WhatA testing and monitoring platform that continuously audits LLM products for context faithfulness — detecting when models silently lose context, hallucinate about document contents, or confabulate about their own capabilities.
SignalUsers report that even the theoretically best-benchmarked models (Gemini) are unusable in practice because they silently forget context and lie about having read documents — the gap between benchmark scores and real reliability is a major unserved pain point.
Why NowAs enterprises move LLMs into production workflows involving long documents and multi-turn conversations, silent context failures become business-critical bugs rather than mere annoyances.
MarketEnterprise AI teams and LLM-powered product companies; $50K-500K/yr contracts; no dominant player in LLM reliability monitoring — existing eval tools focus on accuracy, not faithfulness and context retention.
MoatProprietary failure taxonomy and detection models trained on thousands of real-world context failure modes across providers, creating a unique dataset competitors can't easily replicate.
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