Private platform architecture
Design across Linux, virtualization, Proxmox, Kubernetes, GPU resources, storage and networking around the intended workload.
A supporting engineering capability
Put private AI on a foundation your team can understand and operate. SDS designs model-serving infrastructure, integrations and controlled automation with explicit data access, service health and recovery requirements.
Discuss private AI infrastructureStart with the workload
Define where inference runs, what it can access and how the service behaves when an integration or infrastructure component fails.
Design across Linux, virtualization, Proxmox, Kubernetes, GPU resources, storage and networking around the intended workload.
Connect private inference and APIs to approved applications and workflows. Make identity, privileges and tool access explicit.
Use Ansible, APIs and approved scripts for controlled deployment. Add telemetry, health checks and recovery procedures that operators can follow.
Engineering with a purpose
Private AI can support evidence correlation, investigation and reporting in human-directed security operations. Start with a defined use case and acceptance criteria.
A scoped pilot tests the proposed design against technical and operating requirements before it becomes a dependency. Capacity, access, integration behavior, service health and recovery are part of that evaluation.
Authorized operators retain change authority. Agent-assisted workflows use scoped tools, privileges, approval gates, audit trails and explicit stop conditions.
Scoped deliverables
A good place to start
The uncertain exposure. The noisy investigation. The change your team needs to get right.