An AI proof of concept is a small working system that tests whether an AI approach solves your specific problem on your real data. A well-scoped one takes two to six weeks and ends with measured results and a proceed/pivot/stop recommendation - not just a demo.

Typically a small fraction of the production build it de-risks. The economics are the point: spending a few percent of the potential project cost to discover whether the other ninety-plus percent is worth spending is the cheapest decision-quality upgrade available.

LLM evaluation measures a language-model system's accuracy, safety, latency, and cost on your specific tasks, using test sets built from your real scenarios. You need it because public benchmarks don't predict your use case - and because every model update or prompt change can silently change behavior.

You stop - having spent weeks instead of quarters finding out. We document why it failed (data gaps, accuracy ceiling, cost) and what would need to change, which frequently redirects the budget to an adjacent idea that will work.

Yes. Independent evaluation of third-party systems - accuracy testing, safety probing, cost analysis - is a common engagement, especially before contract renewals or expansions. We test the average case, not the demo case.