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I made an AI agent that helps DevOps teams resolve incidents 60% faster

Hacker News

I made an AI agent that helps DevOps teams resolve incidents 60% faster

Hey HN, We built PlatOps to help platform teams resolve production incidents faster using AI. Our AI agent either auto-resolves incidents using workflows learned from your past incidents or guides your on-call engineers through resolution steps. A common problem we hear from SRE teams is that incident resolution is still largely manual and relies heavily on tribal knowledge. Engineers waste precious time gathering information from multiple sources during incidents, and learnings from past incidents often stay trapped in postmortem docs. PlatOps solves this by: 1. Automatically gathering relevant information (logs, metrics, docs) when an incident occurs 2. Learning from your team's past incident resolutions to suggest or automate fixes 3. Providing AI-guided resolution steps when manual intervention is needed Teams using PlatOps have seen: - 60% reduction in Mean Time To Recovery (MTTR) - Faster onboarding of new on-call engineers - Better knowledge retention from past incidents We're looking for feedback from the HN community, especially from folks who manage production systems. Would love to hear your thoughts on: - Current pain points in incident management - Features you'd like to see - Integration suggestions Try it out at https://docs.platops.ai

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, new, using · Missing: mac, agents, macos
95%95% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
68%68% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
46%46% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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