A number is only useful if you can defend it.
Studio is a prediction system, so the question that matters is simple: how often is it right? We answer it two ways, and we show the track record before you act.
of pilot scenarios landed within 10% of the actual revenue or KPI outcome.
directional accuracy across revenue and KPI predictions in validation to date.
every forecast is checked: historical case studies and live backtests on your data.
These are pilot results, not guarantees. We set confidence intervals with you before a simulation runs, and we report ranges we can show the backtest behind.
Two independent tests.
One proves the model reads a market in general. The other proves it reads your market in particular.
- 01
Historical case studies
We test Studio against business decisions whose outcomes are already known. The prediction is made blind, then checked against what actually happened. It is the cleanest measure of whether the model reads a market correctly.
- 02
Live backtests on your data
Studio’s memory is bi-temporal: it separates when something happened from when it was known. That lets us replay one of your own past decisions using only the information available at the time, then compare the prediction to the result you already hold.
Three things, checked every time.
Revenue and conversion
The headline KPIs a decision moves. Predicted as a distribution across outcomes, not a single point, so you read the likely range and the downside.
Customer sentiment
How the customer base feels about the move. Sentiment reads sharper than revenue: the direction of feeling is easier to call than the exact dollar magnitude.
Cohort-level reasoning
Not just the number, but which cohorts drove it. Every prediction is drillable, so a forecast can be checked against the behavior that produced it.
Ranges you can defend, not promises.
Accuracy depends on the richness of your data and the calibration window. A model with five years of transactions reads sharper than one with five months. We tell you which you have before a single scenario runs.
We state results as validated ranges and only where we can show the backtest behind them. Where a decision sits outside what the data can support, we say so. That is the point of validating: to know when to trust the number and when to gather more.
Bring a past decision.
We will backtest it against what actually happened, so you can judge the model on your own data before you rely on it.