Custom AI Models for Automated Data Entry with Azure Document Intelligence

Bravado Solutions | Specialized in building scalable AI systems, SaaS platforms, and automated cloud-native document pipelines.

Leverage Azure AI Document Intelligence to extract text, tables, and key data from complex forms and automatically update your enterprise database.

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🚀 Overview

Manual data entry is the primary bottleneck for scaling administrative operations. This repository demonstrates an Automated Data Extraction Pipeline that transforms unstructured documents (PDFs, Images, Scans) into structured database records using specialized AI models.

🏢 The Challenge (Real-World Context)

Bravado Solutions recently partnered with a large organization struggling with thousands of purchase order (PO) documents. The manual entry process was:

• Slow: 5–10 minutes per document.
• Error-Prone: Frequent typos in SKU numbers and pricing.
• Resource-Intensive: Required a dedicated team just for data transcription.

💡 The Solution

We implemented a custom AI solution using Azure AI Document Intelligence. This system automatically extracts text, key-value pairs, and complex tables from forms, enabling accurate and scalable data entry across thousands of documents without human intervention.

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🏗️ Architecture

graph LR
    Doc[Source Document] --> Trigger[Queue Message]
    Trigger --> DI[Azure Document Intelligence]
    subgraph "AI Processing"
        DI --> Model{Custom Model}
        Model --> Layout[Table Extraction]
        Model --> KVP[Key-Value Pairs]
    end
    DI --> Logic[Validation Logic]
    Logic --> DB[(Enterprise SQL Server)]
    Logic --> Alert[Manual Review Flags]

    style DI fill:#0078d4,stroke:#005a9e,color:#fff
    style DB fill:#f29111,stroke:#b36b08,color:#fff

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⚙️ Key Features

• Custom Neural Models: Trained on industry-specific forms for 99%+ field accuracy.
• Table Extraction: Intelligent reconstruction of multi-page line items and nested tables.
• Automated SQL Mapping: Direct mapping of extracted JSON to relational database schemas.
• Asynchronous Processing: Built with a queue-based architecture to handle high-concurrency document uploads.
• Confidence Scoring: Automatic flagging of low-confidence extractions for manual review (Human-in-the-loop).

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📋 1. Technical Pre-requisites

A. Azure AI Document Intelligence Resource

• Tier: Must be Standard (S0). The Free (F0) tier does not support "Neural" custom models.
• Region: Ensure your resource is in a region supporting Neural models (e.g., East US, West Europe).

B. Labeled Training Data

The system requires labeled data to build a specialized model:

• Blob Storage: A container with 5–10 labeled sample documents.
• Labels: Use Azure Document Intelligence Studio to label fields.
• SAS URL: The CONTAINER_SAS_URL must point to this specific container.

C. Infrastructure Requirements

• Storage Queue: Create a queue named doc-processing-queue.
• CORS: Enable CORS on your Storage Account for the Document Intelligence service.
• SQL Server: An Azure SQL Database or local instance with the following schema:

CREATE TABLE ExtractedDocuments (
    Id INT IDENTITY(1,1) PRIMARY KEY,
    DocumentType NVARCHAR(100),
    FieldName NVARCHAR(100),
    FieldValue NVARCHAR(MAX),
    Confidence FLOAT,
    ProcessedAt DATETIME DEFAULT GETDATE()
);

📂 Repository Structure

bravado-serverless-ai/
├── .env.example            # Template for environment variables
├── .funcignore             # Files to exclude from Azure deployment
├── .gitignore              # Files to exclude from GitHub (secrets/cache)
├── Dockerfile              # Containerization configuration
├── function_app.py         # The Serverless Worker (Queue Trigger)
├── host.json               # Azure Function global config
├── orchestrator.py         # The Admin tool (Training & Feeding Queue)
├── README.md               # Documentation & Setup Guide
├── requirements.txt        # Python dependencies
└── src/
    ├── __init__.py         # Makes src a package
    ├── client_factory.py   # Azure Client Factory
    ├── database_manager.py # SQL Server Integration Logic
    ├── model_manager.py    # Training Logic
    └── processor.py        # AI Extraction Logic

🚀 Getting Started

1. Clone the Repo

2. Install Dependencies:

   pip install -r requirements.txt

3. Configure Environment: Rename .env.example to .env and fill in your Azure & SQL credentials.

4. Train & Queue: Run the orchestrator to build your custom model and begin processing:

   python orchestrator.py

5. Run Worker: Use the Azure Functions Core Tools to run the serverless extraction worker locally:

   func start

📚 Resources & Further Reading

🧠 Azure AI Document Intelligence

• Official Documentation: Comprehensive guides on OCR, Layout, and Custom Models.
• Document Intelligence Studio: The no-code graphical interface for labeling and testing models.

⚡ Serverless & Scaling

• Azure Functions Python Developer Guide: Best practices for scaling Python workers.
• Azure Storage Queues SDK: Technical details on message polling and visibility.

🗄️ Database Integration

• Python SQL Driver (pyodbc): Documentation for connecting Python to SQL Server.
• ODBC Driver 18 for SQL Server: Installation links for the required database drivers.

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🤝 Support & Consulting

Bravado Solutions specializes in architecting custom AI pipelines and cloud-native automation. If your organization needs help scaling document workflows or integrating LLMs into existing enterprise systems, reach out to our team.

• Website: bravadosolutions.com
• Inquiries: contact@bravadosolutions.com