Why we built this.

The same scenario plays out across financial services, healthcare, and legal. An AI system produces a plausible-sounding but factually wrong output. It passes through a human review layer that was not equipped to catch it. It reaches a decision. Sometimes the consequence is a compliance violation. Sometimes a treatment recommendation that contradicts current guidance. Sometimes a contract clause that does not reflect the jurisdiction it purports to cover.

The industry response has been to ask humans to review AI output more carefully — to slow down and double-check. That is not scalable. An analyst processing two hundred AI-assisted research summaries per day cannot independently verify every factual claim in each one. The volume of AI output is now larger than the human capacity to verify it manually.

The correct solution is verification at the machine level. Before an AI response reaches a human or a downstream decision system, it should pass through automated verification that checks mathematical correctness deterministically, cross-examines factual claims against structured knowledge, and produces a reviewable evidence record. That is what Perathos does.

The verification pipeline is built as a drop-in middleware layer — not a product that requires re-architecting your AI stack. The evidence trail exists whether or not you need it today. The day a regulator or a counterparty asks what your AI said and whether it was supported, the answer can be reviewed from the retained bundle and logs.