Evidence, not demos.

Most AI content answers “what’s possible.” Almost none of it answers “what did this actually cost, and did it work.” usecaseinai exists to close that gap.

It was started by a practitioner with a senior background in the tech and AI industry, after running into the same wall over and over: teams could point to demos, but not to a single production AI system with real numbers attached to it — latency, evaluation pass rate, cost per request, the failure modes it hit along the way. Most “use case” roundups turned out to be marketing copy dressed up as research, and finding genuine business value in AI kept getting harder, not easier, as the noise grew.

usecaseinai is the fix: one source that documents how real AI systems were actually built, industry by industry, so you can leverage it properly — not just talk about AI, but evaluate it like someone who’s seen the inside of a production system. Every entry cites the deployment it’s drawn from, breaks down the decision that made it work, and states the cost. No hype, no vendor pitch — a reference you can actually bring into a room.

It’s written and maintained by one practitioner, not a content team. Corrections, pattern suggestions, and questions are always welcome — see /contact.