anishganguli presents a detailed blueprint for extracting actionable, trusted data from large semi-structured documents using Azure AI technologies, focusing on scalable, context-aware pipelines and real-world evaluation.

From Large Semi-Structured Docs to Actionable Data: Reusable Pipelines with ADI, AI Search & OpenAI

Problem Space

Processing large, inconsistent, semi-structured documents (like contracts, invoices, hospital tariffs, and compliance records) is challenging due to complex layouts, inconsistent structures, and dispersed related fields, which traditional approaches often fail to handle. Hallucinations and context loss with LLMs present further risks, especially where reliability and traceability are critical (e.g., compliance, finance).

Use Cases

  • Healthcare: Digitizing hospital tariff cards for reconciliation and claims in insurance
  • Banking: Automating loan underwriting from balance sheets and auditor reports
  • Manufacturing: Extracting terms from procurement contracts and SLAs for compliance and automation
  • Regulatory Compliance: Deterministic extraction from compliance/audit documents for checklists and rule engines

Solution Overview & Architecture

The guide proposes a modular, reusable pipeline using Microsoft technologies for transforming documents into structured, machine-readable formats. Key components include:

  1. Chunking: Splits large documents into manageable, logically coherent blocks (Python with pdf2image, PIL)
  2. OCR & Layout Extraction: Uses Azure Document Intelligence or Foundry Content Understanding models to extract text and structural details
  3. Context-Aware Structural Analysis: Identifies relationships and injects necessary context (e.g., missing table headers) via custom Python logic
  4. Labelling: Employs Azure OpenAI GPT-4.1-mini for multi-class classification, entity-based labeling
  5. Entity-Wise Grouping: Groups labeled chunks using Azure AI Search with hybrid/semantic reranking
  6. Item Extraction: Leverages Azure OpenAI prompts to extract normalized objects (key-value pairs, tables) using visual and structural cues
  7. Storage: Stores intermediate (chunk-level) and final extracted data in Azure AI Search, Cosmos DB, SQL DB, or Microsoft Fabric
  8. Integration: Supplies structured outputs for downstream apps via REST APIs, Azure Functions, or Data Pipelines

Sample algorithms for header injection, labelling, chunk/entity relevance, and extraction are detailed, emphasizing precision and robustness.

Deployment Models Compared

  • REST API on Azure Kubernetes Service: Flexible, but resource scaling may be required for large documents
  • Azure Machine Learning Pipelines: Efficient for bulk processing; higher dev/maintenance complexity
  • Azure Databricks Jobs: Robust for time/memory management, tailored to Databricks environment
  • Microsoft Fabric Pipelines: Comparable to Databricks/AML plus real-time integrations (e.g., Fabric Activator)

Each approach must align to operational and scaling requirements.

Evaluation & Metrics

Effectiveness is measured against manually validated data using:

  • Individual Item/Attribute Match (strict and fuzzy)
  • Combined Attribute Match
  • Precision (correct vs. total matches)

Real-world findings: Over 90% precision for individual attributes (fuzzy match), but multi-attribute scores drop to around 43–48%, highlighting key pipeline reliability insights.

Alternative Approaches

  • Using Azure OpenAI alone for structured extraction
  • RAG (Retrieval-Augmented Generation) solutions combining Azure OpenAI, Document Intelligence, and AI Search
  • Non-RAG solutions mixing same components but focusing on pipeline (not conversational AI)

Reference links to Microsoft docs, best practices, and solution accelerators are provided for further detail.

Conclusion

By integrating Azure Document Intelligence, OpenAI models, and AI Search, the pipeline transforms unstructured, chaotic documents into trusted structured data for compliance, analytics, and automation. Modular chunking, context preservation, entity-based retrieval, and precision-focused evaluation drive reliability at enterprise scale.

References

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