The challenge
Every AI initiative turns out, sooner or later, to be a data initiative. The model is rarely the bottleneck — the bottleneck is data scattered across source systems, with inconsistent definitions, no quality control and unclear ownership.
At the same time, pressure on data handling keeps rising: GDPR, NIS2's cybersecurity requirements, and in the public sector the demands of data sovereignty and European control. "Move it to the cloud and call it solved" no longer works.
Our approach
- Mapping. Which source systems, flows and definitions exist — and where do they break? We document the current state and the hidden dependencies.
- Foundation and pipeline. We build the smallest platform that solves the prioritised use case well: ingestion, transformation, quality controls, lineage and monitoring.
- Make data understandable. Data model, catalogue and ownership so the next initiative starts from the data — not from another data survey.
- Governance that works. Classification, access control and GDPR routines the business actually follows — because they are built into the platform.
We hand over documentation, code and knowledge so your own teams can operate and develop it onwards. The platform should be yours — not ours.
Frequently asked questions
Do we need to build a big data platform first?
No. We start from the use case that justifies the work and build the smallest platform that solves it well — then it grows with actual need. A platform nobody uses is a cost, not an asset.
Which technology do you use?
It depends on your environment and requirements. We are vendor-neutral — hyperscaler services, open source or hybrid — and document the reasoning. Since we sell no platform, our recommendation is simply what is best for you.
How do you ensure data is handled correctly under GDPR?
Classification and minimisation from the start: personal data identified, flows documented, deletion and restriction built in as features — not bolted on. We work closely with your DPO.