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Sovereign AI needs a
sovereign platform.
PrivateMind keeps your models and data inside a jurisdiction you control. Options Modern Cloud is the OpenShift platform that runs them in production — GPU scheduling, governance, audit, operated 24/7. Prove it on one workload in 30 days.
The bottom line.
PrivateMind is sovereign AI: your models, your data, your jurisdiction — never exposed to public models, audit-ready for SEC, FCA and GDPR. But a model is not a production system. Running one needs GPU scheduling, governed serving, secrets, identity and audit — and that is platform work.
Options Modern Cloud is that platform: Red Hat OpenShift hardened for FSI, with OpenShift AI for the model lifecycle, run 24/7 by PodOps. PrivateMind sits on top. One bill, one SLA, one accountable provider. A Validation Sandbox proves it on one AI workload before you commit.
Why now.
Public AI economics
Token pricing and tier-based rate limits make production agentic AI hard to budget. A throttle event becomes an availability event.
Egress and data gravity
AI moves data constantly, and public cloud meters egress. The cheapest place to run inference is where the data already lives.
DORA & AI oversight
DORA, FFIEC, NYDFS and FCA treat hyperscaler AI as a regulated ICT dependency. Sovereign deployment answers the exit question.
Platform & MLOps gap
You have quant and business talent. Kubernetes, GPU scheduling and MLOps engineers are scarce. PodOps runs the substrate so your team runs the workload.
GPU economics
GPU speedups only appear if the workload is actually scheduled on a GPU, with the right drivers and scaling. That is platform work.
The strategy.
Where It Applies
- FSI firms with sensitive data that cannot reach public AI services
- Engineering-led firms hitting public-API rate limits and unpredictable token bills
- Capital markets firms with AI demand but no platform or MLOps team
- Quant and risk teams with heavy Monte Carlo workloads to accelerate on GPU
Honest Edge Cases
- State-of-the-art frontier reasoning — open models still trail closed frontier models on some tasks
- Fully elastic, bursty, low-volume use — public APIs suit that; sovereign GPU is fixed capacity
- No sensitive data and no regulatory constraint — public AI services may be the right answer
Sovereign stack
Sovereign AI needs a sovereign platform.
PrivateMind gives you sovereign AI. It only holds if the platform underneath is sovereign too. Three layers, one accountable provider.
Layer 3 · AI
PrivateMind — sovereign model serving. Data never leaves your environment; no exposure to public models. Audit-ready for SEC, FCA and GDPR.
Layer 2 · Platform
Options Modern Cloud — Red Hat OpenShift with OpenShift AI: GPU scheduling, governed model serving, guardrails, RBAC and audit.
Layer 1 · Ops + infra
PodOps and global infrastructure — 24/7 managed operations across 70+ data centres, on low-latency networking you already trust.
How it runs on OMC.
GPU capacity lives in the Advanced and Dedicated tiers. Whichever you pick, your models land on the same four-layer stack — the tier changes worker isolation, not the foundation.
Standard
Shared, multi-tenant platform. Fastest way onto FSI-hardened Kubernetes.
Advanced
Dedicated worker pools with tighter data-plane isolation and custom SLAs.
Dedicated
Single-tenant control plane in your jurisdiction. Full sovereignty over data and identity.
PodOps managed service
Options FSI hardening
Red Hat OpenShift
Kubernetes
Validation Sandbox.
A signed 30-day engagement where we stand up a GPU-backed namespace with PrivateMind, run one of your AI workloads, and hand you the evidence: throughput and latency on your scenario, a data-flow map, and a cost view vs your public-API baseline.
- ✓ No procurement, no lock-in, no obligation to continue
- ✓ Your workload, your data, measured on your scenario
- ✓ Named FSI platform architect from day one
Most teams find the model is the easy half; the platform work underneath is what the sandbox proves.
Engagement path.
Discovery
A working session to scope one sovereign AI workload and the success criteria: throughput, latency, cost, audit.
30-day sandbox
PrivateMind runs your workload on a GPU-backed namespace. We measure; you get a decision-ready artifact.
Phased production
A staged rollout of the reference architecture, from infrastructure through PrivateMind, operated by PodOps.
Proof and business case.
Red Hat Premier partner, all three OpenShift specialisations · 600+ FSI customers · 15 years SOC