01DEFINE
Start with the use case
Separate training, fine-tuning, and inference needs. Capture model sizes, concurrency, response-time targets, and data sensitivity before sizing infrastructure.
Private AI & Sovereign Infrastructure
Your AI. Your data. Your control.
Plan AI infrastructure around workload needs, data control, operational ownership, and the freedom to evolve.
From strategy to execution
Choose where AI runs—and who controls it.
Private deployment is one part of the decision. A resilient AI architecture also considers administrator access, encryption keys, support dependencies, software supply chains, and the ability to move workloads.
01Match infrastructure to the workload
02Define data and operational control requirements
03Understand capacity, cost, and dependencies
04Validate a practical deployment architecture
Assessment and delivery scope
A practical path forward.
Work with IRIS to define the appropriate scope, evidence, and outcomes for your environment. Implementation follows verified product support, technical feasibility, and an agreed statement of work.
02DESIGN
Plan compute, storage, and networking together
Evaluate GPU and CPU capacity, memory, storage throughput, and network performance as one architecture, including power, cooling, and growth constraints.
03CONTROL
Map sovereignty requirements
Identify acceptable data locations, operator access, key custody, support arrangements, and jurisdictional dependencies. Translate requirements into procurement and architecture criteria.
04INTEGRATE
Connect AI to enterprise systems
Plan identity integration, secure data access, segmentation, platform management, backup, and monitoring across private and hybrid environments.
05PROTECT
Evaluate confidential computing where appropriate
Assess supported hardware-backed environments for protecting data during processing. Check workload compatibility, attestation, and the boundaries of protection.
06VALIDATE
Pilot performance and operational cost
Benchmark representative workloads, utilization, availability, recovery, and portability. Compare measured results with business acceptance criteria.
Practical questions
Make an informed decision.
Does on-premises automatically mean sovereign?
No. Location alone does not establish sovereignty. Operational control, supplier dependencies, key ownership, and access arrangements also matter.
Do we need to build a large AI factory?
Not necessarily. Size the platform around defined workloads and growth assumptions. A smaller inference environment may be more appropriate than a training cluster.
Does this guarantee local regulatory compliance?
No. Applicable obligations require a specific review. Architecture decisions should follow your legal, contractual, and sector requirements.
Industry perspective
The context behind the conversation.
Gartner’s 2026 trends include AI supercomputing platforms, confidential computing, and geopatriation. IDC’s July 2026 European infrastructure analysis discusses private and sovereign AI deployment.
These public references provide industry context. Engagement recommendations are tailored to your organization.
Plan your next step
Turn your priorities into a workable plan.
Bring your use cases, existing platforms, and business requirements. We will help define the next assessment or pilot.