Solutions / 05
Sovereign AI Infrastructure
AI capability that runs inside your boundary: local inference, open models, and the infrastructure and telemetry to operate them.
The problem
For some workloads the constraint is not model quality but data movement. Security telemetry, regulated records and internal source code are all categories where sending content to a third-party endpoint changes the risk position materially — sometimes for policy reasons, sometimes for contractual ones, sometimes because the data belongs to someone else.
The counter-argument is usually cost and capability. Both are moving quickly: open models have become genuinely useful at sizes that run on a single well-specified host, and the operational question has shifted from whether local inference works to how you would run, secure and observe it.
Architecture
What the system looks like.
Inference
Local model serving with capacity, context and concurrency as designed limits.
Retrieval & memory
Vector and object storage inside the boundary, under the same classification rules.
Agent runtime
Where models are used agentically, the Secure Agentic AI control plane applies unchanged.
Infrastructure
Virtualisation, isolation, storage, secrets and network policy.
Observability
Throughput, latency, cache behaviour, saturation — alongside the security events.
The architecture treats an inference host as ordinary infrastructure with unusual resource characteristics: it needs capacity planning, isolation, secrets handling, telemetry and a failure model, exactly like any other platform component.
- Inference layer
- Local model serving with capacity, context and concurrency treated as designed limits rather than discovered ones.
- Retrieval & memory
- Vector and object storage inside the boundary, with the same classification and provenance rules as the rest of the data estate.
- Agent runtime
- Where local models are used agentically, the Secure Agentic AI control plane applies unchanged.
- Infrastructure
- Virtualisation, isolation, storage, secrets and network policy — a platform engineering problem before it is an AI one.
- Observability
- Throughput, latency, cache behaviour, saturation and failure telemetry, alongside the security events the agent control plane emits.
Capabilities
- Local inference architecture with explicit capacity and context limits
- Open-model selection and evaluation against your workload
- In-boundary retrieval, embeddings and object storage
- Isolation, secrets and network policy for inference workloads
- Full performance and saturation telemetry
- Integration with the agent control plane where agents are involved
Security controls
- Inference workloads isolated from general-purpose compute
- Model artefacts verified and version-pinned before deployment
- Retrieval corpora classified and access-controlled like any other dataset
- No egress by default from the inference boundary
- Prompt, retrieval and completion telemetry retained under a stated policy
Integration approach
KOR IT operates a sovereign AI lab environment and publishes measurements from it. Those figures describe that specific configuration and workload — they are a demonstration that the architecture runs and can be measured, not a performance promise for another estate.
- Establish which workloads genuinely require in-boundary inference, and which do not.
- Size the platform against a real workload profile rather than a benchmark.
- Build the infrastructure — isolation, storage, secrets, network policy — first.
- Deploy inference and retrieval with version-pinned, verified artefacts.
- Instrument throughput, latency, cache behaviour and saturation from day one.
- Apply the agent control plane where the models are used agentically.
Expected outcomes
- AI capability for workloads whose data cannot leave the boundary
- Capacity limits that are designed and measured rather than discovered in production
- Inference infrastructure operated with the same rigour as the rest of the platform
- Telemetry sufficient to reason about both performance and security
Limitations
Local inference is not universally cheaper or better. For high-volume, latency-sensitive or frontier-capability workloads, hosted models frequently remain the right answer. The decision is a workload-by-workload one, and we will say so when the analysis points that way.