Most companies ship RAG systems without measuring them. We audit your retrieval pipeline and tell you exactly where it's failing — with metrics, not opinions.
Most RAG failures aren't model failures. They're retrieval failures — invisible, unmeasured, and silently degrading your AI product.
Your AI confidently answers with wrong information because retrieval returned irrelevant chunks that the model couldn't verify against.
Long documents confuse your pipeline. Critical information gets buried in the middle of long contexts and never surfaces in the final answer.
You have no retrieval metrics to know if your RAG pipeline is improving, degrading, or working at all after each deployment.
No lengthy onboarding. No code access required. Results in 7 business days.
Share your RAG architecture, sample documents, and endpoints. We handle the rest — no engineering time required from your team.
We test using Precision@k, MRR, NDCG, faithfulness and relevance scoring via RAGAS and DeepEval against your real-world query patterns.
A clear, actionable PDF with scores, failure points, and prioritized fixes — ranked by impact so your team knows exactly what to fix first.
The same metrics used by AI research teams at Google, Meta, and Microsoft — applied to your production system.
No retainers. No surprises. A single, defined engagement with clear deliverables.