A governed AI runtime for sovereign and hybrid environments
GREENPOW gives platform, security and AI leaders a private, multi-model runtime for deploying, governing and scaling enterprise AI agents and knowledge workflows in the environments they trust.
The challenge
- AI sprawl across pilots, shadow tools and uncontrolled SaaS endpoints.
- Sensitive data, source code and credentials leaking into public AI apps.
- Hyperscaler and model-vendor concentration with no clean exit path.
- EU AI Act, NIST AI RMF and EDPB obligations turning AI into a defensibility problem.
- Infrastructure, GPU and governance readiness lagging behind AI demand.
- Agent platforms that look like dev tools, not enterprise-grade runtimes.
How GREENPOW solves it
Private, multi-model runtime
Run open-weight and frontier models inside your environment under one policy plane, not inside a vendor's workspace.
Governed agents and workflows
Identity, retention, tool governance, evaluation and audit logs as first-class controls, by tenant and by workload.
Cross-environment orchestration
One control plane across private cloud, sovereign cloud, hybrid estate and edge, with workload placement by policy.
Sovereignty as a deployment option
Pin data, models and operator access to the jurisdiction that the workload actually requires.
Production observability
Tracing, evaluations, fallback logic and incident-ready telemetry for AI workloads that have to keep running.
Carbon-aware AI placement
MAIZX places inference and training where energy and grid signals allow, without breaking residency, latency or governance.
Use cases
Private enterprise assistants and company knowledge
Internal copilots with per-business-unit memory isolation, private deployment and audit-ready retrieval.
Governed workflow automation and cross-system agents
IT, operations, compliance and internal-service agents tied to systems of record, not abstract multi-agent demos.
AI governance and observability
Policy enforcement, usage logging, model-routing visibility and controlled rollout across teams.
Regulated industry copilots
Financial, healthcare and public-sector AI workloads with data locality, attestation and confidential-computing support.
Sovereign AI for sensitive data
Inference and fine-tuning on data that cannot leave the jurisdiction, with open-weight or contracted frontier models.
Governed runtime needs governed memory
An AI runtime without an operational memory layer leaks context across tenants and forgets what it should remember. Cortex gives every tenant, team, or client its own memory namespace, configurable retention, and an audit ledger of every prompt, retrieval, and agent action. Continuity without contamination, with a defensible record of what the agent knew and when.
Economics of GREENPOW for this sector
GPU training and inference where placement, scheduling, and energy mix drive both unit cost and Scope 2.
- Compute cost lever
- 20-40%
- Carbon evidence
- Per-job gCO2e
- Operational risk
- Sovereign GPU options
Modeled cost reduction from carbon-aware placement and off-peak scheduling versus a single-region default.
Per-job energy and carbon footprint for training and inference. Tag: Modeled, validated in R&D.
EU regions and sovereign capacity for sensitive training data and model artifacts.
- 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
- Positioned as an enterprise AI runtime, not another assistant or model gateway.
- Multi-model and multi-environment by design, with explicit exit paths.
- Governance, audit and isolation treated as core product, not add-ons.
- No sustainability claim without a measured source under our claims policy.
Frequently asked questions
Is this a replacement for OpenAI, Anthropic or Bedrock?
No. GREENPOW is the governed runtime above models. You can use OpenAI, Anthropic, Bedrock, Azure OpenAI, open-weight or self-hosted models inside one policy-controlled estate.
Do we have to self-host every model?
No. You choose per workload: self-hosted open-weight models inside your boundary, frontier providers under enterprise terms, or a mix routed by policy.
How is this different from Azure OpenAI or AWS Bedrock?
Those are excellent inside one hyperscaler. GREENPOW operates above multiple clouds, sovereign environments and edge sites, so AI governance is not anchored to a single vendor.
Can our security team actually trace what an agent did?
Yes. Every prompt, retrieval, tool call and agent action is recorded against the tenant boundary and surfaced for audit and incident response.
How does this help with the EU AI Act?
We do not claim turn-key compliance. We give you the deployment, control, logging and evidence points that make defensibility realistic, mapped to AI Act, NIST AI RMF and EDPB guidance where relevant.
Can we start small?
Yes. The usual entry point is one governed workflow, typically a private assistant or a cross-system agent, then extending the same runtime to more teams and use cases.
Move enterprise AI from pilots to governed production
Let's scope one private, high-value AI workflow you can put into production this quarter, under your governance and inside your environment.
Carbon figures on this page follow our claims policy. How we measure this · Evidence labels (Observed / Measured / Modeled / R&D validated).