Most of us have been there: It’s 3 AM, there’s an outage, and the #incident channel is exploding with 200+ messages. Once the fix is deployed, the real pain begins—spending 4 hours reconstructing the timeline for the post-mortem.
I built ProdRescue AI to automate this. It’s an incident intelligence engine that correlates technical logs with human context from Slack.
How it works:
Native Slack Integration: Connect via OAuth 2.0. We only access channels you explicitly invite the bot to.
Contextual Correlation: It maps Slack timestamps to log events, identifying not just what failed, but who made which decision and why.
4-Layer Intelligence: We use a pipeline to Sanitize (mask PII), Correlate (logs + chat), Infer (RCA), and Verify (link every claim to a source log line).
Security: We use ephemeral processing. No log retention, no training on your data.
I’m really interested in your thoughts on the "Evidence-Backed" approach. Instead of just generating a narrative, we link every finding to a specific evidence tag ([1], [2], etc.) to eliminate AI hallucinations.
Check it out here: https://prodrescueai.com
Would love to hear your feedback on the Slack-to-Timeline flow!
