A European approach built into architecture — not retrofitted.
GDPR, EU AI Act, decision traceability, data ownership: these requirements are not external constraints. Well integrated from design, they become a competitive advantage — on trust, readability and system durability. The cabinet practice organizes around documented principles, without claiming a compliance guarantee or certification.
What we do not promise.
Sovereignty is a working frame, not a label. To prevent any over-reading of this page, three clarifications.
- 01No invented certification — legal compliance remains with the client and their DPO.
- 02No absolute sovereignty — a realistic system documents its dependencies, boundaries and architectural trade-offs.
- 03An approach to design, documentation, control and arbitration — not a guarantee of regulatory outcome.
Five sovereign design principles.
- 01Data under control — the enterprise retains ownership and control of its operational data.
- 02Decision traceability — every agent action is logged with context, inputs and outputs.
- 03Human validation where required — high-stakes decisions are never purely automatic.
- 04Access governance — agents can only see and do what their role permits.
- 05Documented architecture — the system is explainable to a regulator and to a business leader alike.
A cabinet reading of GDPR.
Agentic architecture multiplies the collection and processing points of personal data. Without discipline, it also multiplies risks. The cabinet practice adopts a proactive reading of GDPR as an architectural constraint.
The practice favors minimization (collect only what's needed), purpose limitation (use data only for declared purposes) and controlled retention. These principles translate into architectural constraints: an agent does not access a field for which its purpose is not justified.
Data subject rights (access, rectification, erasure, portability) are designed into the system from the start — not added in reaction to a request. Legal compliance always remains with the client and their DPO; the cabinet brings the architecture that makes this work possible.
EU AI Act — anticipate rather than endure.
The EU AI Act classifies systems by risk level and imposes differentiated obligations. The cabinet practice prepares for this classification from design — without constituting a compliance guarantee.
For each system, the practice documents: classification (minimal, limited, high, unacceptable), purposes, training datasets where applicable, human controls, measured performance and monitoring mechanisms.
This documentation is not a final deliverable — it builds up sprint by sprint, leveraging the system's native traceability. When the AI Act becomes fully enforceable, the organization is ready.
Stack choice and data localization.
Not all data is processed with the same rigor. The stack is chosen case by case based on sensitivity.
For operations with high sovereignty stakes (strategic data, regulated data, critical customer data): sovereign models hosted in Europe (Mistral, open models on European infrastructure), European databases, isolated processing.
For complex reasoning or creative generation with lower stakes: frontier models (Claude, GPT) consumed via contractually framed channels (GDPR-compliant DPAs, standard contractual clauses for transfers).
This dual standard, made explicit in the blueprint, protects strategic value without sacrificing performance.
Human validation and the governance loop.
Governance is not just a written charter. It lives in concrete operational mechanisms embedded in every agent.
The governance loop — decide → validate → execute → trace → measure — is documented for each agent. High-stakes decisions go through human validation (synchronous or asynchronous depending on context). Routine decisions are continuously traced and auditable.
This discipline makes the system explainable, auditable and adaptable. It is also the foundation for meeting the future demands of regulation — which will only keep tightening over the coming years.
Operational sobriety and environmental impact.
Sobriety is an architectural discipline. Choosing a lightweight model over a frontier model where it isn't justified, hosting in Europe, selecting providers aligned with concrete environmental commitments: these choices have a cumulative impact.
The practice's technical infrastructure — site, analytics instance, internal services — is hosted at Hetzner Online GmbH in Germany and Finland. The data centers used are powered by 100% hydroelectric electricity, since 2008 for Germany and 2018 for Finland.
Published indicators are telling: PUE between 1.10 and 1.16 (close to the theoretical 1.0 floor), WUE of zero (air-only cooling, no water consumption), free cooling up to 98% of the year. German sites are EMAS-certified (European Union Eco-Management and Audit Scheme). According to the provider, this energy choice avoids approximately 77,000 tonnes of CO₂ per year compared to the standard German electricity mix.
On the agentic architecture side: the practice favors multi-model routing, reserving heavy foundation models for tasks that warrant them and using more efficient models where sufficient. This discipline reduces inference costs, response times — and the energy footprint of deployed systems.
Sobriety is not a marketing argument: it is an architectural quality criterion. A mature agentic system consumes less because it is better designed.
Assess your current sovereignty posture.
A sovereignty diagnostic identifies blind spots (data exfiltrated unknowingly, unframed models, missing traceability) and prioritizes compliance initiatives.