Catch issues before they page you
Unsupervised, seasonality-aware anomaly detection with adaptive thresholds and early-warning forecasting.
Outages don't start with an alert — they start with a drift no one noticed
Static thresholds miss slow degradations and drown on-call teams in noise until it's too late.
Static thresholds miss real drift
Fixed limits ignore seasonality and normal variation.
Alert fatigue burns out on-call
Too many false positives train teams to ignore pages.
Warning comes too late
Breach-based alerting fires only after impact begins.
What it does
How ITMox forecasts and flags anomalies early
Baseline
Model seasonal, per-metric normal ranges from history.
Score
Compare live telemetry against adaptive thresholds continuously.
Forecast
Project trend deviation ahead of a threshold breach.
Correlate
Group related signals into one root anomaly.
Page
Route only confirmed anomalies to on-call, with context.
Who it's for
SRE / Ops
Catch issues before they escalate.
Infrastructure Lead
Fewer surprises, earlier warning.
CIO
Proactive, not reactive operations.
Buyer outcomes
Why teams choose adaptive anomaly detection
Fewer pages, earlier warnings, and confidence that a quiet dashboard means things are actually fine.
Earlier warning window
Forecast-based alerts flag drift before a threshold breach.
Fewer false pages
Adaptive baselines cut noise so on-call trusts every alert.
Less on-call burnout
Confirmed, correlated anomalies replace 3am guesswork.
Works with existing tooling
Layers onto current monitoring — no rip-and-replace.
Catch issues before they page you
Automate this workflow with agentic AI — grounded in your operational data and governed by policy.
Detect Anomalies Early