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Situations
An AI POC that will not reach production
The demo works, everyone has seen it, and yet nothing is live six months later. The problem is rarely the model, it is everything around the model.
What we observe
- The result changes between runs on the same input
- Nobody can say whether the last prompt change improved or degraded the product
- No usable trace when an answer goes wrong
- Cost per request has never been measured at real volume
What usually blocks
A POC proves something is possible. Going to production proves it is reproducible, measurable and repairable, which is entirely different work.
The tipping point is almost always the same: there is no evaluation set. Without one, no change can be validated, so nobody dares touch the system, so it never ships.
What we do
- 01We freeze an evaluation set from real cases, including the ones that failed
- 02We version prompts and trace every call with its cost
- 03We set thresholds you can hold on latency and on the share of accepted answers
- 04We wire human supervision where an error is expensive, and nowhere else
The method in full
The reference guide on the subject, with its definition, its sources and its guardrails.
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