IT Incident Auto-Responder

The IT Incident Auto-Responder is an AI-powered assistant that automatically triages incidents, retrieves similar past cases, generates an action plan, and posts results to Slack.

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🌟 Inspiration

Every engineer knows the feeling: an alert goes off, dashboards light up, and suddenly you’re in panic mode trying to fix an incident. Most of the time is wasted searching through old tickets, chat logs, or manuals. We wanted to build something that could cut through the noise and give engineers an immediate, structured plan — so they can act faster and with more confidence.

That’s where the idea for the IT Incident Auto-Responder came from.

🛠️ How We Built It

We combined three powerful pieces:

TiDB Serverless with Vector Search – we ingest historical tickets and manuals, embed them into vectors, and store them in TiDB. This lets us quickly retrieve the most relevant past cases.

LLM Planning Agent – once we have context, we call GPT to generate a structured JSON plan with steps, risks, and confidence.

Slack Integration – the plan is posted directly to Slack, so the team sees it right where they already collaborate.

We also persist every run in TiDB with metadata and confidence scores, so we can replay incidents later or audit past decisions.

🚧 Challenges

Vector search quirks: making sure our embedding parameters and queries matched TiDB’s expectations was trickier than expected. Sometimes results came back empty until we debugged the way vectors were passed.

Data simulation: we didn’t have access to real incidents, so we generated synthetic tickets and combined them with PDFs as a proof of concept.

Integration juggling: connecting OpenAI, TiDB, Slack, and OCR together in a single automated flow stretched our debugging skills — but it was worth it when everything finally clicked.

📚 What We Learned

How to chain multiple AI steps into a single workflow, not just one-shot Q&A.

How vector search + metadata filters can make retrieval more precise and trustworthy.

That even a simple UI + Slack notifications can make AI output much more actionable for real teams.

In the end, we learned that it’s not about building the fanciest model — it’s about delivering the right information at the right time to the people who need it.

Built With

  • javascript-(vanilla)-frameworks:-fastapi
  • slack-sdk
  • tailwind-css-cloud-&-database:-tidb-serverless-(vector-search-+-sql)-apis-&-tools:-openai-(gpt-+-embeddings)
  • tesseract-ocr-other:-vs-code
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