Businesses are increasingly overwhelmed by inquiries related to their services or products. Relying on human agents to handle inquiries via email results in higher costs and delayed responses, contributing to customer dissatisfaction. In response to these challenges, this pilot study leverages advancements in Large Language Models (LLMs) by proposing a fully automated method for generating a knowledge graph from unstructured data in help pages, which is then utilized to power a fully automated dialogue management system. By transitioning to a chat-based approach, our method aims to handle ambiguous, incomplete, or nonspecific inquiries more effectively and enhance customer satisfaction with tailored, natural responses. We also implement explicit safeguards to improve intent identification and prevent response hallucinations. We validate our proposal in the hotel industry, demonstrating that our knowledge graph based AI agent outperforms the baseline Retrieval-Augmented Generation (RAG) model in accuracy while facilitating more natural and coherent dialogues.
