Intelligent Document Processor

A mid-market finance team of 12 people spent their days re-keying data from PDF invoices, scanned contracts, and compliance forms into their ERP. It took an average of 15 minutes per document. Error rates hovered around 4%, good enough to pass most audits, expensive enough to cost real money when it failed. The CFO wanted to repurpose the team toward analysis work, but no off-the-shelf tool could handle the diversity of document formats they processed.

We built a document-processing pipeline that pairs OCR with GPT-4 for structured extraction. Documents drop into AWS S3 via email-to-inbox or direct upload; Celery workers process them asynchronously so burst uploads don't back up the API. GPT-4 extracts the fields that matter (vendor, amount, dates, parties, payment terms) with confidence scores per field. A React review UI shows extracted data next to the source document; operators can approve, correct, or flag. Every correction feeds back into the extraction prompts, so accuracy compounds over time.

• Processing time per document fell 90%: 15 minutes → 90 seconds
• Extraction accuracy climbed from 78% at launch to 96% through the feedback loop
• 12-person manual entry team fully reassigned to analysis and exception handling
• Error rate on the final ERP entries dropped from 4% (manual) to 0.8% (AI + human review)

GPT-4 beat Claude for this narrow task because its structured-output mode was more reliable on deeply nested invoice schemas at the time we built this. Celery workers process documents asynchronously, which matters because finance teams upload in bursts (Friday end-of-week, month-end). AWS S3 with pre-signed URLs keeps document storage secure and scalable. The feedback loop is the quiet win: every correction is a free training signal, and accuracy has gone up every quarter without any model updates on our end.