AI Lies. We Catch It.
AI models hallucinate 15–20% of the time. Cross-model verification exposes fabricated claims.
What Is an AI Hallucination?
An AI hallucination is when a model generates confident-sounding information that is factually incorrect, fabricated, or unsupported.
Unlike a human mistake, AI hallucinations sound authoritative. The model doesn't hedge or express uncertainty — it states false information with the same confidence as true information. This makes hallucinations particularly dangerous for anyone relying on AI for research, decisions, or fact-checking.
How We Detect It
Cross-referencing independent AI models reveals claims that don't hold up.
Contradicted Claims
When one model states a "fact" that other models directly contradict, it's flagged as disputed with a red confidence marker.
Unmentioned Claims
When only one model mentions a claim and others don't address it at all, it's marked as uncertain — a potential fabrication.
Verified Claims
When 3–4 models independently agree on a claim, it's marked as verified — highly unlikely to be a hallucination.
Example: Catching a Hallucination
A simple factual question reveals how cross-model comparison catches errors.
Question
"When was the Golden Gate Bridge completed?"
Verdict: Disputed — contradiction detected
3 of 4 models agree: 1937. Gemini's answer of 1935 is flagged as disputed — the hallucination is caught.
How Often Does Your AI Hallucinate?
Different models hallucinate at different rates, and on different topics. NoParrot tracks agreement patterns across thousands of queries.
Check the scoreboard to see how your preferred AI model stacks up against the others.
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Ask a question and see which claims hold up across four independent AI models — and which ones don't.
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