🧠 AI-Powered Ticket Routing & SLA Breach Prediction in JIRA using Transformers – Feedback & Suggestions?

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Hi :hugs: community,

I’m excited to share an applied NLP project I recently open-sourced:
:link: GitHub: AI-Powered Ticket Routing & SLA Breach Prediction in JIRA

:puzzle_piece: What It Does:
This tool uses transformer-based models (fine-tuned on internal support data) to:

  • Predict SLA breach probability for incoming tickets.
  • Automatically classify and route tickets based on historical handling patterns.
  • Generate alerts for high-risk cases using a trained classifier.

It’s designed for real-time integration into JIRA environments, especially helpful for large support teams under SLA pressure.

:gear: Tech Stack:

  • Hugging Face Transformers (bert-base-uncased)
  • Scikit-learn for baseline models
  • Pandas, FastAPI, JIRA REST API
  • Model interpretability with SHAP

:pushpin: What I’m Looking For:
I’d love to get feedback from the community on:

  • Better NLP architectures for short, noisy, multilingual support tickets
  • Suggestions for incorporating zero-shot classification (e.g., facebook/bart-large-mnli)
  • Ideas for improving label imbalance in ticket outcomes
  • Ways to optimize model latency for real-time usage in MLOps pipelines

:brain: Bonus:
If you’ve worked on similar use cases in ITSM, support automation, or task classification, I’d love to connect and hear your thoughts!

Thanks for checking it out — and feel free to :star: the repo if you find it useful!
Looking forward to your insights :folded_hands:

— Arooj Javed

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