Engagement
RAG Audit Readiness Assessment
Know exactly what your RAG system gets wrong before your regulator does.
Retrieval-Augmented Generation systems fail in ways that are hard to catch in development: confidence-hallucination mismatch, retrieval gaps on specific query types, context contamination from outdated documents, and silent performance drift after document base updates. An audit readiness assessment surfaces these failure modes on your data before they reach production — or before a compliance review.
This is for you if
- Teams with an existing RAG system in production or late-stage development
- Organizations in regulated industries where AI output accuracy has legal or financial consequences
- Engineering teams preparing for an internal or external audit of their AI systems
- Firms that have had a RAG failure in production and want to understand the root cause
What you get
Failure mode inventory: systematic categorization of how your specific RAG system fails across query types
Calibration analysis: does your system's confidence signal predict accuracy? ECE measurement on your task
Retrieval quality audit: precision and recall on a representative query sample, gap identification
OOD sensitivity test: performance on novel query types, edge cases, and adversarial inputs
Evaluation dataset: 200–500 labeled examples from your domain, reusable for ongoing regression testing
Remediation roadmap: ranked list of interventions by impact, with implementation guidance
Timeline
3–4 weeks from kickoff to final report
Engagement type
Assessment engagement. Requires read access to your system and document base.
Pricing
Fixed-fee assessment. Pricing on the discovery call.
The outcome
A complete picture of your RAG system's failure profile, a reusable evaluation dataset, and a prioritized remediation plan — before these issues surface in production or in an audit.
20-minute call. No commitment. I'll tell you plainly if this engagement is the right fit.