Master Azure AI Document Intelligence with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Explore the key features that make Azure AI Document Intelligence powerful for document processing workflows.
Train extraction models on your own labeled documents to handle proprietary formats. Document Intelligence Studio provides visual labeling tools. Custom template models work for fixed layouts (5+ samples needed). Custom neural models handle variable layouts (10+ samples needed). This capability is absent in Amazon Textract.
An insurance company has a proprietary claims form with 40 fields unique to their business. They label 10 sample forms in Document Intelligence Studio, train a custom neural model, and achieve 95% extraction accuracy on new claims.
Identifies document structure beyond text: paragraphs, sections, headers, footers, page numbers, tables with merged cells, figures, and reading order. Outputs structured data suitable for LLM and RAG pipelines.
A legal firm converts 10,000 contracts into structured data for a RAG system. Layout analysis preserves section hierarchy, clause numbering, and table relationships so the LLM can answer questions about specific contract terms with proper context.
Extracts vendor name, address, customer info, invoice number, dates, line items with descriptions, quantities, unit prices, totals, tax, and payment terms from invoices regardless of format or layout.
An AP department processes invoices from 200 different vendors. The prebuilt model handles each vendor's format without per-vendor configuration, extracting structured data for automated payment processing at $0.01/page.
Browser-based visual interface for testing prebuilt models, labeling training data for custom models, and building extraction pipelines without code. Supports drag-and-drop field labeling and real-time extraction preview.
A business analyst without coding skills opens Document Intelligence Studio, uploads sample purchase orders, draws boxes around the fields to extract, and trains a custom model. No developer involvement needed for the initial prototype.
Document Intelligence wins on custom model training (Textract has none), layout analysis depth, and basic OCR pricing ($0.001 vs $0.0015/page). Textract wins on AWS ecosystem integration and simpler pricing structure. Choose based on your cloud provider and whether you need custom models. If your documents have unusual formats, Azure is the better option.
Custom template models need at least 5 labeled samples for fixed-layout documents. Custom neural models need at least 10 samples for variable-layout documents. More samples improve accuracy, but the minimum is surprisingly low.
Yes. Unlike Textract's 3-month free tier, Document Intelligence's 500 pages/month free tier has no expiration. It's available indefinitely on all Azure subscriptions.
Document Intelligence Studio provides a browser-based visual interface for testing prebuilt models, labeling training data, and building custom models. Business analysts can create extraction models without writing code, though developers are needed for production integration.
Now that you know how to use Azure AI Document Intelligence, it's time to put this knowledge into practice.
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Tutorial updated March 2026