Truth, engineered

AI models are good at reading and reasoning, and every one of them gets things wrong with confidence. Ask one model to fact-check a document and you inherit its blind spots, its training-data cutoff, and its particular way of hallucinating. Publishers, researchers, and teams that publish at volume need more than one model's opinion.

So we treat verification as an engineering problem. Evidence is gathered before any model rules, and every citation is checked against what the source says. Disagreement stays on the record. Every verdict keeps an audit trail back to its evidence. We measure our accuracy on public benchmarks and publish it. And the price is quoted up front, per claim, before any work runs.

Stamp AI, the default, searches the live web for every claim in your document and for any correction or denial of it, screens the results, and rules on that evidence alone, in seconds. For claims that must survive scrutiny, the Research Panel has models from different providers research each claim independently while a challenger and a separate judge weigh their votes into one verdict. Our methodology page documents the full pipeline, its known limitations, and how to read the scores.

Fact Engineering is built by Fact Engineering Inc. Questions, feedback, or press: reach us at support@fact.engineering or through the contact form.