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Letting Data Speak, AI Act!
Case Study
AI-Assistance for Customer Support for Rent-to-Own Industry
About the Client
A rent-to-own industry organization requiring intelligent assistance for customer support agents to improve lead conversion rates and response quality during live chat conversations.
Challenge
The organization faced critical challenges in maintaining consistent, high-quality customer support across their agent team.
Customer support agents demonstrated varying levels of expertise, resulting in inconsistent response quality and conversion rates. New agents struggled to craft persuasive responses that effectively moved customers through the sales funnel, while experienced agents lacked mechanisms to systematically share their proven communication strategies with the broader team.
In the fast-paced rent-to-own industry, response speed directly impacts conversion rates. Agents spending time crafting responses or searching through documentation meant slower reply times and lost conversion opportunities. The organization had accumulated extensive historical data from thousands of successful customer conversations stored in Amazon RDS, but this valuable knowledge remained trapped and inaccessible during live interactions.
Traditional approaches such as extensive training programs and static documentation proved difficult to scale as the customer base grew. Without an intelligent system to analyze conversation context and surface relevant insights from successful past interactions, agents could not benefit from the collective wisdom embedded in the organization's historical data.
The lack of real-time, context-aware guidance meant agents were essentially starting from scratch with each customer interaction, unable to leverage patterns and strategies that had proven successful in similar situations.
Key Results
Enabled agents to receive three context-aware response suggestions within seconds during live customer conversations, dramatically reducing response time and improving agent productivity.
Implemented retrieval-augmented generation (RAG) architecture that learns from historical successful conversations, ensuring every agent benefits from proven communication strategies regardless of experience level.
Delivered consistent, high-quality response suggestions by leveraging semantic search across conversation patterns that led to successful lead conversions, eliminating inconsistency in customer interactions.
Achieved cost-effective operation processing up to 36,000 requests per month while maintaining sub-second response times through serverless architecture.
Solution
The solution implemented a dual-phase architecture combining offline knowledge preparation with real-time AI inference, leveraging AWS serverless services and Amazon Bedrock.
Architected an ETL pipeline using AWS Lambda functions that extracted historical conversation data from successful lead conversions stored in Amazon RDS, transformed the raw data into structured JSON format with deduplication and semantic chunking, and loaded the processed data into Amazon S3 as the foundation for the Knowledge Base.
Developed an AWS Bedrock Knowledge Base utilizing vector embeddings to enable semantic search across thousands of successful customer conversations, allowing the system to retrieve contextually relevant past interactions based on conversation patterns rather than keyword matching.
Built a real-time inference Lambda function triggered via Amazon API Gateway that implements a three-step intelligent workflow: intent identification to understand the conversation context, knowledge retrieval using vector similarity search, and response generation using Claude Haiku 4.5 via Amazon Bedrock to produce three distinct, actionable suggestions.
Implemented intelligent orchestration that analyzes each customer message in real-time, generates semantic search queries to fetch relevant historical examples from the Knowledge Base, and synthesizes retrieved knowledge with current conversation context to generate tailored response options for agents.
Established automatic fallback mechanisms in Lambda functions that ensure agents always receive response suggestions even during AI service disruptions, maintaining system reliability through rule-based generation when needed.
Deployed secure infrastructure with VPC isolation for database access, AWS Secrets Manager for credential management, and Amazon CloudWatch for comprehensive monitoring and logging of all Lambda executions and API requests.
Implemented comprehensive error handling and resilience patterns including automatic retries, graceful degradation, and detailed CloudWatch metrics enabling rapid troubleshooting and performance optimization.
Technologies Used
AWS Bedrock (Claude Haiku 4.5)
Amazon OpenSearch Service
Amazon Titan Embeddings v2
AWS Lambda
Amazon DynamoDB
Amazon S3
Amazon API Gateway
Amazon EventBridge
React
Python
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