What Foundation model means in practice
A foundation model is an LLM, vision model, or multimodal model trained on enough breadth that it can be adapted to many downstream tasks without retraining from scratch. The economic argument for foundation models is amortisation: the very expensive pretraining cost is paid once by the model maker, and the cheap adaptation cost is paid per use case by the enterprise. In enterprise practice this means a single sovereign-deployed Llama 3.3 70B can serve a bank's customer-support, internal compliance Q&A, and credit-memo summarisation use cases concurrently, with the per-use-case work confined to retrieval, prompting, and evaluation.
Related terms
Large Language Model (LLM) →
A transformer-architecture neural network trained on very large text corpora to predict the next token in a sequence, producing fluent natural-language output across a wide range of tasks.
Fine-tuning →
Adapting a pretrained model to a specific domain or task by continuing training on a smaller, curated dataset.
Small Language Model (SLM) →
A purpose-built or distilled model in the 1–8 billion parameter range, optimised for a specific domain or task, typically served on commodity GPUs.
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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.