Your data. Your hardware.
Your AI.

Air-gap capable means the AI system can operate with zero outbound internet connectivity at inference time. No model API calls leave your network, no telemetry or usage data is transmitted externally, no cloud service dependency exists during production operation. The entire inference stack — model weights, tokenizer, serving framework, and application layer — runs within your network perimeter. We have deployed fully air-gapped systems in defense-adjacent contexts, critical infrastructure, and financial institutions with strict network segmentation policies. Air-gap capable does not mean no external access during deployment and model updates — those can be handled through controlled, auditable transfer processes. It means that once deployed, the system produces no outbound traffic as part of normal operation. This architecture eliminates a class of data exfiltration risk that cloud-hosted AI cannot address regardless of contractual controls.

On optimized on-premise hardware, we achieve 173 tokens per second on a $30,000 GPU node — comparable to cloud inference throughput for most enterprise workloads. For high-throughput production use cases, we scale horizontally across multiple nodes to match any required capacity. Latency for internal applications is typically lower than cloud deployments because there is no WAN round-trip: the model is co-located with the application server, not a remote API call away. The performance comparison depends heavily on workload: batch processing, real-time generation, and retrieval-augmented generation have different hardware profiles. We benchmark your specific workload before recommending a hardware configuration, ensuring you do not over-provision for batch jobs or under-provision for latency-sensitive real-time applications. Most clients achieve performance parity with cloud providers at equivalent hardware cost within 18 months.

GDPR, DORA, NIS2, and HIPAA are our four primary compliance frameworks, covering financial services, healthcare, and critical infrastructure sectors in the EU and US. We have also supported ISO 27001 certifications, SOC 2 Type II audits, and sector-specific regulatory requirements including MiFID II data governance requirements for financial instruments, CMIA for healthcare organizations in California, and CMS billing requirements for Medicare and Medicaid providers. For each deployment, we document data flows, model provenance, access controls, and audit logging in a format suitable for submission to your compliance team, external auditors, or regulatory bodies. We are not a compliance advisory firm — we do not provide legal opinions — but we architect systems that make compliance evidence collection straightforward and ensure your technical controls align with your documented policies.

No. Hardware procurement is part of the engagement, not a prerequisite. We scope compute, GPU, storage, and networking requirements during the initial assessment phase based on your workload profile — the type of tasks you need the AI to perform, expected request volumes, latency requirements, and data retention needs. We provide reference architectures for common workload profiles that avoid over-provisioning for batch workloads and under-provisioning for real-time generation tasks. We can procure hardware on your behalf, advise your internal procurement team, or work within an existing procurement framework if you have preferred vendors. Lead times for GPU hardware vary — we account for this in deployment timelines and can structure the engagement to maximize productive work during hardware procurement. Most clients receive a detailed hardware specification within the first two weeks of engagement.

We deploy open-weight models under permissive licenses including the Llama family (Meta), Mistral, Mixtral, Qwen (Alibaba), Phi (Microsoft), Falcon, and others as the open-source landscape evolves. We also work with enterprise-licensed models that include on-premise deployment rights — several major model providers now offer enterprise agreements that allow self-hosted inference. Model selection is driven by four factors specific to your deployment: accuracy requirements on your task types, hardware constraints (GPU memory, compute budget), compliance posture (some organizations cannot use models trained on certain data), and inference speed requirements. We run systematic benchmarks across candidate models on representative samples of your actual workload before recommending a model family. We do not recommend a single model to all clients — the right model for a healthcare clinical note summarization task differs substantially from the right model for a financial contract analysis task.

We provision automated model update pipelines, real-time monitoring dashboards, performance alerting, and operational runbooks as part of every Sovereign AI deployment. After the initial deployment and 30-day hypercare period, typical ongoing maintenance burden on your internal team is 2 to 4 hours per month — covering review of monitoring alerts, approval of model updates, and coordination on any infrastructure changes. This low maintenance burden is by design: we automate the operational tasks that would otherwise require dedicated MLOps staffing. For organizations that prefer to have no ongoing maintenance burden, we offer optional managed operations covering model updates, performance monitoring, incident response, and quarterly optimization reviews. Managed operations clients receive a defined SLA for incident response and access to our engineering team on a retained basis. Pricing is based on deployment complexity and response time requirements.