An integrated complaint management system based on large language models: Case study in the electric sector

Abstract

Efficient complaint management is essential for service companies aiming to maintain high levels of customer satisfaction. Traditionally, complaint resolution has relied on human analysts performing manual processes, which are time-consuming and error-prone. As an alternative, rule-based systems have emerged as an initial step toward automation, seeking to make complaint resolution a reliable process independent of analysts’ judgment. However, these systems are often inflexible and limited in understanding and responding to complex, unstructured customer feedback, making analysts’ interventions unavoidable. In this context, recent advancements in natural language understanding by large language models (LLMs) have opened new opportunities for developing complaint management systems that leverage the capabilities of LLMs to classify, prioritize, and resolve complaints with minimal human intervention. This work addresses this opportunity by creating an integrated complaint management system, using complaints in the electric sector as a case study. Based on a service-oriented architecture, the proposed system integrates a data processing microservice, an artificial intelligence module deploying LLM-based virtual analysts, and a user-friendly web application. Extensive evaluations, including load testing and latency analysis, demonstrate the system’s performance, robustness, and scalability.