Gather and make reliable
We build the flows that bring this data home, reconcile it and flag the gaps, with documented quality rules and a log of corrections. Deliverable: flows in production, documented quality rules, consultable log.
Data & ML
Make your data reliable, then train the models that use it.
An agent only answers correctly if the data it reads is current and accurate. We gather what is scattered between the ERP, exports and mailboxes, we make it reliable, then we train the forecasting models that rely on it.
Three strands
Gather, forecast, make it readable.
A data mission produces one or more of these three objects, and the order matters: a model trained on wrong data learns the errors and repeats them at scale.
The flows and the training code are handed over, like everything else.
Train on your history
Demand, stock-outs, delivery delays, unpaid invoices: the model learns on your own data, and its error is measured before any production release. Deliverable: evaluated model, measured error, training code handed over.
Make the result readable
Results land where the decision is made: a dashboard, an e-mail alert, one more column in the tool your teams already have open. Deliverable: dashboard or integration into the existing tool.
Order of projects
Data first, agent second.
It happens that an agent project runs into a problem older than itself: the data it queries is incomplete, or nobody keeps it up to date. When the audit shows that, this project moves to the front.
What the audit looks at
The AI audit establishes the real state of your data before any promise: where it lives, who keeps it up to date, what is missing, what contradicts itself. If it concludes that an agent would read wrong data, it says so and prices the data work separately.
See the AI auditWhat builds on it
Once the flows are in place, agents, automations and applications read current data with no catch-up at every request. The same quality rules then serve all three.
See custom developmentControl
What happens when the model brings nothing.
A model is compared to what you already do: the shop-floor rule of thumb, the average of the last three months, the planner's experience. That comparison appears in the evaluation report you are given.
A period the model has never seen
The evaluation runs on weeks set aside before training, and the error is read in your units: pallets, days late, euros. That number conditions the production release.
If the model does no better than the rule in place, the mission stops at this step. You keep the flows, which serve everything else.
The figures published on this site come from delivered missions. None of them is about a predictive model.
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