Onboard
A signal becomes a monitored view
Teams identify a service signal, name it clearly, and make it part of a workspace review flow.
How it works
From service signals to trusted operational decisions.
eazeTrace gives teams a simple workflow for turning current behavior, emerging patterns, and forecast confidence into proactive monitoring decisions.
StartOnboard signals
Bring service behavior into a workspace teams can review.
ShapeQualify models
Check which trained models are suitable before they shape a forecast.
DecideAct with trust
Review confidence before moving forecasted risk into action.
Discover → Forecast → Decide
The same three stages, shown in the product.
Discover the signals worth monitoring, forecast what comes next while keeping observed and forecasted data separate, then decide with trust context attached.
- ErrorsError signal
- Latency (p95)Latency signal
- Request volumeTraffic signal
Observed
Forecast
-25m-15m-5m+10m+30m
-25m-5m+30m
Observed stays separate from the forecast horizon.
- Forecast
- 17 errors expected
- Trust
- High
- Confidence
- 0.91
- Decision
- Prediction accepted
- Reason
- Stable operating regime
Workflow
From noisy signals to qualified, trusted, confident action.
- 01
Discover signals
Start with the services your teams own, then find the operational signals — error trends and anomaly patterns — that deserve monitoring attention.
- 02
Build candidate models
Train predictive models using the selected target and input signals. A model that trains successfully is a candidate, not yet a trusted contributor.
- 03
Qualify models
Evaluate whether trained models are suitable enough to participate in trusted prediction workflows. Only qualified models move forward.
- 04
Forecast what comes next
Use qualified models and forecast inputs to estimate future target behavior, keeping current signals separate from forecasted ones.
- 05
Evaluate runtime trust
Assess the trust evidence available now — forecast behavior, qualified-model disagreement, thresholds, and reason codes — before a prediction becomes a decision.
- 06
Act with context
Surface trusted forecasts, warnings, or suppression so teams know not only what is predicted but whether it deserves action.
Example journey
Every forecast should arrive with context.
The workflow is designed to keep teams from treating prediction output as certainty. Forecasts become decision candidates only when the trust context supports action.
Qualify
A candidate model earns qualification
The model is evaluated for suitability before it can influence a forecast. Qualified models participate; rejected ones do not.
Act
A team chooses the next action
Forecasts built from qualified models and backed by runtime trust move into operational decisions. Unreliable ones are questioned or suppressed.
Trust-aware decisions
Accept, question, or suppress based on confidence.
Confidence is strong enough for the team to use the forecast.
The forecast needs review because signal quality or pattern stability changed.
Unreliable predictions stay out of the action queue until trust improves.
Team workflow
A workspace for proactive reliability work.
Built for the way reliability teams make decisions
eazeTrace helps teams align around the signals that matter, the services at risk, and the forecasts that deserve action.
Service owners see risk in the context of the systems they support.
Operations teams can reduce noise by focusing on confidence-backed changes.
Workspace users share a consistent view of signal quality and forecast posture.
Start with a workspace
Start building a predictive monitoring workspace
Create a workspace where service signals, forecasted risk, and trust-aware review become part of the same reliability workflow.