Forecasting, optimization and decision intelligence aimed at the questions operations and the P&L actually turn on. We build the model, the decision it feeds, and the way you check it is still right.
We start from the call being made — who makes it, how often, and on what evidence — and work back to a question a model can answer.
Forecasting, optimization and simulation are built against an honest baseline and tested causally, so a real effect can be told apart from a coincidence.
The recommendation goes where the decision is made, carries the reasoning that produced it, and comes with the monitoring that says when it stops holding.
We judge a data science engagement by whether the person making the call now makes it differently — not by AUC or RMSE. That means the forecast lands inside the planning cycle rather than beside it, the recommendation carries its reasoning, and the assumptions are written down where someone can argue with them. Where none of that holds, the model is not finished, however well it scores.
Read the case study →