Single-model fact-checking has a fundamental flaw: when the model is wrong, there is no independent check to catch it. Triple-engine fact-checking — running the same claim through ChatGPT, Perplexity Sonar Pro, and Google Gemini simultaneously — applies the same adversarial verification logic used in scientific peer review and financial auditing. No single model's hallucination survives intact when three independent systems disagree.
How Each Engine Works Differently
ChatGPT (GPT-4o) uses parametric knowledge — everything encoded in its weights during training — plus real-time web search when enabled. It excels at synthesising complex reasoning chains. Perplexity Sonar Pro is a retrieval-first system: it fetches live web pages, indexes them in real time, and grounds its answers in explicit citations. It is strongest for current events and verifiable claims. Google Gemini 2.5 combines Google's web index (the world's largest) with multimodal reasoning, making it especially strong on claims involving images, documents, and cross-language sources.
Consensus Scoring
Omniscient AI's multi-engine fact-checker aggregates verdicts from all three engines into a consensus score. When all three engines agree (e.g., all three return "False" with independent citations), the consensus confidence is highest. When they disagree, the disagreement itself is informative: it signals a claim that is contested, ambiguous, or rapidly evolving — all of which require human editorial judgement rather than automated verdict.