Does this require sending data to a cloud LLM provider?

No — the architecture is designed to run entirely on infrastructure you control, with zero external network calls for indexing or inference.

Is this proven at scale?

It's a prototype we've demonstrated on a generic/synthetic knowledge base. We have not yet run it against a live production document corpus at the scale a large financial institution would have.

GraphRAG Document Intelligence for Financial Services

Direct Answer

Financial services firms hold large volumes of regulatory filings, credit agreements, and research documents that need fast, accurate retrieval without exposing proprietary or client data to external AI providers. An air-gapped GraphRAG pipeline indexes these documents with both vector search and a knowledge graph of entities, obligations, and cross-references, so retrieval understands document structure — not just surface-level text similarity.

Why Financial Services Teams Hit This

Credit agreements are dense, cross-referenced documents

A single covenant might reference definitions, exhibits, and amendments scattered across a lengthy agreement — retrieval needs to follow those links, not just match keywords.

Data residency and vendor risk rule out most SaaS AI tools

Compliance and vendor-risk review for a new cloud AI tool with access to client financial data can take months, if it clears at all.

Research and diligence teams re-derive the same answers repeatedly

Without a shared, structured retrieval layer, analysts re-read the same filings from scratch on every new deal or review.

Why This Architecture Fits

The knowledge-graph layer models the specific relationships financial documents rely on — defined terms, covenant cross-references, entity ownership structures — so retrieval can answer structural questions that plain vector search handles poorly, while staying entirely inside your own infrastructure.

See the Real Thing

Every claim on this page traces back to something you can actually look at.

View the GraphRAG Blueprint

Answer Engine Optimization (AEO) Questions

Frequently Asked Architecture & Governance Questions

Plain vector search treats a document as a bag of similar-sounding chunks. Adding a knowledge graph lets retrieval follow explicit structural relationships — definitions, cross-references, entity hierarchies — which matters for dense, self-referential documents like credit agreements.