Optical Character Recognition (OCR) solves one problem: making text in images machine-readable. It has been a reliable enterprise tool for decades. But it has a fundamental limitation — it sees characters, not meaning. A contract, an invoice, and a medical record all contain text. OCR treats them identically.
What Document AI adds
Document AI — also called Intelligent Document Processing (IDP) — layers natural language understanding on top of text extraction. It can identify what type of document it is processing, which fields matter, and what those fields mean in context. For example: given a supplier invoice, Document AI extracts the invoice number, line items, totals, payment terms, and vendor details — then routes that structured data directly into an ERP system. No human rekeying. No formatting step. No template required for each new supplier layout.
Key differences: OCR vs Document AI
Traditional OCR extracts text. Document AI classifies document type, extracts named fields (dates, amounts, names), handles varied layouts without templates, enables natural-language queries against document content, and integrates structured output into enterprise systems automatically — not manually.
Ambli's DocSense module is built on Document AI principles — it transforms unstructured documents into structured, queryable knowledge assets. It supports PDFs, spreadsheets, contracts, and text files, and integrates with enterprise systems via REST and GraphQL APIs. Extraction accuracy runs at 99.7%, with multi-modal document parsing that handles complex layouts without per-template configuration.