ENTERPRISE RAG-BASED INTELLIGENT CUSTOMER SUPPORT ASSISTANT

Abstract

Customer support is an essential part of modern enterprises because customers expect quick, accurate, and personalized responses to their questions. Organizations commonly maintain large collections of product manuals, frequently asked questions, service policies, troubleshooting guides, and internal support documents. Finding the correct information from these resources manually can take considerable time and may result in inconsistent responses. The proposed Enterprise RAG-Based Intelligent Customer Support Assistant is an AI-powered platform designed to provide accurate and context-aware responses to customer queries. The system uses Retrieval-Augmented Generation (RAG) to retrieve relevant information from authorized enterprise knowledge sources before generating an answer. This approach allows the assistant to provide responses based on current organizational information rather than relying only on the language model's general knowledge. The system processes enterprise documents through document extraction, cleaning, chunking, embedding generation, and vector indexing. When a customer submits a question, the query is converted into a semantic representation and compared with the indexed knowledge base. Relevant documents or content sections are retrieved and supplied as context to the Generative AI model, which produces a natural-language response. The platform can also provide source references, confidence indicators, conversation history, escalation mechanisms, and feedback collection. When the system cannot confidently answer a question, it can recommend transferring the conversation to a human support representative. This helps reduce unsupported responses while ensuring that complex customer issues can receive appropriate human attention.