SupportOps AI

Inspiration

Support teams often get messy, incomplete, or urgent requests in Slack. It can be hard to quickly understand what the issue is, how serious it is, and what response should be sent. I built SupportOps AI to help teams move faster by turning support messages into clear triage information and draft replies.

What it does

SupportOps AI is a Slack assistant for support operations. A user can submit a support request using /support, and the bot analyzes the message, classifies the issue, estimates priority, and generates a clear draft response. The goal is to help support teams respond faster while keeping messages organized and consistent.

It can also be expanded with /support-summary to generate an end-of-day digest showing common ticket themes, priority trends, and key issues the team should review.

How I built it

I built the project in Python using Slack Bolt for the Slack bot logic. The bot receives slash command input from Slack, sends the request text to Gemini, and returns a structured response back into Slack. I organized the project with separate files for the main bot logic, Gemini API calls, and prompt templates so the system stays simple and easy to expand.

Challenges I ran into

The biggest challenge was keeping the project focused. There are many possible features, such as ticket analytics, dashboards, and long-term reporting, but my main priority was building a clean demo loop first: submit a support request, analyze it, and return a useful response in Slack. Another challenge was designing prompts that produce consistent and practical support replies.

Accomplishments that I'm proud of

I am proud of building a useful Slack-based workflow that solves a real support problem. The project has a simple structure, a clear use case, and room to grow beyond the hackathon. It shows how AI can support teams without replacing human judgment.

What I learned

I learned how to connect Slack slash commands with an AI model, how to structure prompts for support workflows, and how important it is to keep an MVP focused. I also learned that the best AI tools are not just chatbots — they fit directly into where teams already work.

What's next for SupportOps AI

Next, I want to add /support-summary, ticket trend analysis, pattern detection, and summary statistics. I also want to support integrations with ticketing systems, improve priority scoring, and give managers a better view of recurring support issues.

Built With

• github-codespaces
• google-gemini-api
• python
• slack-api
• slack-bolt-for-python