Prediction workflows with built-in trust decisions.
Run documented workflows for classification, regression, continuous prediction, and time-series forecasting. Where supported, review model outputs alongside errors, residuals, range checks, disagreement, uncertainty, and trust decisions.
Responses can include model outputs, diagnostics, warnings, and trust information.
Inspect prediction results alongside uncertainty, disagreement, and range checks when the workflow returns them.
Use model guides, API references, and the simulator to examine supported paths.
Developer example
See the prediction and the decision behind it
A simulator training request returns the AIML result plus supported trust-summary diagnostics.
POST /api/v1/train/simulator/random-forest
{
"customerKey": "eazepredict-demo",
"featureModel": "expenseForecastDemo",
"subFeatureModel": null,
"startDate": "2026-05-01T00:00:00",
"timeSeriesType": "h",
"timeSeriesFrequency": 1,
"train": 70,
"featureOutput": {
"label": "predicted_expense",
"output": [4200, 4350, 4520, 4680, 4800, 4970]
},
"externalFeatures": [
{
"label": "usage_volume",
"output": [900, 960, 1010, 1075, 1120, 1185]
},
{
"label": "unit_cost_cents",
"output": [450, 452, 455, 457, 460, 462]
}
],
"n_estimators": 100,
"max_depth": 6
}{
"simulator": true,
"operation": "train",
"modelType": "random-forest",
"status": "success",
"result": {
"message": "Models trained and saved successfully",
"modelFamily": "randomforest",
"trust_summary": {
"level": "medium",
"reasonCodes": ["LOW_R2"],
"metrics": {
"mae": 160.5,
"rmse": 171.2,
"r2": 0.22
},
"rangeChecks": [
{
"feature": "usage_volume",
"outOfRangeCount": 0
}
],
"modelDisagreement": {
"enabled": false,
"peerModelCount": 0
}
}
}
}Technical approach
Prediction workflows should make model behavior visible.
Explore prediction, forecasting, model comparison, and trust evaluation in one place. The workflow keeps diagnostics and decision context close to the model output so results can be examined before they are used.
Use cases
Explore demand, sales, expense, risk, classification, and time-series signals
The workflows help technical and non-technical readers examine future behavior across measurable signals before acting on a result.
Demand and resource forecasting
Use historical demand, seasonality, usage, and capacity patterns to estimate future resource needs.
Usage and engagement prediction
Estimate whether usage, engagement, participation, or drop-off patterns may change based on measurable signals.
Workflow trend prediction
Forecast growth, slowdown, demand shifts, workload changes, or process risk across a workflow.
Product and workflow usage
Predict feature adoption, usage spikes, drop-offs, or engagement changes across digital workflows.
Risk and decision prediction
Combine signals into future-looking risk indicators so teams can act earlier and with more confidence.
Expense and loss forecasting
Estimate future expense, exposure, loss, or resource pressure from measurable activity and cost signals.
How it works
From historical signal to a reviewed prediction
The platform starts with signal history, builds prediction context, evaluates model output, and presents trust information with the result.
Provide signal history
Demand signals, product events, learning activity, expense records, capacity records, workflow history, or organization-level records.
- Demand, inventory, volume, resource, or capacity signals
- Usage, engagement, and product behavior from digital workflows
- Expense, loss, risk, or workflow history when the prediction depends on operating context
Build prediction context
eazePredict organizes signal history into a structure that can be evaluated by a model.
- Recent values, seasonality, spikes, and repeating behavior
- Related context such as usage, events, inventory, cohorts, schedules, cost, or resource metrics
- Signal groups aligned to the decision the team needs to make
Forecast future behavior
Models estimate what may happen next across demand, usage, expense, capacity, risk, classification, or time-series signals.
- Trend and seasonality forecasting for numeric signals
- Feature-based prediction when context changes the outcome
- Classification-style workflows for risk, category, or decision-support outcomes
Evaluate trust
Trust-aware checks help explain when a prediction is reliable, uncertain, disputed, or should be suppressed.
- Confidence and uncertainty signals
- Model disagreement and warning indicators
- Suppression when the forecast should not guide action
Act earlier
Use the forecast and its trust context to review plans, capacity, inventory, learning interventions, expenses, or risk response.
- Prepare capacity before demand exceeds supply
- Prioritize cohorts, users, queues, or workflows that may need attention
- Respond earlier when demand, expense, loss, or workflow risk is rising
Reuse the prediction layer
The same prediction pattern can support learning, planning, product usage, expenses, capacity, loss forecasting, and risk workflows.
- Apply one prediction pattern across many measurable signals
- Review reason codes when a forecast is uncertain
- Adapt the same pattern to specialized domains after the general prediction workflow is understood
Trust-aware prediction
Predictions are more useful when their limits are visible.
eazePredict does not treat every prediction as automatically reliable. The platform can surface confidence, uncertainty, disagreement, and warning signals so teams understand whether a forecast should be accepted, reviewed, or ignored.
Confidence and caution
Show when a forecast looks stable and when it should be reviewed carefully.
Disagreement visibility
Spot cases where different model paths disagree instead of hiding uncertainty behind one polished number.
Suppression over false certainty
Withhold or flag unreliable forecasts when conditions suggest the output should not drive action.
Forecast examples
What can eazePredict forecast?
Use the workflows to frame practical questions around demand, product usage, cohorts, capacity, expenses, risk, time-series movement, and measurable activity.
Demand movement
Will demand, volume, or workload increase next week?
Cohort change
Which user, learner, account, product, or process segment may slow down, return, or drop off?
Usage trends
Is product usage growing, dropping, or shifting across features?
Demand and capacity
Will demand exceed inventory, staffing, resource, or support capacity?
Workflow risk
Which signal is moving away from normal and may need attention?
Loss and risk
Is exposure, event frequency, or historical loss moving toward a higher-risk range?
Documentation
Explore prediction APIs when you are ready
eazePredict APIs support time-series forecasting, feature-based prediction, and classification-style workflows for demand, product usage, expense forecasting, loss forecasting, capacity, and practical signal analysis.
Time-series forecasting
Forecast future behavior from historical signals with trend and seasonality awareness.
- ARIMA — classic autoregressive forecasting
- SARIMA — seasonality-aware forecasting
- AutoARIMA — parameter selection automation
- AutoReg — lightweight autoregressive inference
- SARIMAX — seasonality-aware forecasting with exogenous features
Feature-based prediction
Predict numeric outcomes using product, usage, expense, capacity, cohort, risk, or workflow context beyond time alone.
- Linear Regression — interpretable baseline
- Polynomial Regression — curved feature relationships
- Poisson Regression — count-aware modeling
- Decision Tree — rule-based patterns
- Random Forest — robust non-linear interactions
- Logistic Regression / SVM / Naive Bayes — classification-oriented API services