Unlike traditional software that fails loudly with stack traces and error codes, AI systems degrade silently — reasoning quality, semantic correctness, and output reliability shift without triggering any observable signal. There is no standard framework or tooling category for monitoring semantic correctness or detecting quality drift the way infrastructure metrics are monitored for traditional systems. This means production degradation goes undetected until downstream consequences surface.

AI systems degrade silently in production — reasoning quality and output correctness drift without any alert — because no standard monitoring framework exists for semantic correctness the way Datadog exists for infrastructure.

Engineering and ML platform teams at companies running LLM-powered features in production (customer support bots, code assistants, content pipelines) who currently discover quality regressions only via angry user reports.

Teams already pay $50K+/yr for observability (Datadog, Sentry) and are duct-taping eval suites together internally; a purpose-built semantic drift platform with shared community benchmarks converts that DIY pain into a paid category — the 'Datadog for AI quality' positioning has immediate budget-holder resonance.

MVP is an SDK that hooks into LLM call outputs, runs lightweight embedding-distance and LLM-as-judge evals against user-defined golden sets, and fires alerts on drift; the platform layer lets teams publish and subscribe to community eval benchmarks (e.g., 'medical summarization accuracy') creating a two-sided marketplace of eval contributors and consumers.

Observability market is $40B+ and growing; the AI-specific quality monitoring slice is nascent but every company deploying LLMs needs it — conservatively a $2-5B sub-category within 3 years.