Service scoped
Service-first monitoring
Group operational signals around the services your teams actually own, then review health and risk in that context.
eazeTrace platform
A predictive monitoring platform built around service risk.
eazeTrace helps reliability teams organize current signals, forecasted risk, signal discovery, and trust-aware decisions in one service-first workspace.
Pillar 1Service-first monitoring
Group operational signals around the services each team owns.
Pillar 2Forecast-ready signals
Shape noisy activity into views that support prediction review.
Pillar 3Trust-aware decisions
Keep confidence visible before a forecast becomes action.
From observation to trusted forecast
See a signal move from current behavior to a trust-aware forecast.
A single view connects what a service is doing now, what eazeTrace forecasts next, and whether that forecast is trusted enough to act on.
Latency (p95)312msRising
Observed service behavior over the last 25 minutes.
Observed
Forecast
-25m-15m-5m+10m+30m
-25m-5m+30m
Solid = observed · dashed = forecast horizon
Trusted91%Trust score
Reason · Stable operating regime
Platform overview
Connect service signals to forecast-aware operational decisions.
eazeTrace is not a generic observability dashboard. It is a workspace for teams that need to decide which signals matter, what risk is forming, and whether forecasts are reliable enough to use.
Separated
Current vs forecasted signals
Separate observed behavior from forecasted behavior so teams can compare reality, risk, and confidence clearly.
Risk drivers
Error trends and risk patterns
Understand which APIs, error patterns, and signals are driving risk before teams spend time in the wrong place.
Workspace ready
Signal discovery and onboarding
Turn noisy operational activity into forecast-ready signals that can be reviewed, named, and monitored by the right team.
Eligibility
Model qualification
Evaluate whether trained models are suitable for the modeled signal before they influence a forecast, so a successful training run does not automatically become trusted evidence.
Confidence
Trust-aware decisions
Make forecasts useful by combining qualified models with runtime trust — showing when a prediction can be trusted, when it needs review, and when it should be suppressed.
Team workflow
Workspace collaboration
Give administrators and reliability teams a shared place to manage monitoring setup, users, and operational review.
Service-first monitoring
Organize risk around the services your teams own.
From scattered signals to service-level context
Operational data becomes easier to use when it is tied to the services, APIs, and owner workflows that need attention.
Focus each review around a service and its customer-facing behavior.
Narrow attention to APIs and error trends that contribute to risk.
Prepare monitored signals for forecasting without burying owners in noise.
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.
Trust-aware forecasting
Make forecasts useful by showing when they should be accepted, questioned, or suppressed.
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.
Start with a workspace
Start building a predictive monitoring workspace
Create a workspace and give your team a service-first place to review signals, forecasted risk, and trust-aware decisions.