RAG that actually answers the question.
STACK Vault's STACK Verify continuously scores retrieval relevance, citation faithfulness, and answer groundedness — flagging drift the moment it starts.
What we measure, in real time
RAG breaks silently. By the time users complain, the chunking has been bad for weeks.
Retrieval Precision
Live LLM-as-judge scoring of whether returned chunks actually answer the user's intent.
Citation Faithfulness
Verify every claim in the answer is grounded in retrieved context — flag fabricated citations.
Drift Detection
Embedding-distribution monitoring catches model upgrades, corpus changes, and chunker regressions.
Failure Replay
Failed queries auto-replayed against eval set. Regressions blocked at deploy.
Human Eval Loop
Weekly sampled review queues for SMEs. No more flying blind on niche domains.
Per-Index Scoring
Multi-tenant deployments scored per index, per locale, per content type independently.
Questions teams ask before deploying
Straightforward answers about scope, integration, data handling, and rollout.
Does this work with our existing RAG framework?
Yes — LangChain, LlamaIndex, Haystack, custom. We instrument at the retrieval and generation boundaries.
How do you score without ground truth?
Reference-free scoring (groundedness, faithfulness, context relevance) plus weekly SME review queues for calibration.
Can we use our own eval models?
Yes. Bring your own judge model, or use our default ensemble. Multi-judge consensus reduces single-model bias.
Does this slow down responses?
Scoring runs out-of-band on a sampled tail. Zero added latency to user-facing requests.