Context
In the logistics industry, customer support centers handle a large volume of routine inquiries, including status updates and frequently asked questions (FAQs). To reduce the load on support agents and enhance operational efficiency, an Interactive Voice Response (IVR) solution was developed. This IVR system, integrated with Language Models (LLMs), automates routine inquiries, streamlines customer interactions, and improves overall service delivery. The implementation resulted in a significant reduction of 30-40% in calls typically handled by customer support agents.
Requirements
Load Reduction on Support Agents:
Automate responses to status-related questions and FAQs to reduce the workload on customer support agents.
Dynamic Query Handling:
Enable the IVR system to understand and respond accurately to a wide range of customer inquiries.
Real-Time Information Retrieval:
Provide up-to-date status information using external API integrations.
Telephony Integration:
Integrate the bot with telephony network to make is accessible over the phone.
Multi-Channel Notifications:
Integrate automated WhatsApp and email messaging for specific status conditions requiring further processing.
Scalability and Reliability:
Ensure the solution can handle a high volume of inquiries without performance degradation.
Maintain high availability and reliability for continuous operation.
Approach
Requirement Analysis:
Engaging with stakeholders to understand their needs and expectations.
Identifying key features and functionalities required in the IVR system.
Design and Architecture:
Designing the architecture of the IVR solution, focusing on scalability, reliability, and real-time data processing.
Planning the integration of various components such as AWS Lex, EC2, FastAPI, and Lambda.
Implementation:
Developing the IVR system using AWS Lex for natural language understanding and dialogue management.
Utilizing AWS Lambda for serverless computing to handle backend logic and integrate with external APIs for status updates.
Deploying FastAPI on EC2 instances to expose the LLM RAG (Retrieval-Augmented Generation) chain for answering FAQs.
Integrating automated WhatsApp and email messaging for specific status conditions.
Testing and Validation:
Conducting extensive testing to ensure the IVR system accurately understands and responds to customer inquiries.
Validating the real-time information retrieval process to ensure data accuracy and timeliness.
Deployment and Monitoring:
Deploying the solution on AWS infrastructure, ensuring scalability and reliability.
Implementing monitoring and logging to track performance and address any issues promptly.
Technologies Used
AWS Lex:
Cloud Platforms: AWS
Purpose: Natural language understanding and dialogue management. Handles user interactions and manages the flow of conversation.
AWS Lambda:
Languages: Python
Cloud Platforms: AWS
Purpose: Serverless computing for backend logic and integration. Executes code in response to triggers such as user queries and external API calls.
Amazon EC2:
Cloud Platforms: AWS
Purpose: Deploying FastAPI to handle API requests and responses. Ensures high availability and scalability of the API layer.
FastAPI:
Languages: Python
Libraries/Frameworks: FastAPI
Purpose: A modern, fast (high-performance) web framework for building APIs with Python. Exposes the LLM RAG chain to handle FAQs and dynamic query responses.
Large Language Models (LLMs):
Languages: Python
Libraries/Frameworks: Langchain
Cloud Platforms: AWS Bedrock
Purpose: Enhances dynamic query handling and intent identification. Ensures the IVR system can manage a wide range of customer inquiries accurately.
WhatsApp Business API and Email Services:
Languages: Python,
Purpose: Integrates automated messaging for specific status conditions. Provides timely notifications to customers through preferred communication channels.
AWS CloudWatch:
Cloud Platforms: AWS
Purpose: Monitoring and logging of IVR system performance and interactions. Provides insights into usage patterns and helps in identifying issues.