Sovereign infrastructure for critical workloads and AI
GREENPOW gives enterprises, governments and regulated operators a control layer for running sensitive workloads across private, sovereign, hybrid and edge environments, with jurisdictional control, resilience engineering and workload portability.
The challenge
- Hyperscaler concentration turned into board-level operational risk.
- Extra-territorial access concerns under Schrems II, CLOUD Act and EDPB guidance.
- DORA and NIS2 making continuity and supply-chain control infrastructure requirements.
- Data Act switching obligations forcing real workload portability.
- AI workloads expanding scope of sensitive data without a governed runtime.
- Fragmented estates across private, sovereign, hybrid and edge with no common control plane.
How GREENPOW solves it
Jurisdictional control by workload
Place each workload in the right legal and operational domain, with verifiable data, operator and support boundaries.
Continuity-first design
Independent failure domains, tested failover, degraded-mode operations and recovery as deployment decisions, not after-thoughts.
One control plane across environments
Standardise how teams deploy and operate across private cloud, sovereign cloud, hybrid estate and edge.
Workload portability and exit paths
Open interfaces, deployment topology options and migration paths, so dependency stays a choice rather than a default.
Governed AI runtime inside the boundary
Sensitive AI runs where data sensitivity, auditability and operator trust actually permit it.
Carbon-aware placement, never overriding compliance
MAIZX optimises location for energy and grid signals, constrained by residency, latency and jurisdiction.
Use cases
Sovereign AI for regulated data
Copilots, analytics and inference on data that cannot leave the jurisdiction, with model and operator controls.
Regulated application estates
Private and public workload placement under one control model for financial, public-sector and healthcare systems.
Critical systems and edge operations
Latency, survivability or disconnection requirements handled with distributed compute and edge topology.
Cyber recovery and resilience
Defined recovery objectives, isolated failure domains and failover control for workloads that must survive disruption.
Confidential workloads and secure data collaboration
Data-in-use protection for AI and cross-organisation analysis without exposing source data.
Pin AI context to the right jurisdiction, with an audit ledger over every action
Sovereign infrastructure only holds if the memory layer above it is sovereign too. Cortex pins per-tenant context to the jurisdiction you choose, enforces operator access boundaries, and records every prompt, retrieval, and model call. Open-weight models inside the boundary, frontier providers under enterprise terms, or a mix per workload.
Economics of GREENPOW for this sector
EU-operated infrastructure where residency, jurisdiction, and verifiable energy reporting are the product.
- Infrastructure cost lever
- 10-20%
- Carbon evidence
- Per-workload kWh + gCO2e
- Operational risk
- EU law, EU operators
Modeled cost versus hyperscaler EU regions for sovereign or regulated workloads.
Per-workload energy and Scope 2 with documented emission factors. Tag: Modeled, moving to Measured.
Operated under EU law with documented residency, access, and exit controls.
- Figures shown are modeled defaults. Confirm sourcing before using any figure publicly. See /en/impact#methodology and /en/impact#evidence-labels.
What you can rely on
- Designed for critical workloads, regulated AI and continuity, not generic public-cloud hosting.
- Residency, operator boundaries and recovery handled as primary controls, not paperwork.
- Carbon decisions constrained by compliance, residency and latency requirements.
- No sustainability claim without a measured source under our claims policy.
Frequently asked questions
Why not just use AWS, Azure or Google sovereign offerings?
Use them where they fit. GREENPOW becomes relevant when critical workloads need broader jurisdictional control, clearer operator boundaries and continuity that does not depend on a single global control plane.
Do we have to leave our hyperscaler to work with you?
No. GREENPOW orchestrates workload placement across private, sovereign, hybrid and public environments, including your existing hyperscaler footprint.
Does this scale globally?
The goal is not to rebuild a hyperscaler footprint. It is to place the right workloads in the right control domains while keeping architecture and operations consistent across environments.
How do you ensure resilience?
By designing it into topology, operations and recovery: independent failure domains, tested failover, workload placement rules, degraded-mode operations and automation-led recovery.
Can this support AI workloads?
Yes. Sensitive AI is treated as an infrastructure and governance problem as well as a model problem, with governed runtime, confidential-computing support, data-locality controls and cross-environment orchestration.
What about compliance?
We do not claim blanket compliance. We support obligations through control points, evidence, locality options, access restrictions and auditability, mapped to GDPR, Schrems guidance, NIS2 and DORA where relevant.
Operate critical workloads and AI on infrastructure you control
Let's review a critical workload against sovereignty, resilience and AI governance, and design the right placement model.
Carbon figures on this page follow our claims policy. How we measure this · Evidence labels (Observed / Measured / Modeled / R&D validated).