An AI-Powered Multi-Provider Customer Support Chatbot System with Intelligent Query Routing and Retrieval-Augmented Generation

The rapid advancement of Large Language Models (LLMs) has created unprecedented opportunities to automate enterprise customer support operations. However, existing deployments relying on a single LLM provider suffer from vendor lock-in risks, cost inefficiencies, and capability limitations. This paper presents the design, implementation, and empirical evaluation of an AI-Powered Multi-Provider Customer Support Chatbot System that integrates OpenAI GPT-4 Turbo, Google Gemini 1.5 Pro, and Anthropic Claude 3.5 Sonnet within a unified provider abstraction framework. An intelligent query routing engine dynamically selects the optimal provider based on query complexity, sentiment analysis, budget constraints, and real-time provider health metrics. A Retrieval-Augmented Generation (RAG) pipeline implemented using LangChain and FAISS grounds all responses in a proprietary knowledge base, reducing hallucination rates and improving factual accuracy. The system is deployed on AWS using a containerised microservices architecture. A two-week production trial demonstrates: average response latency of 1.8 seconds (57.1% improvement over baseline), intent classification accuracy of 94.3% (+18.1 pp), customer satisfaction score improvement of 37.5%, Tier-1 escalation rate reduction of 50 percentage points, 99.94% system uptime, and 26.6% reduction in monthly API costs. These results validate the multi-provider paradigm as a significant architectural advancement for enterprise conversational AI deployments.Keywords—Chatbot, Large Language Model, Multi-Provider AI, Retrieval-Augmented Generation, FastAPI,Query Routing, Customer Support Automation, RAG, FAISS, LangChain, AWS Microservices