Aggregate alarms from AlertManager, CloudWatch, SNMP, syslog and PagerDuty. Claude triages them with your runbook knowledge base and proposes remediation. Humans approve risky actions in Slack; safe ones run automatically.
A canned scenario — 47 raw alarms arrive across 4 channels in 8 seconds. Watch the AI dedupe, correlate, and propose remediation. No real systems are touched.
Every alarm gets normalized, deduped, analyzed by Claude with RAG over your runbooks, and routed to the right action.
AlertManager webhooks, SNMP traps, syslog, CloudWatch, PagerDuty, custom REST. Same pipeline, one schema.
Burst detection collapses storms. 47 alarms become 3 root incidents. No more duplicate pages.
Claude queries your runbook knowledge base (ChromaDB + embeddings) before proposing actions. Context-aware, not pattern-matched.
Restart pods, scale deployments, clear caches — confidence > 0.85 auto-runs. Risky stuff (rollbacks, SSH commands) waits for Slack approval.
Approval requests post to Slack with full context. One-click approve/reject. 30-minute timeout falls back to escalation.
K8s API, Ansible runner, SSH/CLI for legacy. Modern cloud-native and on-prem in the same pipeline.
30-minute walkthrough with our team. Bring a real on-call incident from last week — we'll show you what NOC AI Operator would have done.