Hallucination Detector

How It Works: 1. Input Analysis: Send AI responses to our verification engine 2. Fact Valuation: Our system evaluates each claim against reliable sources 3. Weighted Scoring: Claims are scored using risk weights and confidence intervals 4. Statistical Assessment: Results include confidence intervals and significance testing 5. Performance Tracking: AI model statistics are updated for continuous improvement Technical Innovation: Dual-Axis Scoring: Combines intensity scores (error magnitude) with risk scores (error severity) Statistical Accumulation: Unlike traditional ML, our system learns through statistical pattern accumulation Risk Weight Constants: SUPPORTED (1.0), NOT_SUPPORTED (1.3), CONTRADICTORY (2.0) Trust Weight Dampening: 0.35 factor to reduce false positive rates Moving Averages: Dynamic performance metrics that adapt over time Why This Matters: Combat the growing problem of AI misinformation Provide users with confidence in AI responses Enable responsible AI deployment in critical applications Support fact-checking organizations and researchers Promote AI transparency and accountability Market Impact: Journalism and media fact-checking Educational technology and learning platforms Business intelligence and decision support systems Legal and regulatory compliance applications Healthcare and medical information verification Built with modern web technologies and deployed on edge computing infrastructure for real-time performance