What Intelligent Document Processing (IDP) means in practice
Intelligent Document Processing is the modern, LLM-augmented evolution of OCR. Where OCR was "image to text", IDP is "document to structured business action". A typical IDP pipeline: ingest the document, classify its type (invoice, claim, policy, ID), extract the fields the workflow needs, validate them against business rules, route to the appropriate downstream system, and surface exceptions for human review. MindMap's DocuMage is our flagship IDP platform; it handles 3,000+ documents per day at customer sites with 94% straight-through processing across heterogeneous document types. The 6% that escapes to human review is where the value-engineering happens — most teams optimise for raw extraction accuracy and ignore exception-handling design.
Related terms
OCR (Optical Character Recognition) →
The classical step of converting an image of text into machine-readable characters — the foundation layer underneath any document processing pipeline.
LLM-augmented extraction →
Using a large language model to extract structured fields from documents — particularly effective on layout-free documents where template-based OCR fails.
Schema-driven extraction →
The pattern where the target output schema is the primary input to the extraction prompt — the LLM is told exactly which fields to find and what type each should be.
More in this category
All 62 terms, in plain language
Sovereign AI, RAG, agentic AI, IDP, MLOps and the regulations that shape enterprise AI.