System
AI output verification
Find out whether what the AI produced is true, before anyone relies on it.
ProofOne pass over 190 records caught 19 the automated filter had passed as clean.
The problem
AI output looks finished whether it’s right or wrong, and a rule-based check will pass a confident mistake.
The outcome
Wrong answers get caught before anyone relies on them. One review caught 19 of 190 records the first filter had passed as clean.
AI output looks finished whether it’s right or wrong. So we don’t take its word for it.
For anything that matters, a separate set of agents goes back to the source and checks each claim. In one pass over a 190-record research set, 113 were confirmed against a fetched source and 19 were reclassified. That’s 10% of the batch, all passed as clean by a rule-based filter.
Auditors overreach too. So a different agent re-verifies every finding before anything changes.
It’s a separate job from the release gates on software. Those decide whether code ships. This decides whether a fact is a fact.
It travels to any team using AI for research, classification or analysis, where a confident wrong answer costs more than a slow right one.
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