Intelligent Document Processing (IDP)

Documents are the connective tissue of enterprise operations. An invoice arrives as a PDF; a human opens it, reads the vendor name, invoice number, line items, and total, and manually types those values into the ERP system. A contract is signed; a paralegal extracts the key dates, obligations, and renewal terms into a spreadsheet. An insurance claim arrives; a clerk reads it and types the relevant fields into the claims management system. IDP automates all of this.

The IDP pipeline has five components: (1) **Ingestion** — documents arrive via email, upload, or integration from source systems; (2) **Classification** — ML models identify the document type (invoice vs. purchase order vs. contract) and route it to the appropriate extraction workflow; (3) **Extraction** — computer vision + OCR extracts raw text, while NLP/LLM models identify and normalize specific fields (vendor, amount, date, line items) regardless of document layout variation; (4) **Validation** — extracted values are checked against business rules and cross-referenced with master data (is this vendor in the approved vendor list? does the PO number exist?); (5) **Integration** — validated data is written to ERP, CRM, ECM, or workflow systems via API.

Modern IDP platforms have moved beyond template-matching (which breaks when a vendor changes their invoice layout) to generalization — the same model handles hundreds of different document formats because it understands the semantic meaning of fields, not just their position on the page. LLM integration has further raised the bar: complex clause extraction from contracts, multi-page context reasoning, and exception handling that previously required human review can now be handled automatically for the majority of documents.