As the era of foundation models unfolds, the relationship with information is shifting from retrieval to reasoning. In mission-critical environments (e.g., satellite operations, healthcare, defence, and transportation), adoption of AI remains constrained by the non-negotiables of trust, explainability, and security. This keynote presents a practical blueprint for constructing domain-specific large language models (LLMs) that convert raw signals and dense technical manuals into actionable, citation-linked knowledge under strict operational constraints. An end-to-end stack will be outlined how: (1) a base model is fine-tuned on validated domain corpora; (2) semantic vector retrieval enables version-aware document access; (3) a citation-aware question-answering interface grounds outputs in source material; and (4) deployment patterns are defined for air-gapped infrastructures where confidentiality and regulatory compliance are non-negotiable. A motivating scenario is provided for the design of a Domain-Specific LLM for Satellite Operations.