Enterprise Document Processing

A mid-market financial services firm was drowning in paper. Each day, their operations team manually reviewed hundreds of loan applications, compliance documents, contracts, and client correspondence — extracting data, flagging anomalies, and routing documents to the appropriate teams.

The process was slow, error-prone, and consuming 60% of their operations team's working hours. New regulations had increased document complexity, adding new required fields and compliance checks. The team was falling behind.

They had tried rule-based OCR systems, but documents arrived in dozens of formats — some structured, many not. The rule-based system handled the structured ones adequately, but failed entirely on anything non-standard. And in financial services, the edge cases are often the most important documents.

We designed a Claude-powered document intelligence pipeline. The core insight was that Claude's ability to understand document context — not just extract text, but comprehend what the text means — eliminated the need for format-specific rules.

Documents are ingested via a secure upload API, converted to a consistent representation, and processed through a multi-stage Claude pipeline: initial classification, field extraction with confidence scoring, anomaly detection, and routing decision.

The critical design decision was treating Claude as a reasoning engine, not just an extractor. For each document, Claude provides not just extracted values but a structured assessment: what it found, what confidence it has, what looks unusual, and what human review (if any) is warranted.

The pipeline runs on AWS with a Python backend. Documents are preprocessed with a combination of PDF parsing and vision capabilities for scanned documents. Claude handles the intelligence layer — we use claude-3-5-sonnet for standard documents and escalate to claude-3-opus for complex cases requiring deeper reasoning.

We designed a two-pass architecture: a fast first pass classifies the document and extracts high-confidence fields, a second pass focuses on uncertain elements and performs compliance checking. This keeps average latency under 8 seconds while ensuring thorough analysis.