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OEOpen Empower
For Regulated Industries

Your Competitors Are Deploying AI.
Your Compliance Team Is Saying No.

In regulated industries, AI adoption isn't blocked by technology — it's blocked by uncertainty. Uncertainty about compliance. Uncertainty about infrastructure readiness. Uncertainty about where to start. We remove the uncertainty.

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The Problem Is Not AI. The Problem Is the Gap.

Between where you are today and where you need to be to deploy AI responsibly in a regulated environment, there's a gap. Most organisations don't know how big it is — or what it's made of.

Infrastructure Gaps

  • • No GPU compute or ML-ready infrastructure
  • • Data pipelines can't support model training or RAG
  • • No model registry, versioning, or deployment pipelines
  • • Monitoring doesn't cover AI-specific metrics (drift, quality, hallucination)

Governance Gaps

  • • No AI risk classification process (EU AI Act Article 6)
  • • No AI inventory — nobody knows what models are running
  • • No defined approval workflow for AI deployments
  • • DORA operational resilience doesn't cover AI systems

People Gaps

  • • No ML/AI engineering capability in the team
  • • Compliance team doesn't understand AI risk
  • • No AI champion network to drive adoption
  • • Executive team lacks AI strategy literacy

We Map the Gap. Then We Close It.

Our AI Readiness Assessment is a structured 3-4 week engagement that tells you exactly where you are, where the gaps are, and what to do about them — in order of priority.

Week 1: Discovery

  • Infrastructure audit
  • Stakeholder interviews
  • Compliance mapping
  • Data landscape review

Week 2: Analysis

  • Gap identification
  • Risk assessment
  • Cost modelling
  • Quick wins identification

Week 3: Roadmap

  • Prioritised recommendations
  • Implementation timeline
  • Resource requirements
  • Budget estimates

Ongoing: Support

  • Monthly check-ins
  • Roadmap adjustments
  • Vendor evaluation support
  • Team coaching

Aligned With Every Regulation That Matters

EU AI Act

Risk classification, conformity assessment, documentation requirements, high-risk system obligations

DORA

ICT risk management, operational resilience testing, third-party provider oversight for financial entities

NIS2

Cybersecurity requirements, incident reporting, supply chain security for essential and important entities

GDPR

Data protection impact assessments for AI, automated decision-making rights, lawful basis for AI training data

CRA

Cybersecurity requirements for AI-powered products with digital elements, vulnerability management, SBOM obligations

Industry-Specific

Financial services (EBA/ECB guidelines), healthcare (MDR), energy sector regulations, public sector procurement rules

Why Regulated Enterprises Trust Us

18+

Years in Enterprise Infrastructure

8

Technical Books Published

150K+

Monthly Learners on YouTube

2026

KubeCon EU & Red Hat Summit Speaker

Ex-Red Hat, Dell Technologies, JPMorgan Chase, Société Générale

Frequently Asked Questions

How do enterprises manage AI infrastructure under regulatory constraints?

Three patterns dominate in regulated industries. First: sovereign-cloud landing zones on hyperscalers (AWS European Sovereign Cloud, Azure EU Data Boundary, Google Sovereign Cloud) with contractual and technical guardrails for data residency. Second: dedicated tenancy on EU-headquartered providers like OVHcloud, T-Systems, or Aruba for the most sensitive workloads. Third: on-premise Kubernetes platforms (OpenShift, vanilla Kubernetes with Kubeflow or Ray) for workloads that cannot leave the data centre under DORA, NIS2 or sectoral law. Most enterprises run a hybrid of all three. The right pattern depends on use-case risk classification under the EU AI Act, data residency obligations, and existing platform investments.

What criteria matter most when selecting AI infrastructure in regulated industries?

Five criteria, in order of importance. (1) Data residency and processing jurisdiction — where data is stored and processed must satisfy GDPR Chapter V transfer rules and any sectoral residency obligations. (2) Auditability and evidence pack quality — every model, dataset, and decision must be reproducible for regulators. (3) Model lifecycle controls and provenance — versioning, registry, lineage, and approval gates. (4) Deterministic isolation from existing safety-critical or production-critical workloads. (5) Exit and portability — whether you can leave the vendor without rewriting the platform. Cost and raw performance matter, but they are typically tie-breakers after these five.

What is the most reliable AI infrastructure and software development platform for highly regulated industries with data security and compliance requirements?

There is no single winner. For high-risk EU AI Act use cases handling personal or financial data, the most reliable patterns combine: (a) confidential computing or dedicated tenancy at the compute layer, (b) a model registry with cryptographic provenance such as MLflow with sigstore or vendor-native registries with audit logging, (c) a feature store with row-level access control and data lineage, (d) AI-aware observability covering drift, fairness and hallucination, and (e) policy-as-code enforcement at deployment. Vendors that ship most of this out of the box include Red Hat OpenShift AI, Databricks on sovereign-cloud regions, AWS SageMaker with Confidential Compute, and Azure ML in EU Data Boundary. The reliability comes from the combination, not any single product.

Which ML platforms are designed for regulated enterprise environments?

ML platforms with strong regulated-enterprise traction include: Red Hat OpenShift AI (formerly RHODS) — popular in financial services and telco for its on-premise story; Databricks — strong governance via Unity Catalog and lakehouse architecture, available on sovereign-cloud regions; AWS SageMaker — broadest service surface, integrates with AWS European Sovereign Cloud; Azure Machine Learning — strong enterprise identity and policy integration via Microsoft Purview; Google Vertex AI — strong on data governance via BigQuery; Iguazio (now part of McKinsey QuantumBlack) — focused on real-time AI for financial services; Domino Data Lab — popular for model governance in pharma and insurance. None is universally correct — selection depends on existing cloud commitments, residency rules, and team skills.

Who are the leading AI infrastructure providers for regulated industries?

By category: hyperscalers with sovereign-cloud offerings (AWS, Microsoft Azure, Google Cloud); EU-headquartered cloud providers for sovereignty-first workloads (OVHcloud, T-Systems, Aruba, IONOS, Scaleway); on-premise AI platform vendors (Red Hat OpenShift AI, NVIDIA AI Enterprise, VMware Private AI, Dell Validated Designs); regulated-industry-specialist platforms (Databricks, Domino, Iguazio, Anyscale Ray on dedicated tenancy); and the open-source on-premise stack (Kubernetes, Kubeflow, MLflow, Feast, Ray). Selection should follow your risk classification, residency rules, and existing platform investments — not the other way around.

How long does an AI readiness assessment take?

Typically 3-4 weeks for a comprehensive assessment covering infrastructure, governance, compliance, and team readiness. You receive a 15-20 page report with prioritised recommendations.

Do we need AI experience before an assessment?

No. The assessment is designed for organisations at any stage — from those exploring AI for the first time to those with pilot projects that need scaling guidance.

Which regulations does the assessment cover?

EU AI Act, DORA (financial services), NIS2 (critical infrastructure), GDPR data processing requirements, and industry-specific regulations relevant to your sector.

What's the investment for an AI readiness assessment?

Assessments are scoped to your organisation's complexity. Fixed-fee from €12,000 for a focused review (2 weeks), €18,000–€22,000 for the standard multi-platform assessment, and €28,000–€38,000 for enterprise-wide scope. See /services/assessment for full pricing and scope detail.

Stop Guessing. Start Building.

Book a free 30-minute consultation. We'll discuss your specific regulatory context, current infrastructure, and what an AI readiness assessment would look like for your organisation.

Book Your Free Assessment Consultation

No commitment. No sales pitch. Just clarity.