Challenge
A SaaS platform scaling to 50K+ users was drowning in operational toil. The
on-call team handled 200+ alerts per week, most of which were false positives
or issues with known remediation steps. Incident response was manual, slow,
and burning out senior engineers.
What We Did
Deployed ML-driven anomaly detection to cut alert noise by 80%. Built
autonomous remediation agents that handle common failure modes (disk
pressure, pod evictions, certificate renewals, and connection pool exhaustion) without
human intervention. Implemented LLM-powered runbooks that guide on-call
engineers through unfamiliar incidents with context-aware troubleshooting.
Outcome
Actionable alerts dropped from 200+ to under 30 per week.
Mean time to resolution fell by 70%. Two senior engineers moved from
firefighting back to product development. The system now self-heals
through 85% of previously manual incidents.