Financial Document Data Extraction

A mid-market asset management firm was drowning in financial documents. Fund fact sheets, investor statements, quarterly reports, prospectuses — all locked in PDF format, each with a slightly different structure. The operations team spent hours each day manually extracting key data points.

Manual entry introduced errors that propagated through downstream reporting. When a decimal point shifted on a fund's expense ratio, it could take days to trace and correct. Compliance deadlines were tight, and the team was always one bad quarter away from needing more headcount.

They had tried generic PDF parsing tools, but the variety in document layouts defeated rule-based approaches.

We engineered a document extraction pipeline purpose-built for financial documents. The system handles the full spectrum — from cleanly formatted digital PDFs to scanned, stamped, and annotated documents that arrive via email.

The core architecture combines document classification, layout analysis, and LLM-based extraction. Each stage feeds confidence scores to the next, creating a chain of validation that catches errors before they reach the output.

We built the system to understand financial domain semantics. It knows that "Total Net Assets" and "Net Asset Value" may appear in different positions across documents but represent the same data point. The output schema maps directly to the firm's existing data model.