Layer 01
Sovereign Infrastructure
Control over where and how AI runs.
Sovereign AI infrastructure starts with a simple question: where does the work happen, and who decides? The platform is designed so you can run AI in the cloud, in a private cloud, on-premise or across all three, with the same identity, security and observability everywhere.
What the infrastructure layer includes
- Compute
- GPUs
- Cloud
- Private cloud
- On-premise
- Hybrid
- Networking
- Storage
- Inference
- Security
- Identity
- Deployment
- Observability
Choose where AI runs
Deployment is a decision you make per workload, not a constraint you inherit. The platform is built so that the same models, agents and workflows can move between these options.
Cloud
Managed capacity for teams that want to start quickly and prove value first.
Private cloud
An isolated environment in a public cloud account or a bare-metal footprint, dedicated to your organisation.
Read more about Private cloudOn-premise
Inference and agents inside your own data centre for workloads that must not leave it.
Hybrid
Sensitive workloads on infrastructure you control, burst or experimental workloads elsewhere, under one set of policies.
Controls that travel with the workload
Placement is only useful if the controls move with it.
Identity first
Every workload, model endpoint and agent runs as a known identity, so access can be granted and revoked.
Isolation by design
Network, storage and compute boundaries are set per environment, so one team’s data and models stay out of another’s.
Inference you can place
Serve open and private models on GPUs you choose, close to the data they work on.
Read more about Inference you can placeObservability throughout
Logs, traces and cost are captured at the infrastructure layer and feed the audit trail above it.
Dependencies are part of sovereignty
Where a server sits is only part of the picture. Sovereign infrastructure also means knowing which providers, regions and components your AI depends on, and being able to change them. That is why this layer connects to supply-chain sovereignty as well as infrastructure sovereignty.
Where this sits in the closed intelligence loop
Control, then intelligence, then agency, then execution, then outcomes. Outcomes and their evidence flow back into data and context, so the loop closes.
- 01 · Layer 01ControlDecide where and how AI runs.(this page)
- 02 · Layers 02 and 03IntelligenceTurn data and knowledge into useful intelligence.
- 03 · Layer 04AgencyLet AI act within defined authority.
- 04 · Layer 05ExecutionEmbed that action in how the organisation operates.
- 05 · Layer 06OutcomesMeasure business results, and feed evidence back into data and context.
This layer is the Control stage of the loop. Every later stage inherits its placement, isolation and identity.
Where Swfte products sit in this layer
Product names are kept. Each is an entry point into the platform.
Dedicated cloud
Isolated VPC or bare-metal deployment in a public cloud or your own data centre.
GPU
Reference for the GPUs used to serve and train large language models.
Connect: self-deploy
Run the model gateway in your own cloud, VPC or data centre, designed for on request.
What this means for your team
- CTO
- Deploy models, agents and workflows on infrastructure you control.
- CISO
- Give AI identity, permissions, policies and auditable controls.
- CIO
- Build one controlled environment for enterprise AI.
Frequently asked questions
What does sovereign AI infrastructure mean?
It means retaining meaningful control over where AI runs and what it depends on: compute, hosting, deployment and critical dependencies. It is about control, not only about the location of a server.
Can AI run on-premise or in a private cloud?
The platform is designed for cloud, private cloud, on-premise and hybrid deployment. The right option depends on your workload, jurisdiction and security policy, and we scope it with you.
Does Swfte offer a private GPU cloud?
Private and dedicated GPU capacity for inference is part of the platform’s position. Talk to our team about what is available for your region and workload.
Where is my data stored?
It depends on your deployment. For what is true today about hosting regions and what is still in progress, see the trust centre. Data residency options are part of the platform’s design, and are agreed per deployment.
How does infrastructure connect to the layers above it?
Placement, isolation and identity set here apply to data, models, agents and workflows. Governance runs through all of them, so a policy decision made above is designed to be enforced on the infrastructure below.
Across every layer
These ideas apply to every layer of the platform.
Controlled autonomy
Five levels, from AI that recommends to AI that adapts within limits.
Trust Profile
The record that describes what each AI system is and may do.
AI sovereignty
Seven kinds of control over your AI estate.
AI governance
Governance that runs inside AI, not beside it.
Swfte Intelligence Platform
Analyse and visualise your data, usage, agents and outcomes, then build from what you see.
Company brain
One governed place for everything your organisation knows, for every product to read from.
Custom models
Models adapted on your own data, evaluated before release and deployed under your control.
How Swfte builds with Cortex
An honest first-party account of how we build, review and approve work, labelled by status.
Governed agents
Identity, permissions, policy, approvals and audit for agents, with ten templates.
Compliance-approved workflows
Workflows with approval gates and evidence built in, with twelve templates.
Build on Sovereign Infrastructure with Swfte
Start with one entry point. Add intelligence, agents, workflows and infrastructure as you prove value.