Tech / IT Support · intermediate
AI-Powered IT Support Ticket Automator
A Python system that uses an LLM to auto-categorize, prioritize, and draft responses for incoming IT tickets.
Project overview
IT support is moving from manual triage to AI-assisted automation. This project proves you can leverage AI to increase help desk efficiency, reduce response times, and handle high-volume ticket environments — skills highly valued by modern enterprises.
Real-world use case
A mid-sized tech company receiving 500+ tickets daily uses this tool to instantly route critical server issues to the DevOps team while auto-responding to common password reset requests.
Tools and technologies
- Python
- OpenAI API
- FastAPI
- Jira/Zendesk
Skills you can demonstrate
NLP, API Integration, Prompt Engineering, Python
Build roadmap
- Set up your environment
Install Python, create a virtual environment, and install openai, pandas, and flask. Get an API key from OpenAI or Anthropic. - Create a mock ticket dataset
Generate 30–50 fake IT tickets in a CSV (columns: ticket_id, description, user). Use AI to help create variety — password resets, VPN issues, hardware failures. - Build the classifier function
Write a Python function that sends each ticket description to the LLM and returns JSON with category, priority, and suggested_fix. Use the prompt provided below. - Wrap it in a Flask API
Create a /classify endpoint that accepts a ticket and returns the classification. Add a simple /dashboard route that shows processed tickets. - Add the "time saved" metric
Track how long each classification takes vs. a manual baseline (~5 min/ticket). Display the total saved on your dashboard. - Record a demo + write the README
Screen-record the tool classifying 10 tickets in under 30 seconds. Write a clear README with setup instructions and a GIF of the demo.
Ways to extend the project
Add a "Self-Healing" feature where the AI generates a specific PowerShell or Bash script to fix the issue (e.g., clearing a full disk) and offers it to the technician for one-click execution.
How to showcase your work
- GitHub: Repository with clear README, sample ticket data, and a demo video of the AI classifying tickets.
- LinkedIn: Post a screen recording of the tool in action, highlighting the "Time Saved" metric.
- Resume: List under "Projects" with focus on "AI-driven automation" and "Efficiency gains".