Intelligent document processing (IDP) is software that uses AI to read business documents, such as invoices, purchase orders, claims, application forms, IDs and contracts, work out what each document is, pull out the data that matters and pass it to the systems that need it. It combines text recognition with machine learning (ML) and, increasingly, large language models (LLMs), so it can handle varied layouts that rigid templates struggle with. Results it isn’t confident about are typically sent to a person for review.
Not to be confused with an Identity Provider, which is also abbreviated IdP: the system that verifies who users are and vouches for them to other applications.
At a glance
- IDP turns documents, whether scanned, photographed, emailed or digital, into structured data business systems can use.
- It usually combines OCR, document classification, data extraction, validation and human review.
- It handles varied layouts better than template-based capture, though accuracy still depends on document quality and type.
- Common uses include accounts payable, customer onboarding, insurance claims, logistics paperwork and contract review.
- It is often paired with robotic process automation (RPA) or workflow tools to complete the process end to end.
What problem it solves
Many business processes still start with a document. Someone opens an invoice, reads the supplier, amounts and line items, and keys them into the finance system; someone else checks a form against a policy. This is slow, expensive at volume and error-prone, and backlogs build when volumes spike.
Older capture tools used fixed templates: tell the software where on the page each field is, and it reads that spot. That works until a supplier changes its layout or a new form arrives. IDP is designed to cope with variety by recognizing what a field is from context rather than position alone, which makes it practical for documents from many outside sources.
How it works
Ingestion. Documents arrive from email, scanners, upload portals, shared folders or other systems.
Recognition and classification. OCR converts images to text where needed, and the system works out what kind of document it is, for example an invoice, a bill of lading or a driver’s license.
Extraction. Models identify the fields required for that document type, such as invoice number, totals, dates, names or line items, and return each value with a confidence score. Some products use language models to handle less structured documents such as contracts and letters.
Validation. Extracted data is checked against rules and other systems: do the line items add up, does the purchase order exist, is the supplier known?
Human review. Low-confidence or failed items go to a person, whose corrections may be used to improve future results, depending on the product.
Integration. Clean data is sent to ERP, CRM, claims, document management or workflow systems, through APIs, connectors or RPA bots.
Where language models are used, extraction can occasionally produce values that aren’t in the document, a form of AI hallucination, which is one reason validation and review steps matter.
When it matters for buyers
- When document volume is high and repetitive. Invoices, claims and onboarding packets are the classic starting points.
- When documents come from many outside parties. Varied layouts are where IDP earns its keep over template tools.
- When documents hold sensitive data. IDs, financial and health documents raise questions about where processing happens and how long copies are kept.
- When planning broader automation. IDP is often the front end of hyperautomation programs that combine several tools.
Our artificial intelligence overview covers providers offering document automation.
Questions to ask vendors
- Can you show accuracy measured on a sample of our own documents, field by field?
- Which document types work out of the box, and how much setup do new types need?
- How are low-confidence results routed for review, and what does the review screen look like?
- Which systems can you send data to, and through what connectors or APIs?
- Where are documents processed and stored, for how long, and are they used to train models?
- How is pricing calculated (per page, per document, per month), and what counts as a page?
- Do you use language models for extraction, and how do you check their output?
How it differs from RPA
Robotic process automation (RPA) automates actions in software: clicking, copying and entering data across applications by following set rules. It needs structured input to act on. IDP produces that structured input from unstructured or semi-structured documents. In a typical accounts payable flow, IDP reads the invoice and extracts its data, and RPA or an integration enters it into the finance system. Neither depends on the other, but they are frequently bought and deployed together.