Triall

Triall is an AI hallucination-detection platform that pits three frontier AI models against each other in a structured blind peer-review process. Before any model answers, Triall analyzes the question for hidden assumptions and failure risks. After independent answers are generated, each model reviews the others anonymously, and surviving answers are stress-tested by an adversarial critic and verified against live web sources.

• Pre-Analysis — Triall classifies your question, identifies hidden assumptions, and flags what's most likely to go wrong before any model responds.
• Independent Council — Three models from different AI providers (including Claude, Gemini, and Grok) answer in parallel; architectural diversity surfaces different failure patterns.
• Anonymous Peer Review — Each model reviews the others' responses blind, explicitly checking for over-compliance, false confidence, and fabricated details.
• Web Search Integration — Real-time web results are pulled before models start answering so they work with current information, not stale training data.
• Convergence Analysis — When all three models agree without providing evidence, Triall flags it as a correlated hallucination risk — the most dangerous kind.
• Adversarial Refinement — The best answer is attacked by a critic model and refined iteratively; each loop makes the answer harder to break.
• Anti-Sycophancy Detection — A background over-compliance risk score tracks whether an answer is trying too hard to please, and warns subsequent reviewers.
• Claim Verification — After the loop closes, specific claims are checked against live web sources and marked verified, unverified, or contradicted.
• Devil's Advocate — A final model makes the strongest possible case against the answer, surfacing counterarguments, failure scenarios, and blind spots.
• MCP & Integrations — Triall can be used inside Claude, ChatGPT, or any AI via its integrations page, extending hallucination protection to your existing workflows.