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Faster Response
Lower MTTR
Fewer Escalations
A fast-growing SaaS company with 50+ microservices was drowning in alerts from Prometheus, Datadog, and various monitoring tools. Their DevOps team of 8 engineers was spending nights and weekends manually triaging incidents and executing runbook procedures.
Alerts often went to the wrong team, critical context was missing, and runbook execution was inconsistent. The result: high MTTR, frequent escalations, and burned-out on-call engineers.
We built an n8n-powered incident management platform that ingests alerts from all monitoring tools, enriches them with context, intelligently routes to the right team, and automatically executes runbook procedures.
Parse alerts from any source, determine severity and service ownership, route to correct squad/channel with full context and on-call schedules.
Auto-attach recent logs, traces, last deployment info, related metrics, and similar past incidents to every alert for faster diagnosis.
Trigger automated remediation actions: service restarts, rollbacks, traffic shifting, feature flag toggles via APIs without human intervention.
Auto-create Jira tickets with timeline, metrics, and logs. Generate post-mortem draft with complete incident data for review.
Faster Alert Acknowledgment
From 45 min to 9 minReduction in MTTR
From 3 hours to 1.2 hoursFewer Wrong Escalations
Right team, first timeComplete Post-Mortem Drafts
Auto-generated with dataDowntime Reduction: $400,000 annual savings from faster incident resolution
Team Productivity: 30+ hours per week saved on manual incident handling
On-Call Quality of Life: 65% reduction in burnout scores, 40% fewer after-hours pages
Incident Intelligence: Complete data-driven post-mortems for continuous improvement
Webhooks from Prometheus, Datadog, Grafana, etc. trigger workflows. Alerts normalized to common format with severity, service, and metadata.
Auto-fetch recent logs from Loki, traces from Jaeger, deployment history from CI/CD, and similar past incidents from knowledge base.
Determine service ownership, check on-call schedules, route to correct Slack channel and PagerDuty escalation policy with enriched data.
For known issues, execute runbook automatically: restart pods, rollback deployments, toggle feature flags, scale resources via Kubernetes API.
If auto-remediation fails or manual review needed, create incident war room, invite experts, provide debugging links and dashboards.
Auto-create Jira ticket with full timeline, metrics, and affected services. Generate post-mortem template with action items and incident data.
Let's discuss how n8n automation can transform your incident response and reduce on-call burnout.
Implementation Time
ROI Timeline
MTTR Improvement
Let’s discuss how we can help you reduce MTTR and improve your DevOps efficiency.
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