Data & AI Architecture & Technology
Cloud and sovereign platforms
Architecture & Technology: a platform that matches your ambitions
A scalable platform, aligned with your long-term ambitions. We design state-of-the-art data and AI architectures tailored to your environment, striking the right balance between standardization and flexibility.
What we do
Target data and AI architecture:
modern data stack, lakehouse, data mesh or Fabric approach, depending on your context and organization.
Platform selection:
comparative assessment (Snowflake, Databricks, Microsoft Fabric, native AWS, Azure and GCP services) based on explicit criteria: cost, skills and portability.
Cloud foundations:
landing zones, security, networking, DevOps and FinOps built in from the design stage.
AI platforms:
foundations for machine learning and generative AI, RAG, agentic AI and integration with existing systems.
Sovereignty:
architectures that meet data residency and independence requirements, including on European sovereign cloud (OVHcloud, Outscale).
Modeling:
Data Vault 2.1 and dimensional approaches for auditability, flexibility and automation.
Our approach
Architecture follows ambition, not the other way around. We start from your use cases and your organization’s trajectory to size a realistic target, reusing existing assets wherever they are sound.
Sovereignty, security and cost controls are built in from the design stage, never bolted on after the fact.
What sets us apart
We are vendor-agnostic:
we objectively assess the best technical stack for your context, skills and preferences.
Architectures designed to be run:
observability, automation and run costs are factored in from the design stage.
Recognized expertise:
across leading market platforms, with active partnerships providing direct access to vendors.
An objective technology choice for a major public body
Client case study
A major Belgian public body
Challenges
Provide future generative AI use cases (RAG) with a vector search foundation, within an Azure environment and a public-sector-specific framework: cost control, security, sovereignty.
Decide between extending an existing PostgreSQL foundation (pgvector) and a managed service (Azure AI Search) based on objective criteria.
Solution
Structured comparative assessment: performance, cost, security, integration with the existing IT landscape, in-house skills and portability.
Documented recommendation, presented as an executive summary to senior management.
Benefits
An objective, traceable technology decision, aligned with the teams’ context and skills.
A right-sized foundation, ready for generative AI use cases, without over-investment.
FAQ
How do you choose between Snowflake, Databricks and Microsoft Fabric?
There is no one-size-fits-all answer: the choice depends on your use cases (analytics, ML, real time), in-house skills, existing cloud ecosystem, cost model and portability requirements. We bring objectivity to the decision with a comparative assessment against these criteria, applied to your context.
When should you consider a sovereign cloud for your data?
When regulatory, industry-specific or strategic requirements call for control over data location and protection from extraterritorial legislation (for example, the CLOUD Act or FISA). Hybrid architectures make it possible to reserve sovereign cloud for sensitive data while still benefiting from hyperscaler services.
Why use Data Vault to model a data platform?
Data Vault 2.1 provides auditability (built-in historization), flexibility as sources change, and strong automation potential. It is a strong fit for evolving multi-source environments, while dimensional models remain well suited to the presentation layer.
Need to make an objective platform choice or define a target architecture?
Let’s talk.