Almost nobody building AI today is an engineer.
Every eval tool on the market was built for the ten percent who write code. We spent a decade in AI risk before we noticed that. It changed what we built.
We started as risk people, not software people.
Before LuminosAI, we spent a decade helping companies manage AI risk, work that used to fall to legal, privacy, and compliance teams. They weren't engineers either. But they had to answer the same question every AI builder faces now: will this thing cause trouble?
Some of the places we've been.
Then AI stopped being an engineering thing.
Product managers, ops teams, marketers, almost everyone is building AI features now, often with no code at all. It's not a fringe trend. It's becoming the default way AI gets built inside a company.
But nobody built them a way to test it.
Every eval tool assumes you can write code. So most builders skip straight to asking one AI to grade another. That doesn't work. An eval isn't code, it's a judgment call about what failure looks like, and that call has to be right the first time.
“The people shipping the most AI have the least way to find out if it's safe.”
So we built the eval for everyone else.
Everything we'd learned about turning risk into a real test, we kept. Everything that required an engineer, we removed. Describe your system, hand us your data, and we build and run the eval ourselves.
Building AI got easy. Evaluating it didn't. That's the gap we closed.
You build the agent.We build the eval.
However you built it, whatever you built it in, if you're shipping AI, this is for you.