What's the Most Annoying Part of Incident Response? I Built 5 AI Tools Trying to Solve It

A solo developer from Tumkur, India built five AI-powered incident response tools over three months, including incident triage, pre-mortem scanner, blast radius predictor, post-mortem auto-draft, and on-call handoff briefing. Internal testing shows ~87% root-cause identification accuracy and ~19-second average analysis time. The tools are free to try with no signup required.

Three months ago, I noticed something frustrating. When incidents happen, engineers often spend more time gathering context than actually solving the problem. Logs are scattered. Alerts are noisy. Dashboards multiply. The root cause is usually buried somewhere in the middle. So I started building tools to reduce that friction. Over the last 3 months, as a solo founder from Tumkur, India, I've built five AI-powered incident response tools: 🚨 Incident Triage — identify likely root causes in seconds 🔍 Pre-Mortem Scanner — catch deployment risks before they reach production 💥 Blast Radius Predictor — estimate downstream impact before changes go live 📄 Post-Mortem Auto-Draft — generate incident reports automatically 🔄 On-Call Handoff Briefing — create a concise summary for the next engineer Current results from internal testing: • ~87% root-cause identification accuracy • ~19-second average analysis time • 7 webhook integrations live • Free to try • No signup required Built with Netlify Functions, Supabase, and multiple AI models. I'm still validating whether this solves a real problem for SRE and DevOps teams. If you've handled production incidents before: What is the most annoying part of incident response that nobody seems to solve well? I'd love honest feedback. operatormesh.com