Decision support

Predictive Intelligence

Use forecasts only with a baseline, uncertainty range, evaluation window, domain fit, and a named decision owner.

A single predicted number can hide missing history, shifting conditions, and the cost of being wrong.

A forecast is useful when it changes a specific decision and exposes how confident, current, and limited that forecast is.

Capacity planning: Historical demand and a planner who can compare the forecast with operational constraints. Boundary: The forecast informs staffing or inventory review; it does not place orders automatically.
Pipeline risk review: A consistent historical record and a leader who owns the intervention decision. Boundary: Missing activity data remains missing and is not replaced with an inferred customer fact.

No accuracy guarantee, probability of success, or financial outcome is published here.
No model is assumed valid outside its stated domain and data history.
A research metric is not a production forecast.

Synthetic illustration of the design sequence. It uses no client, dashboard, or production trace.

Text equivalent
Step	Stage	Meaning
1	Decision	Name the decision, owner, and action the forecast is allowed to inform.
2	Baseline	Define the historical baseline and the population it represents.
3	Features	List the features used and the data that was unavailable.
4	Uncertainty	Show uncertainty rather than presenting one number as a fact.
5	Evaluation	Evaluate performance over a declared window and comparison method.
6	Domain fit	State where the domain, season, or operating context makes the model unfit.