Intelligent Document Processing

Intelligent document processing (IDP) uses AI and machine learning to turn documents into structured data your systems can act on. It classifies a document by type, locates and extracts the fields that matter, validates them against your systems of record, and passes clean data to the workflows and AI agents that do the rest of the work. 

OCR converts an image of text into characters. That is one step inside IDP, not the whole thing. IDP adds classification, so it knows an invoice from a claim form, and context-based extraction, so it finds the invoice total wherever it sits on the page rather than at fixed coordinates. It also validates what it extracts and routes what it cannot resolve. 

Structured forms, semi-structured documents like invoices and explanations of benefits, and unstructured documents like contracts and correspondence. Accuracy varies with document quality and how much the format varies between senders, so a short assessment on your actual documents is worth more than any vendor benchmark. 

Not in the way a person does, and it is worth being precise about that. IDP models classify documents and read the context around a field, which is why they handle format variation that template-based capture cannot. They do not comprehend meaning or intent. Where a decision depends on understanding what a document means, that decision stays with a person. 

Yes, and the two work together. An agent cannot act on a document it cannot read. IDP is what turns the invoice, claim, or contract into data the agent can reason over and the workflow can route. Organizations that skip this layer are usually the ones whose agent pilots stall. 

It depends on document variety, integration requirements, and how clean your source systems are. The honest answer is that a single well-scoped document type moves faster than most people expect and a sprawling multi-format program takes longer. [CONFIRM: typical timeline range from a delivery lead]