Aixys runs gradient-boosted, generalised linear, and time-series models against your historical data, with cross-validated performance estimates and interpretable feature attributions. The output is a forecast you can defend in a meeting: confidence intervals, drivers, sensitivity, and the assumptions you are betting on.
Click any card to expand. Every action carries provenance — what data was used, what was decided, when, by whom.
Aixys ships every forecast with SHAP attributions so the drivers of each prediction can be read out loud. No model is a black box.
Three representative scenarios drawn from real research workflows. Click any to expand the follow-up that the team typically runs next.
Industries where this module is most directly relevant. The underlying engine is general — sector templates accelerate the work.
A 30-minute working session with the Aixys studio. Bring a real dataset; leave with an analysis, a plan, and answers to your team's hardest research-ops questions.