Enhancing Customer Support with AI: Real-time NLP for Ticket Classification and Automated Responses

Abstract

Artificial intelligence (AI) integration into customer support systems is becoming more and more important for companies trying to keep up with the ever-increasing needs of their customers in today's quickly changing customer care industry. In order to enhance automated response generation, this research suggests a novel hybrid framework that combines the potent powers of a Reinforcement Learning (RL) agent with a BERT-based classifier for real-time ticket categorization. Improving customer support interactions' accuracy, speed, and general efficiency is the main goal. By utilizing feedback-driven learning and Natural Language Processing (NLP), the system tackles two major issues: delayed responses and incorrect ticket routing, resulting in a considerable improvement in service quality. A custom dataset that was gathered from Hugging Face and included a range of customer requests from different industries was used to train the model. Utilizing statistical methods like confidence intervals and t-tests, the research verifies the noteworthy enhancements in performance of the suggested model in comparison to current solutions. According to the research, AI-driven hybrid models can revolutionize customer assistance by increasing user satisfaction, decreasing operating expenses, and increasing issue resolution accuracy. This study establishes the foundation for customer service solutions that are more responsive, flexible, and scalable, highlighting AI as a major force behind client engagement in a variety of industries going forward.