Case Study

AI-Powered Document Intelligence Transforms Legal Services with Real-Time Responses and 80% Contextual Accuracy

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Industry 

Legal Service

Expertise 

Amazon API Gateway, Amazon Bedrock, AWS Lambda, Amazon S3, Amazon Aurora

Offerings/solutions 

An AI-powered multilingual chatbot for real-time document intelligence using RAG.

About the Client

The company provides comprehensive legal and documentation services for banking and corporate clients, including Encumbrance Certificates, document certificate copies, MOD Registration, ROC Certificates, EC index correction, Mortgage Cancellation, ROC/CERSAI verification, Recovery SARFAESI Proceedings, Legal Notices, Arbitration/Conciliation, and registration-related services.

Highlights

80%

Precision-Driven AI Responses

Real-time document query responses

Lightning-Fast Document Intelligence

Multilingual Scale and Flexibility

Amazon Bedrock

The Challenge

Manual document review processes caused longer turnaround times, increased human error, and scaling challenges, with no automated mechanism for intelligent document processing and information extraction.

Solutions

• Developed an agentic AI-powered multilingual chatbot for land document intelligence
• Stored legal documents securely in Amazon S3 with versioned management
• Used Amazon API Gateway as secure entry point for document search requests
• Orchestrated workflows with AWS Lambda for document processing and AI-powered search
• Implemented Amazon Bedrock Knowledge Base for intelligent document chunking, embedding, and semantic retrieval
• Leveraged Amazon Aurora PostgreSQL as vector database for fast semantic similarity search
• Generated AI responses using Amazon Bedrock foundation model (Amazon Nova Pro) grounded in retrieved content
• Stored metadata and conversation history in Amazon DynamoDB
• Enforced secure access using AWS IAM with least-privilege policies

The Results

Built a multilingual AI chatbot on AWS that transformed document query processing from hours to real-time with 80% contextual accuracy using RAG.

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