AI-Driven Customer Support for a Leading Telecommunications Company

About the Client

The client, a leading American Telecommunications Company, provides a range of services, including internet, mobile, TV, and bundled solutions to millions of customers across the United States. To improve customer support, optimize sales processes, and enhance operational efficiency, the client sought an AI-powered solution capable of handling complex customer queries, automating key business processes, and providing data-driven insights.

Requirements

• Enhance Customer Support: Automate responses to customer queries with an LLM-powered chatbot for faster issue resolution.
• Automate Business Processes: Enable reconciliation, product provisioning, and cross-selling to optimize operational workflows.
• Data-Driven Analytics: Provide actionable insights into invoices and purchases using AI-driven analytics.
• Scalability and Performance: Leverage high-performance infrastructure to deliver low-latency, real-time responses, even during high traffic.

Solution

Enshire designed and implemented two AI-driven customer support agents:

• LLM-Powered Chatbot: Built on OpenAI’s Large Language Model, the chatbot serves as the primary interface for customer interactions. The chatbot:
  • Handles general inquiries (e.g., billing issues, plan details, upgrade options).
  • Personalizes responses based on the customer's purchase history and bundled services.
  • Performs automated workflows like cross-sell recommendations and invoice analytics.
• RAGbot (Retrieval-Augmented Generation Bot): For queries requiring deeper insights or precise data points, the RAGbot leverages Retrieval-Augmented Generation (RAG) to combine generative AI with dynamic data retrieval. Integrated with Chroma, a vector database, the RAGbot fetches relevant customer data, service information, and historical records to deliver contextually accurate responses.

Benefits

• Faster Customer Query Resolution: The AI-powered agents reduced average response time by 65%, ensuring real-time query handling and issue resolution.
• Increased Operational Efficiency: Automated reconciliation, provisioning, and cross-selling workflows resulted in a 40% reduction in manual intervention and errors.
• Improved Customer Experience: Personalized, context-aware responses led to a 30% increase in customer satisfaction scores (CSAT).
• Enhanced Revenue Opportunities: Cross-selling capabilities increased bundle adoption rates by 18%, driving additional revenue.
• Scalability and Reliability: The AWS-based infrastructure ensured 99.9% uptime and seamless scaling during peak usage, improving system reliability.

Technologies Used

• AI/ML: OpenAI, Retrieval-Augmented Generation (RAG).
• Database: Chroma (Vector Database).
• Programming: Python, PySpark.
• Frontend: React.js.
• Cloud: AWS with AI 100 GPU Configuration.
• ERP Integration: APIs for backend systems.