AI inherits
the layer beneath it.
Every model, every dashboard and every agent inherits the definitions, the quality and the origin of the data beneath it. We build that layer: from the pipelines that unlock your sources to the governance that decides what may be used, with the semantic layer in between where meaning is defined once.
Most AI projects don't fail on the model. They fail on the data underneath.
Sources that contradict each other. Definitions that differ per department. Numbers without an owner. The model calculates neatly, only nobody knows what it is calculating on. A data and AI platform is only as strong as the foundation it rests on, and that foundation is exactly the layer most organisations are missing.
Trust evaporates at the first discrepancy
Two dashboards, two numbers for the same question, and people start checking by hand again. Then you have AI with the reliability of a spreadsheet.
A pilot on clean data proves nothing
What works on a tidy export falls over on the real source: the exceptions, the duplicates, the empty fields. The difference between demo and production sits in the foundation, not in the model.
Without an owner, quality belongs to no one
If meaning has no owner, data quality is everyone's job and therefore nobody's. “Revenue”, “active customer” and “on time” then mean something slightly different everywhere.
What you cannot trace, you will not steer by
An answer without a source, a version and a definition is an opinion. To act on it, you need to see where it came from.
A platform is not a product you switch on. It is layers that hold up.
We don't build from scratch and we don't improvise. We follow the reference architectures of the parties that design this for you (Databricks, Microsoft and dbt) and fill them in for your landscape. Proven patterns, not a bespoke construction only we still understand. Every layer below has one job and one owner.
- 01
Landing zone and ingestion
Data lands first in a landing zone, outside the platform itself. Your sources and other suppliers can deliver there without access to your platform. From there we pull the data in under control, in batch, streaming or through change data capture when you want to follow changes near real time. The flows run repeatably, and failures surface before they reach your reporting.
- 02
Storage and transformation
Inside the platform we store data in layers: raw as it arrived (bronze), cleaned and standardised (silver), and ready for use (gold). The step from silver to gold runs on dbt, so business users can build powerful transformations in SQL themselves, with version control and tests underneath. Every layer is readable and reversible, and open formats such as Delta and Parquet keep your data yours.
- 03
Compute
Storage and compute are separate. You bring in compute when you need it and scale with demand, instead of a fixed, expensive cluster running day and night. Heavy processing and daily reporting no longer fight each other, and cost follows usage instead of the other way around.
- 04
The semantic layer
On the gold layer we capture your core concepts as definitions, not as copies. Revenue, margin, active customer, on time: defined once and carried into BI, into your processes and into your AI. Change the definition and it changes everywhere at once.
- 05
Governance
Governance is not a separate project; it sits in the layer itself. Data contracts record what a source promises. Lineage shows where every number comes from. A catalogue makes it findable what exists and who owns it. Access is set per role, per row and per column, and data quality is tested the way you test code: automatically, on every run.
- 06
Security and sovereignty
Your data stays within the EU, encrypted at rest and in transit, with access traceable to the person. ISO 27001 certified and GDPR compliant, not as a sticker but as a setup.
- 07
Infrastructure as code
The entire environment is defined as code (Terraform). A second environment is then no longer handwork, and nothing depends on what was once clicked together by hand. Changes go through version control and can be rolled back.
Your existing landscape stays.
We replace nothing. We connect to what you already have and add the layer that was missing until now: agreed meaning, with an owner.
- Source systems
- Data platforms
- Custom software
Source systems
The systems where the work happens: ERP, WMS/TMS, CRM, HR and finance. Think SAP, Dynamics 365, AFAS, Exact and Salesforce.
Data platforms
Where your data already comes together. Databricks, Microsoft Fabric, Snowflake, Azure Synapse and Power BI simply stay, and we work just as well with whatever you already run.
Custom software
Your own applications, portals and databases: Postgres, SQL Server, internal APIs and home-built planning tools.
Read deeper
The company brain
Where your organisation's knowledge lives: owned meaning, trustworthy data and decisions you dare to act on.
Read the pillarConnect one source. See the difference.
We start with one question and one source. If it works, it grows. If it doesn't, you haven't rebuilt anything at scale.