Agents are taking on real economic work.
As runs get longer, mistakes become expensive at best, catastrophic at worst.
Today, teams find out from user complaints. Or never.
Monitoring tools only catch what you told them to watch for: you set up the judges, you trace the issues yourself. But agents will always fail in ways you didn't predict.
That's why we built Currai
Currai understands what your agent is supposed to do, so it surfaces failures you didn’t know to look for, automatically.
You shipped your LLM app — now you're debugging it with print(). You can't see what the model actually received, why it answered the way it did, or what each call cost. Currai fixes that: it traces every prompt, token, and tool call in one view, so you can debug and track spend with confidence — and A/B test prompt versions on live traffic to see which one actually wins. Drop in the Python or TypeScript SDK and your first trace lands in ~5 minutes, with zero infrastructure to run.
SkinEat focuses specifically on the connection between food and skin health, which most food scanner apps don’t address.
Instead of only showing calories or macros, SkinEat analyzes your meal and gives skin-focused insights — helping you understand how certain foods may impact things like breakouts, inflammation, or overall skin health.
