Fact-check AI output with AI that checks itself
Every AI hallucinates. Synero fights this by querying four models from different labs and surfacing where they converge — and where they contradict each other.
AI hallucinations are the default, not the exception
- All frontier models produce plausible-sounding claims that are factually wrong
- Models are especially unreliable with statistics, dates, citations, and obscure facts
- A single model cannot reliably detect its own hallucinations
- The only systematic defense is cross-model verification — checking one model's output against others
Example Prompt
“What percentage of startups fail within their first five years? Cite the source of your data.”
Where models agree
- All models cite a range between 50% and 90% depending on the definition of 'failure'
- All reference Bureau of Labor Statistics data as the most authoritative source
- All note that survival rates vary dramatically by industry
Where models disagree
- GPT cites a specific '90% failure rate' that Claude flags as misleading — the BLS data actually shows about 50% closure within 5 years
- Gemini adds context that 'closure' doesn't always mean 'failure' — many businesses close for non-financial reasons
The synthesis
The synthesis distinguishes between closure rates (~50% in 5 years per BLS) and the widely-cited '90% failure rate' which conflates closure with failure and uses broader timeframes. This distinction only emerged because the models challenged each other.
Frequently asked questions
Can Synero detect all hallucinations?
No tool can catch every hallucination. But when four models from different architectures and training data agree on a fact, the probability of a shared hallucination is much lower than with a single model. When they disagree, Synero flags the disagreement so you know where to verify further.
Is this useful for academic research?
Yes. Researchers use Synero to cross-check claims, verify statistics, and identify areas where the AI literature may be unreliable. The synthesis highlights consensus and flags areas of genuine scholarly disagreement.
How do the advisor roles help with fact-checking?
The Architect focuses on logical structure, the Philosopher questions assumptions, the Explorer brings cross-domain context, and the Maverick challenges the consensus. This diversity of reasoning styles catches different types of errors.