RAG demos fail: hidden retrieval & metadata bugs — automated RAG debugger

RAG demos fail: hidden retrieval & metadata bugs — automated RAG debugger targets a $12.0B = 200K AI-aware organizations x $60K ACV (enterprise AI dev & ops tooling) total addressable market with medium saturation and a year-over-year growth rate of 35%+ growth driven by LLM adoption and observability expansion.

Key trends driving demand: RAG adoption -- companies increasingly augment LLMs with private data, creating brittle retrieval surfaces that need tooling.; Vector DB commoditization -- standardized APIs make it easier to ship cross-DB instrumentation and integrations quickly.; Explainability & compliance -- procurement teams demand provenance and audit trails for model results.; Shift-left AI quality -- developers want CI/CD and automated tests for models and retrieval pipelines similar to software testing..