Sarja

Private AI Infrastructure

Run AI on infrastructure you control.

We design, deploy and look after private AI environments — from a single GPU workstation to a server room — so sensitive workloads run where you decide.

Plan your environmentHardware from SPM

Not every workload belongs on a public AI platform.

Sensitive records, high-volume internal work and systems that reach deep into your network all raise the same question: where does the data go, and who controls it?

  • Sensitive data

    Client records, contracts, health or financial data that shouldn't leave your environment.

  • Heavy internal use

    High-volume, everyday workloads where owning the capacity makes more sense than paying per request.

  • Deep integration

    Systems that need to reach internal databases and applications without exposing them.

What you get

The whole environment, not just the servers.

Hardware is only useful once models, knowledge, access and operations are in place. We deliver and look after all of it.

  • GPU workstation and server design
  • Private model hosting and inference
  • Linux AI environments and model runtimes
  • Storage and networking
  • Local and cloud hybrid architecture
  • Private knowledge and retrieval systems
  • Identity and access integration
  • Monitoring, updates and lifecycle management

Deployment options

  • GPU workstationA team, a contained workload or model evaluation
  • Server environmentModels, knowledge systems and internal apps for wider use
  • HybridPrivate capacity for sensitive work, provider models for the rest

What changes when AI runs in your environment.

Private infrastructure gives you much more control. It also moves responsibility for security, capacity and operations to you — which is why we design and run it with those built in.

  • Model hosting

    Public AI service: Runs on the provider's infrastructure, on their terms and update schedule.

    Private environment: Selected, hosted and updated on infrastructure you control.

  • Data flows

    Public AI service: Prompts, documents and context are sent to an external service.

    Private environment: Sensitive workloads can be processed inside your environment.

  • Access

    Public AI service: Managed through the service's own accounts and settings.

    Private environment: Follows your identity, roles and network rules.

  • Storage

    Public AI service: Retention follows the provider's policies.

    Private environment: Logs, indexes and outputs are stored where you decide.

  • Integration

    Public AI service: Limited to what the service exposes and can reach.

    Private environment: Models connect to internal systems inside your network.

How we deliver it.

  1. Step 1

    Assess

    Workloads, data, users and constraints.

  2. Step 2

    Architect

    Hardware, models, network and access design.

  3. Step 3

    Build and deploy

    Install, configure, harden and test.

  4. Step 4

    Integrate

    Connect identity, knowledge sources and applications.

  5. Step 5

    Operate

    Monitor, update and improve over time.

Usually delivered alongside

Considering private AI infrastructure?

Tell us about the workloads, data and people involved. We'll help you work out whether private, hybrid or provider-based AI fits — and what it would take.

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