Intelligent document processing (IDP)

Definition

Intelligent document processing uses OCR and AI models to read documents such as invoices, contracts and forms, extract the fields you need, check them and post them into your systems.

Updated

Also called document AI.

How it works

An IDP pipeline runs the same stages for every document:

  1. Intake from an inbox, upload folder, scanner or API.
  2. OCR or a vision model turns each page into text with its layout.
  3. Classification decides what the document is: invoice, purchase order, W-9, lease.
  4. Extraction pulls the fields (supplier, dates, line items, totals) with a confidence score for each.
  5. Validation checks them against rules and your records: totals add up, the PO exists, the supplier is approved.
  6. Low-confidence or failed items go to a person, and the rest post to the ERP, accounting system or CRM.

Managed services such as Azure Document Intelligence (opens in a new tab), Amazon Textract and Google Document AI handle common document types well. General vision models from OpenAI, Anthropic and Google read messy layouts that older template-based tools could not. A typical build uses both.

How businesses use it

Accounts payable is the usual first project, because invoices arrive in volume and errors are easy to count. Other common uses are contract intake for legal teams, claims and policy documents in insurance, onboarding forms in healthcare and financial services, and delivery notes in logistics. See the invoice processing pipeline and contract review assistant blueprints for two worked designs.

Our IDP builds run $12,000 to $40,000. OCR and extraction services charge per page, and those fees are billed separately at cost.

Where it goes wrong

Accuracy on a vendor demo says little about your documents. Test on 100 to 200 of your own, including the worst scans and the odd suppliers, before you commit.

The second failure is skipping validation. A model can read a total correctly and still post it against the wrong supplier. Rules and record matching catch what extraction misses, and human review handles the rest.

IDP is the wrong tool when documents already arrive as structured data, such as EDI or XML e-invoices. Parse those directly. It also rarely pays back at low volume: under about 100 documents a month, a person with a checklist is cheaper.

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