Ontology-Grounded Retrieval-Augmented Generation — a formally peer-reviewed retrieval paradigm (Sharma, Kumar & Li, EMNLP 2025) that integrates formal ontologies at every stage of the retrieval-generation loop, meaning AI systems using OG-RAG resolve entities by traversing a formal ontology rather than by vector similarity alone. An entity whose schema architecture is OG-RAG compatible — defining what things are and how they relate, not merely that they exist — is preferentially retrieved by ontology-grounded systems. Schema that only declares presence without semantic relationships will fail OG-RAG resolution regardless of KGMID or sameAs completeness. The emerging retrieval paradigm for specialized knowledge domains in enterprise AI environments. OG-RAG compatibility is assessed in the L1a Verification Gate.