Rootwatch
AI-driven anomaly detection designed to predict failures before they cascade — then stage the fix. Auto-scaling, canary rollbacks and cost sweeps, executed automatically.
From alerting to acting.
Predictive anomaly detection
Learns each service's normal across metrics, traces and logs — flags trajectory, not just thresholds.
Automated remediation
Staged runbooks execute on prediction: scale, rollback, restart, reroute — with human approval gates where you want them.
Early warning
Designed to flag failures before they reach customers — enough lead time for the fix to be boring.
Cost automation
Idle capacity reclaimed nightly, commitments tuned monthly — savings reported per service, per team.
Incident intelligence
Every event correlated into a single timeline with root-cause hypothesis — postmortems half-written by the time you open them.
Your cloud, your rules
Deploys into AWS, Azure or GCP with policy-as-code boundaries on what automation may touch.
Built for fast onboarding.
Instrument
Agents and integrations deploy via Helm or Terraform — Rootwatch baselines your services in 24 hours.
Predict
Anomaly models come online per service; predictions run shadow-mode first so you can verify lead time and accuracy.
Automate
Approve runbooks per class of incident — remediation goes from suggested to automatic at your pace.