A model that learns from your own data, not someone else's approximation of reality An off-the-shelf tool for sales forecasting or product recommendations runs on a generic algorithm that knows nothing about your specific range, your seasonality, or how your customers actually behave. The forecast looks professional, but it's built on patterns from an entirely different business.
Ready to publish without client data. --- # A model that learns from your own data, not someone else's approximation of reality
An off-the-shelf tool for sales forecasting or product recommendations runs on a generic algorithm that knows nothing about your specific range, your seasonality, or how your customers actually behave. The forecast looks professional, but it's built on patterns from an entirely different business.
A dashboard or off-the-shelf BI tool shows nice charts, but it doesn't predict anything — it shows what happened, not what's coming. When you need a sales forecast for next month or a recommendation tailored to a specific customer, an off-the-shelf tool either doesn't do it, or does it with a generic algorithm that knows nothing about your data.
The result: decisions about stock, staffing, or campaigns get made on gut feeling instead of a model that has actually learned the patterns in your own sales history.
We build a machine learning model trained on your own historical data — sales, customer behavior, seasonality — tailored to one specific task, such as sales forecasting or product recommendations. The model learns patterns specific to your business, not averaged assumptions from a different industry.
We build the model for one precisely defined task, not as a generic tool for everything — that lets us reliably measure its accuracy on your historical data before it starts influencing real decisions. We document the entire training and validation process, so you know exactly what basis the model's predictions rest on.
What we don't do: we don't hand over a model whose accuracy can't be verified against your data — every model is tested on historical data before it goes into production use.
This is a new service line — we don't yet have published deployments to show off, and we say so directly rather than inventing references. We run first deployments on preferential terms, in exchange for the right to describe the result, without client data, as a reference case.
The scope covers one model tailored to one predictive task — for example, a sales forecast for one product category.
Price depends on the amount and quality of available historical data, the complexity of the predictive task, and whether integration with your system is needed. You know the amount after the data analysis, not before it.
You set the success threshold. Before we start, we agree on the forecast accuracy the model must reach on historical data for the rollout to make sense.
Validation before rollout. The model goes into production use only after being checked on data it never saw during training.
Code and documentation in your repository. The model, training data, and documentation land in a repository you control, not us.
The same team that builds predictive models also runs the rollouts for our other automation services — so the number of new projects we take on each month is limited.
Send us which predictive task you're interested in and what historical data you have available — we'll reply within a few business days with the scope of work and a firm price.
Every month without a model tailored to your data is a month of decisions made on gut feeling where a specific forecast could have stood behind them instead.
A model trained on your own historical data, for one specific task · accuracy measured before rollout, not after the fact · validated on data the model never saw during training · success threshold agreed before we start · model and documentation stay under your control.
It depends on the task — we check this during the analysis stage and say directly if there isn't enough data for a reliable model.
The model learns patterns specific to your data, instead of applying a generic algorithm that knows nothing about your business.
We validate it on historical data it never saw during training and measure prediction accuracy against what actually happened.
We start with one task to measure accuracy reliably, before considering extending the scope.
We flag this during the analysis stage and agree together on whether the data is sufficient to build a reliable model.
Documentation and code land in your repository — you can maintain the model yourself, or use our separate monitoring and tuning service.