Document processing at scale

Document processing at scale

Manual document extraction is one of the largest operational costs in document-heavy functions. The agent that extracts structured data with confidence scores and human validation flows replaces the manual work while maintaining accuracy and auditability.

The workflow

1. The document arrives in the document management system: a contract, an invoice, a claim, a regulatory filing.
2. The agent reads the document using document intelligence: OCR if needed, layout understanding, table extraction, clause identification.
3. The agent extracts the structured data into the target schema: parties, amounts, dates, clauses, line items, exceptions.
4. The extraction is scored for confidence. High-confidence extractions are auto-approved and routed to the destination system. Low-confidence extractions are routed to a human for review.
5. The human reviews the low-confidence extraction, confirms or corrects, and approves. The agent learns from the correction.
6. The extraction is logged with the source document, the model version, the confidence score, the destination, and the human decision.

What this looks like in production

The agent operates inside the existing document and workflow stack. The agent does not introduce a new system; it accelerates the existing process.

The confidence threshold is calibrated to the function. For high-volume, low-risk extraction, the threshold is high and the human review is reserved for genuine exceptions. For high-risk extraction (legal clauses, regulated filings), the threshold is low and the human review is the rule.

The agent’s accuracy is measured continuously. The model is retrained when accuracy drops. The human review queue is monitored for patterns that suggest the threshold needs adjustment.

What good looks like

• Document processing cost reduced by 60-80% for high-volume extraction.
• Extraction accuracy maintained or improved, measured against human-reviewed samples.
• Cycle time from document receipt to structured data in the destination system reduced from days to hours.
• Human reviewer time focused on the genuine exceptions, not the routine extraction.