Extracting structured data from complex unstructured documents using multimodal LLMs and computer vision. By 2026, IDP systems achieve 99% extraction accuracy,
Intelligent document processing pairs vision-language models with structured extraction: invoices, claims, contracts, and forms are parsed with layout awareness, validated against business rules, and posted into systems of record, with confidence scoring routing uncertain fields to human review. Modern IDP replaced brittle template-based OCR with models that generalize across document variants.
Edge-case documents, poor scans, unusual layouts, handwritten fields, dominate the error budget, and teams that chase 100% automation drown in exceptions. The winning pattern is explicit confidence routing: automate the clean majority, design a fast human lane for the messy tail, and measure field-level accuracy rather than document-level vanity numbers.
OCR reads characters; IDP understands documents, layout, tables, stamps, and meaning, using vision-language models that generalize across formats without per-template configuration, then validates and posts structured data automatically.
Field-level accuracy of 95%+ on clean, common document types is typical, with confidence routing sending the uncertain remainder to humans. Measure per-field on your own golden set: vendor headline numbers rarely transfer.