keep what works → kill what doesn't. Not a brainstorm; a Lab produces live experiments with measured outcomes. Everything that survives gets hardened into infrastructure you keep.

A set of experiments that actually ran, the results, and working systems behind the winners.

AI capability inside the company is wildly uneven — a few people are far ahead and most have used a chatbot a handful of times.

Hands-on from the start. People build something small against a problem they personally have. We're there to unstick them and to notice which problems keep coming up.

People who have shipped something, plus a ranked list of the opportunities they surfaced. That list is usually the most valuable output — the room tells us more about where AI belongs than any assessment would.