AI models hallucinate. Not occasionally — structurally. It's built into how they work: they generate the most probable next word, not the most accurate answer. This means every response carries a risk of confident, undetectable errors — fabricated sources, wrong numbers, plausible-sounding nonsense.

Anyone who uses AI seriously has hit this wall. You get an answer that looks right, sounds right, and turns out to be wrong — and there was no way to tell without checking it yourself.

We studied the research on cross-model verification and built a method that maximizes the chance of catching these errors automatically. Multiple models analyze the same question independently, then challenge each other's reasoning. It's not a vote — it's a stress test. The result is a trust score grounded in where models agree, where they disagree, and why.