OCR reads text from documents. Production Document AI should go further: classify document types, extract key fields, perform cross-document consistency checks, validate formats/rules and route low-confidence items to human review.
Classification
Identify the document type first because fields, rules and workflow differ across IDs, statements, contracts and invoices.
Extraction
Return structured fields that downstream systems can use, not only long blocks of text.
Validation & cross-check
Validate data type, format, totals, dates, field relationships and consistency with other documents.
Human in the loop
High-confidence cases can be automated while uncertain cases go to review, with feedback captured for measurement and improvement.
Frequently asked questions
Are OCR and Document AI the same?
No. OCR is a foundational capability; Document AI adds classification, extraction, validation, review and workflow.
Do we always need a VLM?
No. OCR, layout models, VLMs or hybrid approaches should be selected based on document type, accuracy, latency, cost and infrastructure.
How should accuracy be measured?
Measure at field level and by business-relevant error type, not only with a page-level OCR average.