Private & Sovereign AI

Private AI runs entirely within your own infrastructure — on-premises data centres, co-location facilities, or dedicated hardware. Unlike cloud AI where data traverses third-party servers, private AI ensures that models, training data, and inference requests never leave your security perimeter. This is critical for organisations handling classified information, regulated patient data, or proprietary intellectual property where even encrypted transit to a cloud provider introduces unacceptable risk.

An air-gapped deployment operates with zero connectivity to the public internet. We provision NVIDIA DGX or custom GPU clusters within your secure facility, pre-load foundation models and NeMo microservices onto encrypted media, and configure all dependencies locally. Updates are delivered via verified, signed packages through a physical transfer process. Monitoring and observability run on internal networks only, giving your security team full visibility without any external telemetry.

We architect deployments to respect data residency laws — GDPR in the EU, PDPA in Singapore, LGPD in Brazil, and sector-specific mandates like ITAR in defence. Each deployment gets a data flow map documenting where data is stored, processed, and accessed. For multinational organisations, we design federated architectures where models train locally in each jurisdiction and only share aggregated, non-identifiable parameters across borders when legally permissible.

It depends on workload. For large-scale training and fine-tuning, NVIDIA DGX H100 or DGX B200 systems provide the best performance-per-watt with NVLink interconnects for multi-GPU scaling. For inference-heavy workloads, NVIDIA L40S or H100 NVL cards offer excellent throughput at lower cost. For edge or factory deployments, NVIDIA IGX Orin or Jetson AGX platforms handle real-time inference in ruggedised form factors. We right-size hardware to your actual workload profile rather than over-provisioning.

A standard deployment follows a phased approach. Weeks one through three cover architecture design, security review, and procurement. Weeks four through eight handle hardware installation, network configuration, and base platform deployment. Weeks nine through twelve focus on model deployment, RAG pipeline integration, and user acceptance testing. Total timeline is roughly three months from kickoff to production, though simpler deployments with pre-provisioned hardware can be operational in six weeks.

Private AI has higher upfront capital expenditure — a single DGX H100 system starts around $300,000. However, for sustained workloads running 18 or more hours per day, on-premises infrastructure typically reaches cost parity with cloud GPU instances within 12 to 18 months. Beyond that, you own the hardware outright. We model total cost of ownership including power, cooling, staffing, and depreciation against equivalent cloud spend to help you make an informed decision.

Absolutely. We build integration layers that connect your private AI platform with existing systems — ERP, CRM, document management, data warehouses, and identity providers. APIs are deployed behind your existing API gateway with the same authentication and authorisation patterns your teams already use. We also support event-driven architectures with Kafka or RabbitMQ for real-time data ingestion without requiring changes to upstream systems.

Model security is multi-layered. At the infrastructure level, we enforce encrypted storage, network segmentation, and hardware-based attestation. At the application level, we implement role-based access controls, prompt injection guardrails, output filtering, and comprehensive audit logging. We also conduct red-teaming exercises against deployed models to identify adversarial vulnerabilities before users interact with them. All model artefacts are versioned and signed to prevent tampering.

We have experience deploying AI infrastructure that meets SOC 2 Type II, ISO 27001, HIPAA, FedRAMP (for government-adjacent work), PCI DSS, and NIST AI RMF requirements. During the architecture phase, we map your compliance obligations to specific technical controls and document evidence for auditors. We also provide ongoing compliance monitoring dashboards that track drift from your baseline security posture.

Yes. We offer tiered support ranging from advisory retainers with monthly check-ins to fully managed operations where our team handles model updates, infrastructure scaling, security patching, and performance optimisation. Most clients start with a managed engagement for the first six months while their internal teams ramp up, then transition to an advisory model. We also provide knowledge transfer sessions and runbooks tailored to your operations team.