Secure AI Research Platform for a State Audit Organization
A state audit organization needed a secure AI-powered research platform to help auditors work across published audit reports, laws, regulations, and internal data — with every answer independently verifiable.
Answers auditors could trust, not just an LLM interface.
Auditors needed answers they could independently verify, with material claims traceable to the correct source and page. Retrieval had to stay reliable across years of inconsistently structured documents, and the platform needed to become a reusable enterprise foundation.
Hybrid retrieval, built as a core requirement.
We designed the end-to-end data, retrieval and AI architecture for the Audit Research Tool (ART), implementing hybrid retrieval that combines semantic vector search, lexical search and cross-encoder reranking to improve context relevance.
Verifiable by design, and built to evaluate itself.
We made citation traceability a core architectural requirement, introduced a deterministic evaluation framework to catch retrieval failures, and applied a two-pass metadata extraction process with programmatic guardrails across 1,500+ historical reports.
04 ACCELERATED ROI
A reusable AI foundation, not a one-off application.
Concrete outcomes from the engagement, not projections.
Trust
Every material claim resolves back to its underlying source document and page.
ACCURACY
Hybrid semantic, lexical and reranked retrieval improved context relevance across the corpus.
SCALE
Consistent metadata extraction applied across 1,500+ historical audit reports.
REUSABILITY
Built for enterprise reuse — model abstraction and secure identity support future AI applications.
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