Stack Vault's Stack Verify continuously scores retrieval relevance, citation faithfulness, and answer groundedness — flagging drift the moment it starts.
RAG breaks silently. By the time users complain, the chunking has been bad for weeks.
Live LLM-as-judge scoring of whether returned chunks actually answer the user's intent.
Verify every claim in the answer is grounded in retrieved context — flag fabricated citations.
Embedding-distribution monitoring catches model upgrades, corpus changes, and chunker regressions.
Failed queries auto-replayed against eval set. Regressions blocked at deploy.
Weekly sampled review queues for SMEs. No more flying blind on niche domains.
Multi-tenant deployments scored per index, per locale, per content type independently.
Straightforward answers about scope, integration, data handling, and rollout.
Yes — LangChain, LlamaIndex, Haystack, custom. We instrument at the retrieval and generation boundaries.
Reference-free scoring (groundedness, faithfulness, context relevance) plus weekly SME review queues for calibration.
Yes. Bring your own judge model, or use our default ensemble. Multi-judge consensus reduces single-model bias.
Scoring runs out-of-band on a sampled tail. Zero added latency to user-facing requests.