What Semantic search means in practice
Semantic search is the umbrella term for any search that matches on meaning rather than literal keyword overlap. Under the hood it is dense vector retrieval over embeddings. The user-visible benefit is that "how do I close an account" matches "account closure procedure" without the user needing to know the canonical term. The user-visible failure is that "INV-8429-2026" doesn't match "INV-8429-2026" because invoice numbers aren't in the embedding model's semantic space. This is why production enterprise search uses hybrid retrieval rather than pure semantic search.
Related terms
Embedding →
A dense numeric vector representation of a piece of text (or image, or audio) such that semantically similar inputs produce vectors close in the embedding space.
Hybrid retrieval →
Combining dense vector search with sparse keyword search (BM25), then fusing the results — typically yielding 15–25% accuracy lift over either alone.
Retrieval-Augmented Generation (RAG) →
A pattern where, instead of relying solely on the LLM's training, the system retrieves relevant documents from a knowledge base and includes them in the prompt as context.
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The Sovereign AI Playbook.
Enterprise generative AI that runs on your servers.
- 01Definition. The four non-negotiable properties of a sovereign deployment — and the three patterns that look sovereign but aren't.
- 02Why now. Three forces that converged in 2026: regulator pressure, the open-weights capability gap closing, and unit economics flipping.
- 03Reference architecture. The five-layer stack we deploy: applications → agent runtime → sovereign LLM → RAG + KB → data stores. With model and tooling choices for each.
- 04The regulators. SAMA · RBI · UK ICO · EU AI Act · DPDP · HIPAA — what each requires and how it maps to architecture.
All 62 terms, in plain language
Sovereign AI, RAG, agentic AI, IDP, MLOps and the regulations that shape enterprise AI.