Demand, sales, production or event-rate forecasting built and tuned on your operational history.
Forecasting problems can look simple from the outside — predict the next number in a series.
Real operational forecasting means multiple related series (products, stores, regions) that need to reconcile against each other, cold-start products with no history, irregular seasonality, and horizons that aren't necessarily single numbers but distributions. Because business decisions downstream (staffing, inventory, pricing) can depend on the range of likely outcomes, not necessarily just point estimates, it's often worth considering probabilistic models.
We choose the model to fit the data volume and latency budget: for example exponential smoothing and statistical baselines where history is short or interpretability matters, gradient boosting on lagged and calendar features where there's enough history and cross-series signal to exploit, and sequence models where the horizon and data volume justify the added complexity.
Every forecast is trained on your history, against your actual demand or event patterns — including consideration for variable events such as interruptions, interventions, innovations, outliers and anomalies.
How we approach it
Mapping which series exist, at what granularity, and how they relate hierarchically. Defining the forecast horizon and update regularity against the actual decision it feeds — a weekly staffing plan needs a different horizon than a daily stock audit or a production conveyorbelt.
Identifying and flagging promotions, stockouts, closures, and other shocks so the model doesn't learn them as recurring seasonality. Handling cold-start series with limited or no history via hierarchical pooling or analogous-series matching.
Lag features, rolling statistics, and calendar effects for gradient boosting approaches; statistical decomposition for lower-volume series. Model choice driven by data volume, series count, and latency requirements — not a default preference for complexity.
Ensuring forecasts at different hierarchy levels (SKU, category, region, total) sum consistently. Producing prediction intervals, not just point forecasts, so downstream decisions can be made against a realistic range of outcomes.
Rolling-origin backtests that simulate how the model would have performed in production, evaluated against the metric that matters for the business decision — not just average error. Monitoring schema and retraining cadence defined before deployment.
What the forecast looks like
History and forecast, with uncertainty
Each forecast widens the further out it looks. At each milestone, real observations arrive and the model forecasts afresh from that point — rather than compounding uncertainty from an earlier guess.
Tell us what you're forecasting and we'll scope it together — no obligation.
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