Sovereign AI — Private AI Deployment, On-Device AI, and Local Models for Enterprise by Ava Technologies
Private AI deployment.
AI that runs inside your infrastructure — on local servers, on user devices, or air-gapped — for tasks where data cannot leave the organisation.
What this means
A category, not a movement.
Cloud AI requires sending data to a third party. For some tasks, that is not acceptable — under compliance, under client privilege, or under board policy.
Sovereign AI is the alternative: AI running on your hardware, under your access controls, with no external telemetry. We design and deploy at the right point on that spectrum for the tasks at hand.
Where it fits
Sectors where data control is contractual.
Healthcare and life sciences
Patient data, clinical notes, research data. Local inference keeps protected health information inside controlled boundaries.
Legal and professional services
Client privilege, confidential documents, sensitive correspondence. Private deployment removes the exposure cloud AI introduces.
Financial services
Trading data, customer records, financial models. Self- hosted deployment provides the governance the sector expects.
Government and public sector
Data residency, security classifications, public accountability. On-premise and air-gapped deployment keeps data inside controlled environments with full auditability.
What we deploy
Four capabilities.
On-device inference
Models running on user hardware — phones, laptops, edge devices. No network calls, no telemetry. Suited to executive tools and workflows where data must not leave the device.
Self-hosted deployment
Models deployed inside your VPC, on-premise, or air-gapped. Access controls, audit logging, model governance. Suited to enterprise-scale workloads with strict data-handling requirements.
Edge models
Small, task-specific models that outperform a frontier model on a defined task while running locally. Lower attack surface, lower cost, faster.
Hybrid routing
Infrastructure that routes tasks between local and cloud inference based on data sensitivity. The right model for the task — automatically, without rebuilding the application.
How we work
Architecture before tooling.
01
Requirements and threat modelling
We map data flows, compliance obligations, and risk tolerance before recommending a deployment model. What does AI need to see? What must it never see?
02
Deployment design
We specify the architecture — on-device, self-hosted, edge, hybrid — and the routing logic where tasks have different sensitivity profiles.
03
Build and integration
We build inside your existing infrastructure. For local deployments, we handle model selection, optimisation, access controls, and audit logging.
04
Validation and handover
We validate against your compliance requirements and document the architecture so your team can maintain it. Ongoing support available.
In development
Private cloud proxy.
For tasks that genuinely require frontier model capability, we are building an obfuscation layer that strips personal identifiers and sensitive data before any prompt reaches a cloud provider. Not yet shipped. We mention it here because it is part of the deployment stack, and we would rather flag what is in development than let it sit in present-tense copy.
FAQ
What organisations ask.
Start with the constraints.
Tell us what your data can and cannot do. We'll tell you what kind of deployment fits.
Talk to us →