Reducing document processing time with custom RAG
We built a retrieval-augmented generation system that ingests thousands of financial documents and lets compliance teams query them in natural language, cutting processing time dramatically.
Where they started.
Global Finance Corp's compliance team was drowning in document review. With over 12,000 policy documents, regulatory filings, and internal guidelines to reference on every major transaction, analysts spent more time searching and reading than analyzing. Manual keyword search missed critical context, and critical details were buried in PDFs that took hours to locate.
How we solved it.
We conducted a discovery phase to map document types, access patterns, and compliance workflows. We identified the top 20% of documents that drove 80% of lookups.
We designed a RAG architecture with semantic chunking tailored to financial documents, respecting section boundaries, tables, and regulatory boilerplate that generic chunkers break.
We deployed a hybrid search pipeline: dense embeddings for semantic similarity plus a keyword fallback for regulatory codes and exact citations. A cross-encoder re-ranker improved precision on the top results.
We built a natural language interface that lets analysts ask questions like "What are the capital reserve requirements for cross-border M&A?" and receive cited answers in seconds.
We integrated with their existing document management system and SSO, ensuring audit trails for every query and no data exfiltration.
The outcome.
Compliance analysts reduced document lookup time by 85% on average, from 45 minutes to under 7 minutes per inquiry.
12,000+ documents indexed and searchable with sub-2-second response times for complex queries.
Zero compliance incidents during rollout; full audit logs satisfied regulators.
The system has since been expanded to support legal and risk teams with the same architecture.
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