AI Architecture

Retrieval-Augmented Generation That Survives an Audit

August 19, 2026 · 6 min read

Grounding, citation, and evidence retention are what separate a demo chatbot from an AI system a compliance team will actually approve.

Grounding is a control, not a feature

In BFSI, healthcare and public sector work, an answer without a source is a liability. Every generated statement should resolve back to a document, a claim line, or a transaction record that a reviewer can open.

Design rules we hold to

These constraints cost a little accuracy on paper and buy an enormous amount of trust in production.

  • Cite the source record for every material claim
  • Retain the retrieved context alongside the output for review
  • Never let a model invent an identifier, amount, or date
  • Score confidence and route low-confidence cases to humans
  • Version prompts, indexes and models so results are reproducible

Evaluation before expansion

Build a labelled evaluation set from real historical cases before rollout. Precision on the ten questions your team actually asks matters more than a benchmark score.

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