Intelligent document processing

Intelligent Document Processing (IDP) converts unstructured content—such as PDFs, DOCX files, images, and presentations—into structured, enriched data that powers downstream agents, applications, and analytics.

With Databricks, you can build end-to-end IDP pipelines directly on the Lakehouse using natively composable AI Functions, including ai_parse_document, ai_extract, and ai_classify. These research-developed functions are purpose-built for high-performance document processing. Because all processing runs within Unity Catalog, your production-grade IDP pipelines remain secure, governed, and fully managed in place.

• • Document parsing
  • Convert PDFs, DOCX, images, and PPTs into structured text, tables, and figure descriptions.
• • Information extraction
  • Pull structured fields from documents or plain text using a schema you define.
• • Classify content
  • Assign predefined categories to documents or text, supporting up to 500+ labels.

Common use cases

IDP on Databricks powers a wide range of downstream applications:

• Retrieval-augmented generation (RAG): Parse and structure documents to improve chunking, retrieval quality, and grounding for LLM applications.
• Knowledge extraction and analytics: Extract key fields and metadata to enable search, reporting, and business intelligence on document data.
• Agent-driven workflows: Route, classify, and enrich documents to support automated decision-making and task execution.
• Document understanding and classification: Organize large document corpora by type, topic, or content for downstream processing.

How it works

Databricks enables intelligent document processing as a unified, end-to-end workflow on the Lakehouse. Ingestion, parsing, enrichment, and downstream analysis are built on a single platform, so each stage works seamlessly together without requiring complex integration or data movement.

1. Ingest and orchestrate

   Use Lakeflow Spark Declarative Pipelines to ingest raw documents (such as PDFs, images, and DOCX files) and orchestrate your pipelines. Because ingestion and orchestration are natively integrated with the Lakehouse, documents flow directly into downstream processing without additional infrastructure.

2. Parse documents (Bronze layer)

   Apply ai_parse_document to convert raw files into structured representations. This creates a standardized bronze layer that captures text, tables/image descriptions, and document structure, forming a consistent foundation for all downstream use cases.

3. Extract and classify

   Use ai_extract and ai_classify to enrich parsed documents with structured fields and metadata. These functions operate directly on the parsed outputs, enabling you to extract key information, classify documents, and route them through workflows without additional transformation steps.

4. Analyze and operationalize

   Leverage additional AI Functions or other tools (AI/BI dashboards, Apps, Vector Search) for downstream analytics, retrieval (RAG), and agent-driven workflows. Because all data remains on the Lakehouse, structured document data can be immediately used for search, dashboards, and applications.