I built an AI that remembers previous production incidents instead of starting from scratch

Production incidents are inevitable.

Repeated production incidents shouldn't be.

One thing I noticed while working on production workflows is that every incident leaves behind valuable engineering knowledge—but that knowledge often gets buried in Slack conversations, Jira tickets, postmortems, and internal documentation.

When the same issue happens months later, engineers often end up repeating the same investigation.

That led my teammate Harshini Shree and me to build Incident AI.

Incident AI is an AI-powered incident intelligence platform designed to help engineering teams resolve production incidents faster by learning from previous incidents instead of treating every outage as a brand-new problem.

Some of the capabilities we're working on include:

• Persistent memory to recall similar production incidents
• Context-aware recommendations based on previous successful resolutions
• Intelligent AI model routing to balance response quality, latency, and cost
• Continuous learning from engineer feedback after each incident
• Searchable incident history to reduce repetitive troubleshooting

Rather than replacing engineers, the goal is to preserve organizational knowledge and make it instantly accessible during high-pressure incidents.

We'd genuinely love feedback from engineers who work in DevOps, SRE, Platform Engineering, or incident response.

A few questions we're thinking about:

• How would you design long-term memory for incident response systems?
• What information is most valuable during a production outage?
• Would your team trust AI-generated remediation suggestions if they were backed by similar historical incidents?
• What's the biggest pain point with your current incident management workflow?

GitHub:
https://github.com/chs018/Incident-AI

We're looking forward to hearing your thoughts and learning from the community.