PREDICTION LAYER
Scenario Prediction Engine
Forecasts with uncertainty made explicit.
Probabilistic forecasting with confidence intervals, sensitivity analysis, counterfactuals, and ranked what-if scenarios. Uncertainty is never hidden — every forecast carries the range and the assumptions behind it.
How it works
From input to governed output
1
STEPFrame
Define the outcome to forecast, the horizon, and the levers that could change it.
2
STEPSimulate
Run probabilistic simulations across many futures to build a distribution, not a single guess.
3
STEPRank
Score and rank scenarios by likelihood and impact, with sensitivity to each key driver.
4
STEPExplain
Surface the drivers and assumptions so a human can interrogate the forecast before acting.
Capabilities
Confidence intervals on every forecast
Monte Carlo scenario simulation
Sensitivity analysis
Counterfactual what-ifs
Ranked scenario outcomes
Driver attribution
What it produces
Probabilistic forecastsP10 / P50 / P90 bandsRanked scenariosSensitivity charts
See it applied to your mission
Explore how this fits an end-to-end program, or talk to our team about a pilot.