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A production-ready AI-based solution to predict SLA breaches in customer support workflows using JIRA ticket data. Built to help teams detect bottlenecks, automate triage, and escalate intelligently — before it's too late.
This project is based on the IJERT paper titled:
“Optimizing SLA Compliance in Technical Support Using AI-Based Automation: A Practical Implementation in JIRA Workflows”
📄 Read the Paper (IJERT Submission) (link to be updated after acceptance)
- 🔮 Machine Learning model to predict SLA breaches
- 🧠 Data-driven insights into resolution performance
- 📈 Visual charts for breach rate, agent metrics, and categories
- ⚙️ Flask API for dynamic predictions
- 🗃️ Synthetic or exported ticket data supported
- Python, Flask, Pandas, Scikit-learn
- Plotly (for interactive dashboards)
- HTML (basic frontend)
- CSV / JSON for data inputs
git clone https://github.com/aroojjaved93/ai-sla-predictor-jira-support.git
cd ai-sla-predictor-jira-support
pip install -r requirements.txt
python backend/app.py
Then open the frontend/index.html to view dashboards locally.
Use /data/sample_sla_data.csv as a starting point.
Love what this project is doing? Help us grow:
- ⭐ Star this repo to show support
- 🍴 Fork it and enhance the features
- 🐛 Submit issues or pull requests
MIT License © Arooj Javed — 2025
Built for real-world impact in technical support workflows 🚀
