Online Chatbot-Based Ticketing System

Abstract: Efficient incident response and customer query resolution are critical challenges in modern IT service management and enterprise operations. Although conventional ticketing platforms exist, necessary diagnostic context and initial troubleshooting steps are often scattered across static FAQs, user manuals, and delayed manual response queues. This leads to extended resolution times, high ticket backlogs, and suboptimal user satisfaction. The purpose of this research is to study and propose an online chatbot-based ticketing system that automatically processes preliminary inquiries, creates structured support tickets, and intelligently routes complex issues to appropriate human support personnel.

The research examines existing literature, frameworks, and methodologies related to Natural Language Processing (NLP), Natural Language Understanding (NLU), and automated helpdesk workflows. The proposed platform combines real-time conversational AI assistance with automated ticket categorization, priority tagging, status tracking, and basic automated self-service resolution workflows.

The expected outcome is a user-friendly, highly scalable, and accessible customer support framework that significantly reduces average resolution time and operational overhead. Automating routine service requests frees human engineers to focus on critical technical tasks, optimizing overall organizational workflow and operational productivity.

Keywords: Chatbot, Conversational AI, Natural Language Processing (NLP), Information Technology Infrastructure Library (ITIL), Incident Management, Ticket Categorization.