Most teams change a prompt, eyeball a few outputs, and ship. When quality slips two weeks later, nobody can point at the change that caused it. I build the evaluation layer that turns a prompt or model change into a measurable decision instead of a guess.

A golden set built from your real traffic — the cases that actually matter, with the expected behaviour written down rather than held in someone's head.

A replay loop that runs any prompt, model or provider change against that set and reports the diff: what improved, what regressed, what stayed flat.

Typed failures, so a bad call surfaces as an explicit error instead of a fabricated result that reads fine.

Schema validation on structured output, plus a human approval step before anything changes production behaviour.

A hash-chained run record, so any number in a report can be traced back to the exact inputs that produced it.

Provider-neutral by construction: every call goes through a thin adapter layer, so adding or swapping a provider is configuration rather than a rewrite. I have run it against a CLI provider; an HTTP or SDK adapter is small work I scope up front.

A first engagement usually runs one to two weeks: we go through your real traffic, agree on what "better" actually means, build the first golden set together, and hand back a harness your own team runs on every change. That agreement is normally the highest-leverage hour of the whole engagement.