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
How Synero works
1Ask your question
Your question goes to GPT, Claude, Gemini and Grok. Each answers on its own.
2Get one answer
Synero writes one combined answer from the best of all four.
3See where they split
Agreement is a good sign. Disagreement tells you what to check.
Example question
“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 consensus answer
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.
Which preset to use
Solo
Duel
Council
Deep
Deep: up to four flagship models (GPT-6.1 Sol, Claude Opus 5.5, Gemini 3.1 Pro, Grok 4.7). Use it when a wrong answer is expensive. You can also pick 1 to 4 models yourself from 20 across GPT, Claude, Gemini, and Grok.
See it in 30 seconds
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 does asking several models help with fact-checking?
Different models have different training data and blind spots, so a claim that one model gets wrong is often caught by another. The agreement panel marks which points every model supports and which only one does, which is where errors tend to hide.
Make AI check its own work
Up to four models. One consensus answer. Check where they split before you act.
Start free3 free questions, no card. Then $10/month with $10 of usage included, or pay as you go with credit packs from $5, no subscription. Private by default, never used for training. See pricing