Where should we start?

With a decision you make often and that costs you when it goes wrong: a supplier order, seasonal staffing, which customer to call. On a first 30-minute call, we look with you at whether your data can predict it.

Do we have enough data?

Often more than you think: sales, invoices, CRM, spreadsheets. You usually need a history that covers several cycles of your business, for example at least two full seasons if your activity is seasonal. If you do not have that yet, we tell you before anything starts.

How reliable will the forecasts be?

A forecast is never a certainty. We deliver it with a range and the factors that explain it, then measure the gap with reality on a regular basis. If the model does not beat your current method, we do not roll it out.

Do we need new software?

No. Forecasts show up in your ERP, your CRM, your spreadsheets or a simple dashboard, and alerts come by email or in your team chat.

Where does our data go?

We favour hosting in Europe and use only the data the model actually needs. When customer data is involved, we check the GDPR side with you before we start.

What drives the cost and the timeline?

Mostly the state of your data: spread across several files, incomplete, or already clean. The number of sources to connect and of tools where forecasts should appear matters too. After the first call, we give you a clear estimate.

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Predictive systems

We build models that learn from your history to anticipate demand, customers about to leave, stock needs or tight cash weeks. Every forecast reaches your tools with the reasons behind it, and we tell you plainly when your data is not enough yet.

Start your project

Monthly reporting agent

Collecting files

10s

Reconciling figures

20s

Drafting the report

30s

Linking figures to sources

40s

Waiting for your approval

50s

What we do for you

Concrete examples of decisions your data can inform ahead of time.

Sales forecasts
Sales for the coming weeks are estimated by product or by branch, taking seasons and promotions into account. Purchasing and staff rotas start from a number.
Right stock levels
The model sees stock-outs and overstock coming. It suggests order quantities. Your buyer decides.
Customers drifting away
Customers whose activity is slowing down are spotted early, along with the signals that triggered the alert. Your sales team knows who to call first.
Cash flow outlook
Likely incoming payments, the usual late payers, recurring costs: you see the tight weeks before they arrive.
Predictive maintenance
Using breakdown history and readings from your machines or vehicles, the model flags which equipment to inspect before it fails.
Customer risk and bad debt
Each new customer or large order gets a risk score with its reasons. You decide on a deposit or payment terms knowing what is behind it.

How we work

Four steps, from the first question put to your data to forecasts your team uses every week.

AI assessment

We pick with you the decision a forecast should support, then look at your data: history, volume, quality. Not enough? We say so, and tell you what to start collecting.

Process redesign

We prepare and clean your data, then decide who receives each forecast, when, and what they do with it.

From pilot to rollout

The model is first tested on past periods and compared with the way you forecast today. If it does better, its forecasts go into your dashboards and arrive as alerts in your tools.

Training and governance

We show your team how to read a forecast and its margin of error, and we watch the model as your business changes. When its forecasts start to drift from reality, we retrain it.

Questions about predictive systems

Ready to get started?

Tell us about your project. We will tell you honestly what it takes, how long it should take and who is right to build it.

Start your projectBook a call

Predictive systems

What we do for you

Sales forecasts

Right stock levels

Customers drifting away

Cash flow outlook

Predictive maintenance

Customer risk and bad debt