Intelligent Document Processing (IDP)

Intelligent Document Processing (IDP) is an umbrella term for systems that combine optical character recognition, natural language processing, and machine learning to automatically extract structured data from unstructured or semi-structured documents. Unlike traditional OCR, which simply converts images of text into machine-readable characters, IDP understands the semantic meaning of document content — identifying what a field represents, not just where it sits on the page.

IDP pipelines typically involve multiple stages: ingestion (accepting PDFs, scanned images, Word documents), text extraction (OCR or native text parsing), classification (determining document type), extraction (pulling out specific fields like dates, amounts, or clause text), and validation (checking extracted values against business rules or confidence thresholds). Each stage can be powered by different AI techniques, from rule-based heuristics to large language models.

In practice, IDP replaces manual data entry workflows across industries — from processing invoices and purchase orders in finance, to extracting lease terms in real estate, to parsing regulatory filings in compliance. DocumentIQ implements a full IDP pipeline where users define custom field schemas per project, and LLMs handle the extraction step with contextual understanding that template-based systems cannot match.