Forecast capacity, correlate alert storms into single incidents, and trigger remediations before tickets file. AIOps that's actually opsable — predictive analysis trained on your stack, not generic averages.
Eight capabilities trained on your infrastructure — every model retrains as your stack evolves.
Forecast CPU, memory, disk, and time-left from historical baselines for every device. See exhaustion windows before they hit.
Root-cause analysis from correlated signals across the stack — see why a system slowed down, not just when.
Per-device CPU, Memory, Free Space, and Time-Left forecasts with anomaly bands. Surface what's about to break.
Collapse alert storms into a single, actionable incident with severity-tiered triggers (Critical, Warning, Information).
Auto-prioritize devices by risk score — memory pressure, CPU saturation, disk fill — so the right ones get attention first.
Runbooks that fire on signal, not on ticket. Auto-remediate known patterns; escalate only the new ones.
Catch the weird before customers do. Self-tuning baselines that adapt to your stack's unique fingerprint.
Track every running app with AI-flagged regressions in latency, error rate, and throughput.
Triage alerts faster with auto-correlation — fewer pages, more remediation, no alert fatigue.
Forecast hardware exhaustion months ahead. Buy what you need, when you need it.
Get root cause without the post-mortem — correlated signals tell the story while the incident is still live.