Customer support teams spend significant time reading and routing incoming requests.
I developed TicketTriage AI to explore how natural language processing can assist this workflow.
The repository documents synthetic-data generation, model training, evaluation, automated testing, Docker configuration, and GitHub Actions.
Macro F1 is used as a primary evaluation metric because performance across different ticket categories matters.
The training dataset is synthetic. Real support conversations contain more variation, ambiguity, and changing language patterns.
The system is intended to assist support agents, not replace human decisions.
GitHub repository: https://github.com/tasnem-tech/ticket-triage-ai I'm particularly interested in improving model confidence and practical human-in-the-loop workflows.