Observe
See what is happening now
Keep current service signals, error trends, and risk patterns visible without blending them into forecasted behavior.
Predictive monitoring
Predict service risk before it becomes an incident.
eazeTrace helps teams connect current service signals, forecasted behavior, and trust-aware decisions so monitoring becomes proactive instead of reactive.
NowCurrent signals
See observed service behavior before reviewing forecasted movement.
NextForecasted risk
Use qualified models to understand which services and signals may need attention soon.
DecisionTrust-aware action
Use confidence and reason context before teams act.
The product at a glance
Current signal, forecast, and trust — in one workspace view.
eazeTrace keeps observed behavior, the forecast horizon, and the trust decision connected, so teams can read risk and confidence together instead of stitching them across tools.
Errors now12/5m
Forecast · 30 min17errors
Trust score91%
Observed
Forecast
-25m-15m-5m+10m+30m
-25m-5m+30m
Prediction accepted
Observed errors lead into the forecast horizon; qualified models agree and the operating regime is consistent with training behavior.
From reactive dashboards to predictive decisions
Monitoring teams need more than another wall of charts.
eazeTrace is built for teams that need to understand what is happening, what may happen next, and when a forecast deserves confidence.
The problem eazeTrace is designed to solve
Reactive monitoring can show symptoms after they appear. eazeTrace helps teams move earlier by connecting service signals, anomaly patterns, and forecast trust into a decision-ready workflow.
Noisy alerts make it hard to separate weak signals from real risk.
A successfully trained model is not automatically one worth trusting.
Forecasts without confidence context can create false certainty.
Product value
See now, forecast next, decide with trust.
Forecast
Understand what may happen next
Turn service activity into forecast-ready views built from qualified models, so teams can spot operational risk before it becomes an incident.
Trust
Know whether to use the forecast
Confidence, reason codes, and suppression signals help teams accept, question, or hold back a prediction.
Model qualification
Not every trained model earns a place in a trusted forecast.
Before models take part in trust-aware forecasting, eazeTrace checks whether they are suitable for the modeled signal. Training creates candidates; qualification determines which models are eligible to influence trusted forecasts.
Candidate
Training creates candidates
A model that trains successfully is a candidate — not automatically a model that should influence a trusted forecast.
Qualified
Qualification decides who participates
eazeTrace evaluates whether each model is suitable enough for the modeled signal before it can shape a trusted forecast.
Suppression
Weak evidence is held back
When no model qualifies, eazeTrace can withhold the forecast instead of presenting a number with false confidence.
Trust-aware forecasting
Predictions are useful only when teams know how much confidence to place in them.
eazeTrace makes model qualification and forecast trust part of the workflow, not a hidden assumption.
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.
How it works
A simple path from service signals to proactive reliability.
- 01
Onboard service signals
Group operational signals around the services your teams own and the workflows they protect.
- 02
Separate current from forecasted
Keep observed behavior clear while forecasted views show where risk may be moving next.
- 03
Review trust context
Use confidence, signal quality, and reason codes before turning a prediction into action.
Current signalsObserved service behavior
Teams can see the signal state that exists right now.
Forecasted signalsLikely next behavior
Forecasts stay separate from observed data so decisions stay clear.
Platform capabilities
Built around service risk, signal quality, and confidence before action.
Service first
Predictive service monitoring
Monitor service health through current signals and forecasted risk without losing the difference between the two.
Onboarding
Signal discovery
Identify important operational patterns and turn noisy activity into signals your workspace can use.
Risk patterns
Error intelligence
Surface the error patterns, categories, and trends that are most likely to affect reliability decisions.
Trust-aware
Trust center
Review accepted, questioned, and suppressed forecasts with clear confidence context and reason-code visibility.
Use cases
Practical workflows for teams that own reliability.
API reliability teams
Connect service and API scoped signals to forecasted risk so teams can prioritize the paths customers rely on.
SRE and operations
Reduce noisy monitoring reviews by focusing attention on risk patterns with enough confidence to act.
Release readiness
Watch service behavior after change windows and decide whether forecasts support rollout confidence.
Start with a workspace
Create your eazeTrace workspace
Start building a predictive monitoring workspace where current signals, forecasted risk, and trust-aware decisions stay connected.