Find out if the AI idea works, in weeks not quarters.

Most AI projects stall because nobody agreed what success looks like before the building started. A proof of concept fixes the scope, the price, and the date, and answers one question: is this worth building properly?

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How I work

Clear work, simple handoff, no drama.

Fix the scope

We agree what the proof of concept must show, and what it will deliberately ignore. One narrow slice, built against your real data.

Build it

A working feature, not a slide deck. Connected to your data, with the interface and permissions it needs to be used.

Say what we learned

An eval set, the cost per request at real volume, and an honest read on whether to continue. Concluding you should not build it is a valid outcome.

Outcomes

What should improve.

A working proof of concept
An eval set you can trust
Real cost and latency numbers
A go or no-go you can defend

Next useful pages

Pick the page that is closest to your current problem.

Fractional CTOIf the direction needs owning past the proof of concept.AI product engineeringIf the answer is yes and it needs building properly.

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