Customer support systems must process incoming tickets, determine their urgency, assign them to the appropriate teams, and resolve them while meeting service-level expectations. Manual ticket handling can lead to inconsistent prioritization, delayed routing, and unnecessary workload for support personnel. This paper presents TicketFlow Nexus, a proposed AI-assisted framework for intelligent support ticket classification, prioritization, routing, and resolution. The framework combines natural language processing for ticket analysis with a rule-based priority engine, department routing, service-level agreement monitoring, and a controlled AI-assisted resolution workflow. A retrieval-augmented generation component and restricted tool access are proposed to support knowledge-grounded responses, while response validation and human escalation provide safeguards for cases that cannot be resolved reliably through automation. The architecture separates model predictions from operational decisions and workflow state transitions to improve traceability and control. The current work describes the proposed design and implementation approach; quantitative performance claims are deferred until datasets and experiments are available.

Praneet Nagesh Poojary, Sumeet Sudhir Tawte, Pranay Dhirendra Pandey, Aditya Dinesh Gawade, "TicketFlow Nexus: A Risk-Aware AI Framework for Intelligent Support Ticket Classification, Prioritization, Routing, and Resolution" International Journal of Computer Science and Engineering Development, V1(4): Page(176-181) September - October 2026. ISSN: 3139-0862. www.ijcsed.com. Published by Scientific and Academic Research Publishing.