Intelligent Document Processor.
AI extraction from invoices and contracts — 12-person team reassigned to analysis work
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.
Outcomes that mattered.
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)
Technology decisions.
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 — critical 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.
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