Overview
Intelligent document processing combines OCR, layout understanding and language models to convert documents into structured records. It is what sits between a PDF landing in an inbox and a row appearing in a finance system.
How it works
A pipeline classifies the document type, extracts text and layout, locates the fields that type requires, validates them against business rules, and emits structured output with a confidence score per field so low-confidence values can be reviewed.
Use cases
Accounts payable
Extract supplier, dates, totals and line items and post them for approval.
FinanceContract data extraction
Pull parties, terms, renewal dates and obligations into a register.
LegalOnboarding forms
Read identity and application documents during customer onboarding.
BankingClaims handling
Structure submitted evidence so assessors work from data, not PDFs.
InsuranceBenefits
- Removes manual keying from high-volume document work.
- Handles layout variation that template matching cannot.
- Validates against business rules before data lands downstream.
- Per-field confidence makes human review targeted rather than total.
Limitations
- Unusual layouts still need examples or template help.
- Multi-page tables spanning breaks are frequently mis-parsed.
- Regulated workflows require an audit trail of every correction.
- Accuracy claims quoted by vendors rarely survive contact with real scans.
What to look for when choosing a tool
- Per-field confidence scores, not just a document score
- Human-in-the-loop review queue built in
- Validation rules and downstream system connectors
- Audit trail of every automated and manual correction
- Data residency and retention for sensitive documents
FAQ
How is this different from plain OCR?
OCR gives you the text. Document processing tells you which text is the invoice total, validates it against the line items, and hands you a structured record.
Do I need to train it per document type?
Increasingly no — model-based extraction generalises across layouts. A handful of examples still helps noticeably for unusual or highly specific formats.
How should human review work?
Route by confidence. Auto-post high-confidence fields and queue only the uncertain ones, which is what makes the economics work at volume.