Financial Document Intelligence —
10 TB/week at 99.9% uptime

The situation

A US financial analytics platform needed to query proprietary market data and third-party financial feeds daily — with answers that could be cited, audited, and defended under compliance requirements. The previous approach produced hallucinated answers on complex financial documents. One wrong answer in front of a compliance officer would have been catastrophic.

The data itself was the core challenge: 10+ TB of unstructured market data per week, mixed formats, scanned filings, third-party feeds with inconsistent schemas. Most RAG systems would have collapsed at the ingestion stage. This one had to stay running at 99.9% uptime under continuous production load.

What we built

• Hybrid retrieval pipeline (dense vector + BM25 sparse) to handle both semantic and exact-match queries on financial terminology
• Confidence threshold system with safe-fallback routing — the system refuses to answer rather than hallucinate on low-certainty queries
• RBAC at the retrieval layer with per-user metadata filtering, so analysts only retrieve documents their permissions allow
• Golden test suite of 200+ financial query/answer pairs running on every deploy as a quality gate
• AWS Airflow orchestration with Terraform-managed S3 lifecycle policies, automated tiering, and $20K/year cost reduction
• Citation tracing on every response — every answer links back to the exact source document and passage

System architecture

Every layer of the pipeline was designed with compliance and scale in mind — from ingestion to the citation-traced response delivered to the analyst.

Why it required specialized architecture

Standard RAG implementations fail on financial data for three reasons. First, financial queries mix semantic intent ("what was Q3 performance for energy sector holdings") with exact lookup ("what is ISIN US38141GXX52"). Pure vector search misses exact matches; pure keyword search misses semantics. You need both.

Second, in a multi-analyst environment, document-level access control at the API layer isn't enough. A semantically similar query from an unauthorized analyst can surface restricted documents if RBAC isn't enforced at the retrieval layer itself — before the vector search runs.

Third, financial AI that produces hallucinated answers isn't just an accuracy problem — it's a compliance event. Every output needed to be traceable to a source, confidence-scored, and auditable. That infrastructure has to be built from the start, not retrofitted.