title: "LazyTicket AI: Automate Support Tickets with Open-Weight AI"
published: false
tags: devchallenge, hf26challenge, opensource, python
This is a submission for the [Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady].
What I Built
🎫 LazyTicket AI — Stop managing tickets. Let AI do the busywork.
Support teams spend valuable time reading tickets, identifying issues, assigning priorities, summarizing complaints, and drafting responses.
I built LazyTicket AI to automate these repetitive tasks using open-weight AI. Instead of processing tickets manually, users can process a batch with a single action.
Key features include:
- AI classification: Categorizes tickets into bugs, billing, account issues, technical support, and general inquiries.
- Smart prioritization: Assigns priority levels based on ticket content.
- Sentiment analysis: Identifies customer frustration and emotional tone.
- Automatic summaries: Converts lengthy descriptions into concise summaries.
- Response drafting: Generates suggested replies for human review.
- Duplicate detection: Identifies potentially duplicated tickets using text similarity.
- Smart routing: Recommends the appropriate support team.
- Dashboard and CSV support: Tracks processing results and supports bulk ticket import and export.
The goal is simple: spend less time organizing tickets and more time solving problems.
Demo
🎬 Project demonstration
The demonstration will show the ticket dashboard, batch processing, AI-generated results, and exported ticket data.
Code
💻 GitHub Repository:
https://github.com/banthevaishnavi/lazyticketai
The project is built with Python and Streamlit, with SQLite for persistent storage and an open-weight AI model for ticket analysis.
How I Built It
I built LazyTicket AI using Python, Streamlit, SQLite, Pandas, and scikit-learn.
The AI component uses Gemma, an open-weight model, through a configurable inference provider. The application sends ticket descriptions for analysis and uses the returned results to classify tickets, determine priority, analyze sentiment, generate summaries, recommend support teams, and draft responses.
SQLite stores ticket information and processing results. TF-IDF and cosine similarity help identify potentially duplicated tickets.
The workflow is straightforward:
- Import or select support tickets.
- Run the automation.
- Review the generated classifications, summaries, priorities, and responses.
- Edit results when necessary.
- Export the processed tickets.
The application is designed to keep humans in control of important decisions. Generated responses remain drafts rather than being sent automatically.
Why Does Open Innovation Matter?
Open-weight AI gives developers more flexibility in how they build and operate AI-powered applications.
For LazyTicket AI, it provides several advantages:
- Model flexibility: The AI component can be adapted to compatible open-weight models.
- Reduced API dependence: The project is not inherently tied to a single proprietary model provider.
- Cost awareness: Open-weight models can reduce per-request inference costs, depending on how they are hosted.
- Greater control: Local inference can help keep ticket data on the user's machine.
- Learning and experimentation: Developers can modify prompts and experiment with different models.
My implementation uses a configurable inference provider, so privacy and cost benefits depend on the selected deployment. Hosted inference sends ticket data to the selected provider, whereas local inference can keep processing on the user's machine.
Open innovation makes it easier to experiment, adapt, and build practical automation without locking the entire application to one closed AI ecosystem.
My Agent Session
Optional: Add a DevRelay agent session here if available.
The Lazy Payoff
Imagine manually processing 20 support tickets. Each one requires reading the description, identifying the issue, deciding its urgency, summarizing it, and preparing a response.
LazyTicket AI brings these steps into one batch-processing workflow.
Instead of repeating the same administrative tasks for every ticket, a support worker can review AI-generated results together and focus on cases that need human attention.
The actual time saved depends on ticket complexity, model latency, and the amount of review required. Performance claims should be based on measured results rather than unverified estimates.
Built with Python, Streamlit, SQLite, and open-weight AI.
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