AI-Powered Customer Support Automation: Transforming Ticket Creation and Management.
Mohammad Travadi, Greeshma Reddy, Mubarak, Vignesh G, Mohammed Faizan, Dr Syed Siraj Ahmed
Computer Science & Engineering/Student, Presidency University, Bengaluru, India Computer Science & Engineering/Student, Presidency University, Bengaluru, India Computer Science & Engineering/Student, Presidency University, Bengaluru, India Computer Science & Engineering/Student, Presidency University, Bengaluru, India Computer Science & Engineering/Student, Presidency University, Bengaluru, India Computer Science & Engineering/Faculty, Presidency University, Bengaluru, India
Abstract— The increasing frequency of customer inquiries and complaints in the digital era has put a strain on traditional support systems, resulting in inefficiencies and delayed responses. This paper describes an artificial intelligence- driven ticket automation system that uses advanced Natural Language Processing (NLP) techniques to accelerate ticket production, classification, and resolution. Using frameworks like LangChain, LangGraph, and Retrieval-Augmented Generation (RAG), the system automates operations, increases response accuracy, and interacts smoothly with current support structures. The methodology entails preparing client queries for ticket classification, prioritizing, and multilingual translation, with Large Language Models (LLMs) fine-tuned for typical support scenarios. The system also accepts unsupervised queries and uses parallel processing to manage numerous tickets simultaneously, increasing throughput.
Evaluation indicators, such as efficiency gains (targeted at 10%) and response accuracy, demonstrate the system's real- world usefulness. The key findings reveal that manual classification errors are eliminated, and complicated or multilingual requests receive faster responses. However, issues remain in addressing ambiguous inquiries and maintaining broad AI compatibility, indicating areas for future improvement. Ongoing work will strengthen categorization algorithms, broaden industry support, and incorporate advanced sentiment analysis to improve issue priority.
Key Words: customer support, ticket automation, natural language processing, ai integration, multilingual support, workflow optimization.
