Forecasting specialists for your dedicated team.

Where forecasting fits in a long-term product team.

A point forecast is close to useless on its own. The planning question is not "what will demand be" but "what should we prepare for", and that needs a range. A forecast of a thousand units means something quite different if the interval is nine hundred to eleven hundred than if it is four hundred to two thousand.

Simple methods are a hard baseline to beat. Seasonal naive, where this month equals the same month last year, outperforms elaborate models on many business series, and a project that skips that check has no evidence its complexity earned anything.

What your dedicated team does with forecasting.

Forecasts need monitoring after deployment, which is the step most often skipped. A model that was accurate at launch degrades as conditions change, and without tracked error the drift goes unseen until a planning decision goes badly wrong.

That continuous evaluation is ongoing work, held within an agreed committed monthly capacity.

Common forecasting use cases.

• Intervals alongside point estimates A range to plan against, because the uncertainty is the decision-relevant part.
• Complexity justified against a naive baseline Checking that a model beats seasonal naive before it is trusted.
• Accuracy tracked after deployment Forecast error monitored, so degradation is caught before a plan depends on it.

Adding forecasting skills to your team.

Forecasting sits with a dedicated data science team, with accuracy tracked after deployment as part of the work.

Every forecasting specialist we assign works remotely inside your tools as a dedicated member of your team, with the management on our side. It is how companies hire remote developers without opening an offshore office of their own.