Strategies for Structured Extraction with Azure OpenAI

Executive Summary 

Extracting meaningful, structured data from long, unstructured documents — especially legal contracts, insurance policies, or financial reports — has long been a challenge in enterprise automation. This comprehensive guide outlines proven best practices using Azure OpenAI's Structured Outputs to transform messy, variable-format text into clean, schema-aligned data ready for downstream systems. 

Introduction 

Recently, the Microsoft AI engineering team delivered a real-world solution using Azure OpenAI that transformed complex legal text into reliable, schema-aligned data. This approach not only improved accuracy but also made the extracted information instantly usable in downstream business systems such as CRMs, ERP platforms, and Power BI dashboards. 

Let's walk through the core practices and patterns that made this solution successful — and how you can apply them in your next automation project. 

Figure 1: Azure OpenAI enables intelligent transformation of unstructured documents into structured data 

The Challenge: Chaos in Legal Text 

Legal documents rarely follow a consistent pattern. Information can appear out of order, recur in multiple forms, or hide inside long clauses. The customer's goal was to populate a standardized form from hundreds of these documents, ensuring all fields were correct and complete. 

Common challenges included: 

1. Scattered and non-linear placement of key data points 
2. Missing or conflicting values across sections 
3. Repetitions or rephrasings of the same details 
4. LLMs occasionally generating hallucinated field names or malformed JSON 

The solution needed to ensure strict adherence to a fixed schema — with no missing or extra data. 

Solution Overview: The Three-Pillar Approach 

The implemented framework relied on three guiding principles to achieve consistent structured extraction: 

1. Chunked document processing 
2. Schema enforcement using Structured Outputs (via Pydantic) 
3. Iterative update logic for continuous refinement 

 1. Chunked Document Processing 

Instead of feeding a huge document directly into the model, the team split the document into manageable chunks — typically by paragraph or section boundaries. 

Each chunk was processed in sequence. After analyzing one chunk, the model would review whether any fields in the structured form needed to be created or updated. 

Why it works: 

1. Keeps the model focused on relevant context 
2. Reduces hallucination risk caused by overloaded context windows 
3. Allows the system to "learn progressively" across the document 

Figure 3: Intelligent document processing pipeline with sequential chunking and validation 

 2. Schema Enforcement with Structured Outputs 

Azure OpenAI's Structured Outputs feature (available in Python, .NET, and REST APIs) ensures that model responses conform to a strict predefined schema, enforced by tools like Pydantic. 

Here's an example schema used in the legal document extraction scenario: 

from pydantic import BaseModel 

class ContractData(BaseModel): 
contract_title: str 
contract_date: str 
party_1_name: str 
party_2_name: str 
effective_date: str 
term_length: str 
payment_terms: str 
termination_clause_summary: str 
governing_law: str 
implicit_obligations: str 

By specifying this model, the system guaranteed that: 

1. Only valid fields were returned by the model 
2. Field order and hierarchy matched form expectations 
3. Any deviation or malformed JSON triggered validation errors 

This technique eliminated hallucinated or missing fields, ensuring confidence in downstream systems. 

Figure 4: Structured data validation enforces schema compliance and prevents malformed outputs 

 3. Iterative Update-on-Change Logic 

Rather than regenerating the entire form after each chunk, the solution used smart update logic: 

1. If a previously extracted field remained valid, its value was preserved 
2. If new context offered a better or bn corrected value, only that field was updated 

This incremental refinement allowed continuous improvement across chunks without redundant reprocessing. 

Why it matters: This method balances efficiency (no excessive updates) with accuracy (progressive enrichment), achieving stability over long or inconsistent documents. 

Synthesized Best Practices 

Table 1: Key best practices for structured extraction from documents 

 Industry Applications 

By enforcing structure and applying iterative extraction, enterprises can confidently convert unstructured content into structured, automation-ready data across many industries: 

1. Healthcare → Populate clinical forms from discharge summaries 
2. Finance → Extract regulatory data from reports 
3. Insurance → Summarize claim details from policy text 
4. Logistics → Capture shipment data from packing slips 

Figure 5: Document data extraction automates workflows across multiple industries 

The result is both reliable and scalable AI automation — ready for integration with Power Apps, Power Automate, or Azure Data Factory pipelines. 

Summary 

By combining chunked processing, strict schema enforcement with structured outputs, and update-on-change logic, Azure OpenAI can reliably turn unstructured documents into clean, automation-ready data. This pattern reduces hallucinations, preserves field integrity, and scales across diverse domains like legal, healthcare, finance, insurance, and logistics[2]. 

The key to success is recognizing that structure is not a limitation — it's a guarantee. When you enforce rigid schemas at the model response level, you eliminate entire categories of errors that plague free-form extraction approaches. 

 Conclusion 

Structured extraction is no longer just a research problem — it is a practical, production-ready capability when implemented with the right architectural patterns on Azure OpenAI[2]. 

Teams that invest in schema-first design, intelligent chunking, and iterative refinement unlock trustworthy data flows that feed downstream workflows, analytics, and decision-making with minimal manual intervention. 

Whether you're processing legal contracts, insurance claims, financial statements, or logistics documents, the three-pillar approach outlined here provides a proven path to reliable, scalable, and maintainable automation. 

The future of enterprise document processing is structured. Azure OpenAI makes it achievable today. 

References 

[1] Microsoft Azure Documentation. (2025). Structured Outputs in Azure OpenAI. Retrieved from https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/structured-outputs