Migrate your AI data and models to air-gapped private clouds or sovereign providers in under 8 weeks, ensuring all processing remains within mandated borders.
Service
Architecture review before implementation
Implementation scope and rollout planning
Clear next-step recommendation
Migrate AI workloads from global clouds to sovereign infrastructure for full jurisdictional control and compliance.
Migrate your AI data and models to air-gapped private clouds or sovereign providers in under 8 weeks, ensuring all processing remains within mandated borders.
We execute a zero-downtime migration of your existing AI workloads, including:
This transition locks your proprietary contextual data within sovereign borders while maintaining operational performance.
Our migration delivers:
We provide the technical architecture to replace global cloud dependencies with sovereign control, a critical step in Geopatriated Data Lake Design.
Migrating AI workloads to sovereign infrastructure is a complex technical undertaking. We deliver measurable business value by ensuring compliance, enhancing security, and maintaining operational excellence.
Achieve full compliance with data residency mandates like the EU AI Act, China's DSL, and India's DPDPA. We architect systems where data and processing remain within sovereign borders, eliminating regulatory risk and enabling market access.
Deploy AI inference closer to your regional users and data sources. By leveraging local sovereign clouds, we eliminate the latency and egress costs associated with global hyperscalers, improving application performance and reducing TCO.
Mitigate supply chain and geopolitical risks by air-gapping critical AI models and training data. Our migration designs incorporate confidential computing and hardware security modules (HSMs) to protect intellectual property from external threats.
Leverage our pre-built blueprints and automation for sovereign cloud providers like OVHcloud, Gaia-X, and Yandex Cloud. We streamline the migration of complex data pipelines and ML workloads, moving from assessment to production in weeks, not months.
A structured, risk-managed approach to migrating your AI workloads from global public clouds to sovereign infrastructure. Each phase delivers specific, measurable outcomes.
| Phase & Key Activities | Timeline | Primary Deliverables | Success Metrics |
|---|---|---|---|
Phase 1: Discovery & Compliance Mapping | 2-3 weeks | Sovereignty Gap Analysis Report, Data Lineage Map, Jurisdictional Risk Assessment | 100% of data assets cataloged, Legal requirements mapped to technical controls |
Phase 2: Architecture & Landing Zone Design | 3-4 weeks | Sovereign Cloud Blueprint, Security & Access Control Matrix, Data Residency Gateway Design | Architecture approved by legal & security teams, All data flows defined |
Phase 3: Pilot Migration & Validation | 4-6 weeks | 1-2 Critical Workloads Migrated, Performance Baseline Report, Rollback Playbook | Pilot workloads operational with ≤5% latency increase, 99.9% uptime SLA met |
Phase 4: Full Production Migration | 6-10 weeks | All AI Models & Pipelines Migrated, Automated Compliance Monitoring Dashboard, Operational Runbooks | Zero data sovereignty violations, Full cutover with <2 hours downtime |
Phase 5: Optimization & Federated Integration | Ongoing | Cost-Optimized Resource Scaling, Federated Learning Node Setup (Optional), Continuous Compliance Auditing | 20-40% reduction in sovereign cloud spend, Secure model sharing enabled |
Ongoing Support & Governance | Post-Migration | Dedicated Technical Account Manager, Quarterly Security Reviews, Access to Sovereign AI Updates | Proactive issue resolution, Adherence to evolving regional mandates |
Global enterprises in regulated sectors face mounting pressure to relocate AI workloads from public clouds to sovereign infrastructure. These industries require proven migration expertise to maintain innovation while ensuring full jurisdictional control over data and models.
Enabling Efficiency, Speed & Accuracy
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
Answers to common questions about migrating AI workloads to sovereign or private infrastructure, ensuring compliance and control.
Our standard migration framework delivers a fully operational sovereign AI environment in 2-4 weeks. This includes assessment, data pipeline re-engineering, model porting, and validation. Complex multi-model deployments or legacy system integrations may extend to 6-8 weeks. We provide a detailed project plan with weekly milestones during the initial discovery phase.
About the author
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
The first call is a practical review of your use case and the right next step.
