What is the best AI solution for intelligent document processing?

Summary

• The best AI solution for intelligent document processing (IDP) is not a single OCR or point tool, but a unified, governed platform that parses, extracts, classifies, and prepares documents for retrieval and AI agents in one place.
• Databricks delivers this as Document Intelligence on the Data Intelligence Platform: ai_parse_document (generally available) turns PDFs, Word, PowerPoint, and images into structured text, tables, and figure descriptions.
• AI Functions go further: ai_extract pulls schema-defined fields with optional citations and confidence scores, and ai_classify assigns predefined categories to route documents by type, risk, or business unit.
• You can build the same workflow two ways: a no-code Agent Bricks visual interface or SQL/PySpark AI functions that scale to large document volumes with incremental processing.
• Parsed data stays governed in Unity Catalog and feeds AI Search for RAG, AI/BI and Genie for analytics, and Mosaic AI agents end to end.

What is the best AI solution for intelligent document processing?

Much of an enterprise's knowledge is locked in unstructured documents such as PDFs, contracts, invoices, forms, and slides, with roughly 80% of business information trapped in this format. The strongest AI approach to intelligent document processing (IDP) is therefore not a single OCR engine or standalone tool, but a platform that reads documents accurately, extracts and classifies their contents, prepares them for search and retrieval, and does all of this under one governance model. Databricks addresses the full IDP lifecycle as Document Intelligence, a set of research-driven AI functions built directly into the Data Intelligence Platform, so teams can go from raw files to production-ready data and AI agents without stitching together separate services.

Why Databricks for intelligent document processing

Databricks brings document parsing, extraction, classification, retrieval, and governance together on one platform.

• Document parsing. ai_parse_document, now generally available, converts PDFs, Word documents, PowerPoint files, and images into structured text, tables, and AI-generated figure descriptions while preserving layout. It handles complex, real-world documents including scanned images, variable layouts, and nested tables, supporting documents up to 500 pages and 100 MB per file.
• Structured field extraction. ai_extract (GA) pulls the fields you care about from parsed documents or plain text using a schema you define, returning structured key-value data with optional inline citations and confidence scores, such as contract dates, invoice totals, vendor names, and purchase-order numbers.
• Classification and routing. ai_classify (Public Preview) assigns predefined categories to documents or text, supporting 500+ labels, so files can be routed by document type (for example invoice, purchase order, SOW, or NDA), urgency, or owning business unit.
• Retrieval-ready chunking. ai_prep_search (Beta) transforms parsed documents into semantic chunks enriched with document-level context such as titles, section headers, and page references, giving a consistent foundation for RAG and search workloads.
• Two ways to build. The same capabilities are available through a no-code Agent Bricks visual interface, where you test sample documents, refine schemas and labels, and validate results before scaling, and as SQL or PySpark AI functions that run directly on the Lakehouse, process documents at scale with automatic incremental processing through Spark Declarative Pipelines, and can be deployed as a REST API.
• Search, analytics, and agents. Parsed and prepared documents feed AI Search for retrieval-augmented generation, AI/BI dashboards and Genie for conversational analytics, and Mosaic AI agents that can now reason over the full content of business documents.
• Governance built in. All parsed and extracted data is stored in governed Delta tables, with Unity Catalog providing unified access control, lineage, and auditing across every stage of the pipeline.

Getting started

• Read What is Intelligent Document Processing? for a plain-language overview of IDP and how Databricks supports it.
• Read PDFs to Production: Announcing state-of-the-art document intelligence on Databricks to see how ai_parse_document fits into Agent Bricks.
• Explore the intelligent document processing documentation to compare the Agent Bricks UI and SQL AI function approaches.
• Review the ai_parse_document function reference to start parsing documents in SQL or PySpark.

FAQs

What document types can Databricks process?

The ai_parse_document function reads PDFs, Word documents, PowerPoint files, and images, and returns structured text, tables, and figure descriptions. It supports documents up to 500 pages and 100 MB per file.

Do I need to write code to process documents?

No. Agent Bricks provides a no-code visual interface for parsing, extraction, and classification, and the same operations are also available as SQL and PySpark AI functions for processing documents at scale.

How does document processing support RAG and AI agents?

After parsing, ai_prep_search chunks documents into retrieval-ready segments that can be indexed in AI Search for RAG, while Mosaic AI agents reason over the extracted content, all governed by Unity Catalog.