Online Chatbot Based Ticketing System

Abstract

In the modern digital era, customer support and service desk efficiency are critical components of organizational success. Traditional manual ticketing systems often suffer from prolonged response times, human errors, and high operational costs due to the sheer volume of repetitive queries. To address these challenges, this paper proposes an Online Chatbot-Based Ticketing System, an intelligent web-based application designed to automate and streamline the customer support workflow. Powered by advanced Natural Language Processing (NLP) techniques and Machine Learning algorithms, the system acts as a first-line virtual assistant capable of understanding user queries, resolving common issues instantly, and automatically generating support tickets for complex issues. The application features an integrated architecture comprising a user-friendly conversational interface, an automated ticket classification module, and an administrative dashboard for support agents. When a query requires human intervention, the chatbot intelligently categorizes and routes the ticket to the appropriate department based on priority and context, significantly reducing manual sorting effort. The system is built using standard web technologies (such as Python, Flask, or Node.js) and integrates robust database management to log user interactions and ticket statuses securely. Empirical evaluations demonstrate that the proposed system drastically reduces ticket resolution times, enhances operational scalability, and improves overall user satisfaction by providing 24/7 instant availability. This solution offers a highly efficient, cost-effective, and scalable framework for modern customer relationship management and enterprise IT support. KEYWORDS-Chatbot, Automated Ticketing System, Natural Language Processing (NLP), Machine Learning, Service Desk Efficiency, Gemini API, Flask, Large Language Model (LLM).