Turn Your Data Into Decisions With AI

It depends on the problem, and the honest answer is more than people expect for some tasks and less for others. Predicting a rare event needs more examples than predicting a common one, while data intelligence and some NLP can deliver value with less. During discovery we assess whether you have enough data for what you want, and tell you honestly if you do not.

We test every model on data it has never seen, called held-out data, and report its real accuracy on those cases before you rely on it. A model can score perfectly on the data it learned from and still fail on new cases, so testing on unseen data is the only honest measure. You see those figures and decide whether they are good enough for your decision.

Yes, and most real business data is messy and scattered, that is normal. Part of the work is cleaning, reconciling and bringing data together from the different systems it lives in. We are upfront that this unglamorous step matters, because intelligence built on dirty data produces confident wrong answers. Good data preparation is a large part of why a project succeeds.

Yes. We build to GDPR and AVG, keep data in EU regions where required, and are careful about how personal data is handled throughout. This work involves your real business data by nature, so protecting it and complying with regulation is built into how we run the project. Our European base in Amstelveen means EU data protection is familiar ground.

Yes. We build NLP that handles Dutch, English and other languages, which matters for Netherlands businesses whose documents and customer text are not all in English. Whether analysing Dutch-language reviews or extracting data from Dutch documents, we build the processing to work in the languages your business actually operates in.

A focused predictive model or dashboard can be delivered in a few weeks. Larger projects involving significant data cleaning, multiple models or a full business intelligence setup take a few months. We usually start with one well-defined decision and prove value there quickly, rather than attempting everything at once, so you see a return before committing to more.

Models drift as the world changes and data sources evolve, so we build in monitoring and retrain on fresh data as part of ongoing support. Dashboards need their data pipelines maintained as your systems change. We keep the whole thing accurate over time rather than handing over something that slowly stops reflecting reality, which is the usual fate of unmaintained analytics.