See breakdowns, shortages, and busy periods coming before they cost money

A pump fails on Friday afternoon, and the technician cannot come until Monday. The warehouse is full of an item that has not sold for 3 months, while the fast mover is sold out. The planner counts on a quiet week, and it turns out to be the busiest of the quarter. In every one of these cases, the warning was already in your data. Nobody looked at it in time. We build models that take over that looking and warn you before it costs money.

When forecasting pays off

Forecasting makes sense when something happens again and again and you can still act in time. A machine that stops a few times a year at a moment nobody sees coming. Stock you reorder by gut feeling. A workshop where lead time depends on ten variables that only the foreman carries in his head. Predictive maintenance is the best-known form: vibration and temperature data from a machine show weeks ahead that a bearing is wearing out, so you schedule maintenance instead of fixing a breakdown.

The same principle works for demand forecasting in wholesale, for estimating how busy a care provider will be, or for predicting which invoices will be paid late. The model does not have to be clever. It has to look consistently at patterns that a person overlooks because they have a hundred other things to do.

What your data already knows

You usually do not need to buy new sensors to begin. Your ERP knows when each item sold. Your maintenance log knows when each machine was repaired and why. Your planning tool knows how long a job really took, not how long it was estimated to take. That history from the past years is the raw material. Where it pays off, we still add a sensor, for example a temperature sensor on a compressor or a counter on a production line.

The data is almost never clean. A maintenance visit was not entered once, an article number changed halfway through, a sensor logged nothing for a month. We sort that out before we build a model, because a prediction on dirty data is worse than no prediction. If the data has to come together from several systems, we arrange that through Data and reporting, so it is useful to you beyond the forecasting model.

From history to a first model in 10 business days

We start with one question. Not "predict everything", but "which of these 12 machines is most likely to fail next month" or "how many of this item will we sell next month". We train a model on your own history and test it on a period the model has not seen. Within 10 business days, you can see whether the model guesses better than your experienced planner. The gain is consistency: the model looks the same way every day, and over a hundred decisions a month that adds up.

If it works, we build the prediction into the screen where your people already work: a color next to a machine in the maintenance overview, an order suggestion next to the stock line. If you are unsure which process suits this, the AI process scan is the logical step before it. If the wear or rejects are something you can see with a camera, it belongs with Computer vision, and we combine the two where that helps.

What a prediction is worth, measured on your history

A model gives a probability, not a certainty. We tell you how often the model was right on your own history and what it is bad at. If there is too little history to say anything useful, we tell you right away and stop, instead of delivering a dashboard that mostly looks good. The decision stays with your technician or planner. The model does the first pass and shows why it expects something, so a person can check it.

The model runs on a server in the EU, on your own data, and you own the model and everything it has learned. If the predictions are about people, for example which client will cancel, we look at the GDPR and the EU AI Act before we build. For the formal side, we help you through GDPR compliance.