Sovereign AI: Control of Models, Data, Infrastructure and Dependencies
Sovereign AI is about effective control over models, data, infrastructure, software, operations and strategic dependencies — not simply where an AI model is hosted.
Reference Models
Reference Models
Reference models describe what “good” looks like—independent of tools and vendors.
They define capabilities, controls & evidence, metrics/SLOs, and common anti-patterns.
Models
- Digital Platform — A scalable platform capability map.
- Delivery & Change — Safe, fast, auditable change.
- Security & Trust — Controls-first security and governance.
- LLM Capability — Governance, evaluation, cost, and operations for LLMs.
Articles
MLOps vs LLMOps: What Changes When the Model Is an LLM
MLOps operates machine-learning systems; LLMOps extends those practices to prompts, context, retrieval, providers, tools, evaluations and runtime behavior around large language models.
MCP Explained: What It Connects, What It Does Not Do and Where It Fits
Model Context Protocol connects AI applications to external tools, resources and prompts through a standard client-server boundary. Learn what MCP does, what it does not do, and where it fits in agent architecture.
RBAC vs Tenant Isolation: Two Different Security Boundaries
RBAC controls what a user may do; tenant isolation controls which tenant’s resources that action may reach. Learn why multi-tenant SaaS security requires both boundaries.
What Is Context Engineering? What the Model Receives Before It Answers
Context engineering designs what information an AI model receives before inference, including prompts, retrieval, memory, application state, tool results and conversation history.
Source of Truth in AI Systems: Where Reliable Knowledge Actually Comes From
A Source of Truth defines which source is authoritative for a specific fact or state. Learn how it differs from RAG, provenance, memory, context, vector databases and systems of record.
What Is an AI Platform Architect? Models, Data, Runtime, Security and Operations
An AI Platform Architect designs reusable AI foundations across models, providers, retrieval, agents, identity, security, evaluation, observability and operations.
What Is an AI Solution Architect? System Boundaries, Responsibilities and Trade-offs
An AI Solution Architect turns business requirements into a production-ready AI system across data, models, tools, security, runtime, evaluation and operations.
Generative AI Explained: Models, Retrieval, Tools and Applications Are Not the Same Thing
Generative AI is more than a model. Learn how models, retrieval, tools, context, runtimes and applications fit together in production AI systems.
Agentic AI Explained: When an AI System Can Plan, Use Tools and Act
Agentic AI uses models inside multi-step execution loops where they can choose tools, observe results, update state and adapt their next action within explicit runtime and permission boundaries.
Air-Gapped AI: How AI Systems Work Without Internet or Cloud Access
Air-gapped AI runs models, RAG and AI applications inside an isolated security domain without internet or cloud dependencies. Learn how models, data, updates and tools operate offline.
Vector Databases, Embeddings and Reranking: Three Different Parts of Retrieval
Embeddings represent meaning, vector databases retrieve candidates, and rerankers refine results. Learn how these three retrieval layers differ and work together in RAG.
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