Agentic document extraction and normalization (AI agent use case)

Challenge

Since the client’s data landscape included diverse sources: .xlsx, .csv, and scanned handwritten documents, – data cleaning, normalization, and transformation for business analytics were difficult. The scanned handwritten documents added further complexity, requiring manual extraction of embedded text and data points.

This manual work was error-prone, time-consuming, and inconsistent, with Python scripts needing constant adjustments to handle new data patterns and formats. 

Project Scope

We implemented an AI agent for agentic document extraction based on LangChain and the OpenAI API, seamlessly integrated into the Azure environment. The goal was to automate and streamline data preparation with minimal manual intervention.

Our Approach for AI Agent Development

The AI-driven system automates data preparation through the following:

1. Excel and CSV Analysis

The agent reads and analyzes structured data from Excel and CSV files.

2. PDF Extraction with Azure AI Document Intelligence

Leveraging OCR capabilities, the agent extracts both structured and unstructured content from from scanned hand-written documents.

3. Data Normalization & Exception Handling

It applies predefined normalization rules and automatically resolves data inconsistencies; all without requiring manual input.

System Integration

To ensure seamless operation within existing infrastructure:

• The AI agent connects directly to the existing Java API, providing clean, normalized data for further processing.

Security & Compliance

All AI operations are run within the client’s Azure environment, ensuring:

• Data security;
• Full compliance;
• No exposure of sensitive information outside the client’s trusted infrastructure.

Results and Achievements

Within four months, the AI agent was fully deployed, greatly reducing the need for manual scripting and simplifying the entire data pipeline.

Now it automates data cleaning and normalization across various data sources, while also enabling accurate OCR data extraction process. 
As a result, the client experienced more consistent data, faster availability for analysis, and the ability to focus engineering efforts on high-value tasks.