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Letting Data Speak, AI Act!
Case Study
AI-Powered Knowledge Access Platform for Rent-to-Own Industry
About the Client
A rent-to-own industry organization requiring intelligent access to both public industry knowledge and private organizational data. This enterprise-level client needed a unified conversational interface that could serve both external stakeholders seeking industry information and internal teams requiring data-driven insights from their operational databases. The solution addresses common challenges faced by organizations managing large knowledge bases and complex data warehouses across retail and financial services sectors.
Challenge
The organization faced critical challenges in accessing and synthesizing information from disparate data sources, which severely impacted operational efficiency and decision-making capabilities.
The business operated with two completely separate information ecosystems—publicly available rent-to-own industry knowledge stored in documents and private organizational data housed in relational databases—requiring users to manually search multiple systems and synthesize information across platforms.
Traditional search and query methods forced technical and non-technical users alike to navigate complex database query languages and document repositories, creating significant barriers to timely access of critical business intelligence.
The absence of contextual, conversational access to data meant users could not ask follow-up questions or refine their queries naturally, resulting in incomplete analyses and missed insights that could inform strategic business decisions.
Without an intelligent system to understand user intent and route queries appropriately, the organization experienced significant productivity losses as employees spent excessive time locating information rather than leveraging it for business value.
The lack of integration between public industry knowledge and private operational data prevented the organization from performing comparative analyses and identifying market trends that could drive competitive advantage in the rapidly evolving rent-to-own sector.
Key Results
Reduced average query response time to 5-8 seconds for knowledge base queries and 25-35 seconds for complex database queries, eliminating the hours previously required for manual data retrieval and analysis.
Enabled natural language access to both public industry documents and private organizational databases through a unified conversational interface, improving accessibility for non-technical users
Implemented dual-pipeline RAG architecture with intelligent query routing that automatically determines the appropriate data source (public, private, or both), reducing user decision overhead and improving query accuracy
Solution
The solution implemented a sophisticated dual-pipeline architecture leveraging AWS serverless services and Large Language Models to create an intelligent, scalable knowledge access platform.
Architected a serverless backend infrastructure utilizing Amazon Bedrock with Claude Sonnet 4 for natural language understanding, Amazon OpenSearch Service for vector search capabilities, and AWS Lambda functions for orchestrating the query processing pipeline, ensuring scalable and cost-effective operations.
Developed a specialized Knowledge Base RAG Pipeline that ingested publicly available rent-to-own industry documents, created embeddings using Amazon Titan Embeddings v2, stored vectors in OpenSearch with rich metadata, and implemented query reformulation capabilities to incorporate conversational context for multi-turn interactions.
Built a Text-to-SQL Pipeline with intelligent schema understanding that analyzed user queries to determine relevant database tables, generated and validated SQL queries with built-in guardrails to prevent DDL/DML execution, implemented a retry mechanism with up to 3 attempts for failed queries, and translated structured results into natural language summaries.
Implemented an orchestrator Lambda function that managed session tracking via DynamoDB for conversation continuity, intelligently routed queries based on mode selection (public, private, or both), executed pipelines in parallel using ThreadPoolExecutor for the hybrid mode, and synthesized responses from multiple sources into coherent natural language answers.
Established automated daily synchronization processes using Amazon EventBridge CRON jobs that triggered document ingestion for the knowledge base and LLM-powered schema discovery for the database, maintaining up-to-date context and metadata for both pipelines.
Deployed a React-based conversational interface hosted on Amazon CloudFront with API Gateway integration, providing users with an intuitive chat experience that displayed natural language responses and optionally rendered structured query results in interactive data tables.
Implemented comprehensive monitoring and observability through Amazon CloudWatch for tracking Lambda execution metrics, token usage, and query performance, enabling continuous optimization of the system's accuracy and response times.
Technologies Used
AWS Bedrock (Claude Sonnet 4)
Amazon OpenSearch Service
Amazon Titan Embeddings v2
AWS Lambda
Amazon DynamoDB
Amazon S3
Amazon API Gateway
Amazon EventBridge
React
Python
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