Platform

Architecture ModelArchitecture model for private, governed, and deployable AI systems.Model RegistrySovereignty tiers and model selection guidance for private AI infrastructure.

Operating Layers

LabsTechnical experiments and implementation proofs for SovAIHub patterns.

Sovereign Platforms

Air-Gapped Sovereign AI PlatformFlagship solution path for disconnected, governed, and sovereign AI workloads.Sovereign AI FinOpsComplete cost, utilization, unit economics, and business value for controlled AI.Sovereign EvalOpsOffline-first regression tests and CI/CD release gates for RAG, LLMs, and agents.AI System PassportVersioned ownership, risk, architecture, BOM, evaluation, authority, cost, and change evidence.Sovereign Edge AI & TinyMLEdge AI, TinyML, and private inference patterns for constrained or distributed environments.

Runtime & Governance

Private RAG for Enterprise DocumentsPrivate document assistants with retrieval, grounding, citations, and review controls.Local LLM RAGRAG workflows using local model runtimes, grounded prompts, citations, and audit logs.Internal Artifact Supply ChainApproved images, packages, prompts, tools, checksums, and offline build sources.Controlled Agent RuntimeAgent workflows with approved tools, blocked actions, policy checks, and audit trails.AI Governance & Hallucination ControlEvidence checks, answer validation, citation verification, and escalation patterns.

Interactive Tools

Hardware-to-Model MatchmakerSee which open-weight LLMs fit your GPU, at which quantization, with headroom.EU AI Act Risk CheckerVersioned orientation for scope, role, risk, Article 6(3), Article 50, and current dates.Sovereign AI FinOps CalculatorCompare private AI TCO, managed inference, utilization, unit cost, and business value.Readiness Checklist43-point sovereign AI audit across six control domains with instant scoring.

Guides & Services

Token Cost CalculatorEstimate monthly LLM inference costs with RAG retrieval overhead included.Model Selection GuideDecision tree from data sensitivity and infrastructure to a specific model path.Hardware Sizing GuideEstimate GPU memory from model size, quantization, context, and concurrency.Architecture ReviewFixed-scope review: model stack, hardware sizing, topology, and compliance posture.All ResourcesEvery free tool, checklist, diagram pack, and test kit in one place.

Services Articles About

Readiness Checklist

Interactive Tool

Sovereign AI Readiness Checklist

Check each item your organisation currently has in place. Your score updates live and your progress is saved in your browser — nothing is uploaded anywhere.

All training and inference data is classified by sensitivity level

Data residency requirements are documented per data class

PII and regulated data is identified and access-controlled

Data does not leave approved network boundaries during inference

Ingestion pipelines strip or mask sensitive identifiers before embedding

Retention and deletion policies exist for vector store content

Access logs are maintained for all document and data access

Third-party data sharing agreements reviewed for AI workloads

Model sovereignty tier is defined (Fully Sovereign / Conditional / Not Sovereign)

Approved model list exists and is version-controlled

Self-hosted models are deployed on infrastructure the organisation controls

Cloud-managed models are within tenant boundary with data residency verified

Model update and version change process is documented and approved

Fallback model or degraded-mode behavior is defined

Model licensing reviewed for commercial and enterprise use

AI services are deployed in isolated environments (containers, namespaces, or VMs)

Secrets, API keys, and credentials are managed via a secrets vault

Network egress is restricted for AI service containers

Infrastructure-as-code exists for all AI service deployments

CI/CD pipeline includes security scan and approval gate before AI service release

Rollback procedure is documented and tested for AI service deployments

Environment separation enforced: development, staging, and production are isolated

GPU or compute resources are sized and monitored with alert thresholds

AI use policy is documented and approved by leadership

Approved use cases are defined — prohibited uses are explicitly listed

Human review process exists for AI outputs before they affect decisions

Audit trail captures prompts, retrieved sources, responses, and user identity

Incident response procedure exists for AI system failures or harmful outputs

Regulatory obligations (GDPR, HIPAA, AI Act) mapped to AI system controls

Risk assessment completed for each deployed AI use case

Vendor and third-party AI tools reviewed against data processing agreements

AI system inventory is maintained and reviewed quarterly

Hallucination detection or source-grounding check is implemented

Response confidence scoring or uncertainty signalling is in place

Prompt injection and jailbreak input validation is active

Output filtering for sensitive content categories is configured

Evidence citations are returned with answers where retrieval is used

Token usage is tracked per user, service, and model

Inference latency is monitored with SLA thresholds defined

Monthly AI cost is reported and reviewed against budget

Retrieval quality metrics (relevance, recall) are measured for RAG workloads

Alerts configured for cost spikes, latency degradation, and error rates

Evaluation runs are scheduled to detect model quality drift over time

Your score

0%

0/43

Not Ready

Significant gaps across multiple domains. Do not deploy AI with sensitive data at this stage.

Data Boundary

0/8

Model Selection

0/7

Deployment Control

0/8

Governance & Compliance

0/9

Guardrails & Validation

0/5

Observability & Cost

0/6

Free next step

Run a current EU AI Act orientation covering operator role, intended purpose, Article 6(3), Article 50, and the 2026 AI Omnibus timeline.

Run the EU AI Act Risk Checker

Private AI systems, deployable RAG kits, agent architecture, governance patterns, and implementation playbooks for builders and teams.

Main

Products Solutions Resources Services Articles About

Products

SovAI Air-Gap AI Starter SovAI Air-Gap AI Starter v0.2 with Ollama SovAI Air-Gap Internal Artifact Hub SovAI Architecture Pack SovAI Private RAG Kit SovAI OpenShift AI Deployment Pack SovAI Hallucination Guard

Contact

contact@sovaihub.com

SovAIHub is an independent AI architecture and solutions initiative focused on sovereign AI, private RAG, air-gapped systems, local LLMs, and controlled AI deployments by Rana Kumar. The work is created on personally controlled equipment, accounts, subscriptions, and personal time outside employment hours. The views are personal and do not represent any employer or client. No confidential, proprietary, employer-owned, or client-specific information is used.

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