If you cannot measure it,
you cannot maintain it.

Prompt changes are deceptively risky. An edit that fixes the case in front of you frequently breaks two you are not currently looking at, and without an evaluation set the only feedback is a user complaint weeks later. That is not a development process; it is a slow-motion guess.

An evaluation harness converts that into engineering. A set of real cases with expected behaviour, run on every change, so an improvement is demonstrated rather than asserted. It starts small — a couple of dozen cases drawn from actual usage — and grows every time something fails in production, which is how the same defect stops recurring.

Model choice is deliberately not a permanent commitment. We build behind a provider-agnostic layer so moving between models is a configuration change rather than a rewrite, because capability and pricing in this market shift faster than any build cycle you can plan around.

Cost is instrumented from the start. Inference scales with usage in a way most budgets are not written to expect, and cost per conversation visible in week one is considerably better than a surprise on a provider invoice in month three. We pass inference through at provider rate without markup.

What you get

• Model selection against latency, quality and cost constraints
• Prompt and tool interface design
• Evaluation set built from real cases, run on every change
• Provider-agnostic layer so models can be swapped
• Per-conversation cost monitoring and alerting

Not included

• Model inference costs, billed at provider rate
• Training bespoke foundation models