Vendor-neutral implementation and assurance for sovereign AI
SovAIHub turns architecture and policy into private, portable, and auditable AI workloads across on-premises, cloud, Kubernetes/OpenShift, and air-gapped environments.
What SovAIHub Is
Infrastructure platforms provide compute and deployment boundaries. SovAIHub focuses on what runs inside them: private RAG, approved artifacts, controlled agents, release evaluation, system evidence, and operating economics.
Define where data, models, prompts, tools, and runtime services are allowed to operate.
Use approved images, packages, models, prompts, and tools instead of uncontrolled public pulls.
Ground outputs in retrieved evidence, citations, validation rules, confidence checks, and escalation paths.
Track latency, token use, model behavior, retrieval quality, tool calls, audit events, and deployment health.
Solutions
Choose a solution pattern based on data sensitivity, runtime restrictions, model strategy, deployment boundary, and governance needs.
Flagship solution path for disconnected, governed, and sovereign AI workloads.
Explore solutionComplete cost, utilization, unit economics, and business value for controlled AI.
Explore solutionOffline-first regression tests and CI/CD release gates for RAG, LLMs, and agents.
Explore solutionVersioned ownership, risk, architecture, BOM, evaluation, authority, cost, and change evidence.
Explore solutionEdge AI, TinyML, and private inference patterns for constrained or distributed environments.
Explore solutionPrivate document assistants with retrieval, grounding, citations, and review controls.
Explore solutionRAG workflows using local model runtimes, grounded prompts, citations, and audit logs.
Explore solutionApproved images, packages, prompts, tools, checksums, and offline build sources.
Explore solutionAgent workflows with approved tools, blocked actions, policy checks, and audit trails.
Explore solutionEvidence checks, answer validation, citation verification, and escalation patterns.
Explore solutionAvailable Kits
Begin with working reference implementations, then adapt the patterns to your own data, infrastructure, model runtime, and governance requirements.
Open-source laptop-ready starter kit for demonstrating an air-gap-ready sovereign AI runtime with local documents, approved tools, offline Docker bootstrap, and audit logs.
Open-source local LLM RAG starter that runs private retrieval, grounded prompting, Ollama inference, citations, and audit logging without an external LLM API.
Open-source reference implementation for a controlled internal AI artifact supply chain with local registry, wheelhouse, prompt/tool manifests, approvals, and offline builds.
Resources
Use the resource library to assess readiness, orient AI Act classification, model fully loaded cost and value, select models, size hardware, and test RAG behaviour before production.
Pick your GPU and instantly see which open-weight LLMs you can self-host, at which quantization, with headroom.
Open resourceRun a versioned orientation for EU scope, operator role, intended purpose, Article 6(3), Article 50, and related data-law flags.
Open resourceCompare fully loaded private AI TCO with managed inference, then measure utilization, unit cost, accepted outcomes, and value.
Open resourceAssess data boundaries, model choice, deployment control, governance, guardrails, and observability.
Open resourceEstimate API and self-hosted LLM costs with RAG overhead, embeddings, reranking, and infrastructure inputs.
Open resourceChoose a model path based on data sensitivity, deployment boundary, workload, hardware, and governance.
Open resourceEstimate GPU memory, quantization fit, context overhead, concurrency, and local deployment targets.
Open resourceTest groundedness, citations, no-answer behavior, stale documents, prompt injection, and access controls.
Open resourceArticles
Technical guides on private RAG, sovereign AI, deployment patterns, observability, and practical enterprise AI architecture.
The 2026 AI Omnibus changed the high-risk AI timeline while Article 50 transparency duties still apply from August 2, 2026. Here is the current engineering-oriented map.
Air-gapped AI is not just offline inference. It is an architectural discipline.
A senior engineer's framework for classifying, detecting, and systematically eliminating hallucination across the full RAG pipeline — from retrieval quality to atomic fact verification, NLI-based validation, and continuous production monitoring.
A senior engineer's guide to building enterprise-grade private RAG systems — covering advanced retrieval pipelines, access-layer design, agentic patterns, evaluation frameworks, and regulatory compliance.
A practical explanation of sovereign AI, private AI systems, data control, model choice, and enterprise governance.
Implementation Planning
Share your use case, deployment boundary, data sensitivity, model strategy, and governance requirements to map a practical implementation path.
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