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Letting Data Speak!
Case Study
AI-Powered Document Compliance Validation for Real Estate Transactions
About the Client
A real estate platform provider that connects industry professionals, offering tools and resources for efficient property transactions with a focus on enhancing transparency, trust, and collaboration. The company's ProBidder platform utilized Claude model to collect and analyze user inputs for generating contract bid reports for subcontractors.
Challenge
The client needed to evaluate and implement Generative AI technologies to automate compliance validation for signed real estate agreements, specifically focusing on California Residential Purchase Agreement (RPA) documents. The manual compliance validation process was time-consuming and prone to human error, requiring automated identification of missing fields, incomplete sections, and compliance issues based on predefined federal and state regulatory criteria. The client required a solution that could validate required signatures including DocuSign verification, check proper completion of checkboxes, verify date entries and confirmation timestamps, and ensure adherence to regulatory requirements while maintaining high accuracy and minimizing false positives.
Key Results
Implemented automated compliance validation system that reduced manual document review time by 70%
Achieved >90% accuracy in detecting missing required fields and incomplete sections in RPA documents
Established foundation for expanding validation capabilities to additional document categories, potentially saving processing costs
Solution
Serverless Architecture Implementation: Implemented a comprehensive AI-powered Proof of Concept (POC) solution using AWS serverless architecture with Lambda functions triggered by document uploads to designated S3 buckets.
AWS Bedrock Integration: Integrated AWS Lambda functions with AWS Bedrock to utilize multiple Large Language Models (LLMs) for automated document compliance validation of RPA documents.
Comprehensive Validation Logic: Developed LLM prompts to verify compliance based on RPA criteria documents, including identification of missing required fields, detection of incomplete sections based on compliance rules, verification of proper checkbox completion, validation of required signatures with DocuSign integration, and checking of proper date entries and confirmation timestamps
Model Optimization: Optimized model prompts and parameters to minimize false positives and negatives while ensuring high precision in detecting compliance issues and processing documents efficiently within Lambda execution constraints.
Multi-Model Evaluation: Configured and evaluated multiple LLM models available on AWS Bedrock to determine optimal performance for RPA validation tasks through comprehensive testing with sample RPA documents.
Technologies Used
AWS Lambda
AWS Bedrock
AWS S3
Generative AI - Claude, OpenAI
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