Clinical AI depends on both the relevance of the information it receives and the meaning of that information. Logica is an interoperability platform that separates clinical meaning from physical data representation and separates language-model interpretation and reasoning from deterministic retrieval. A first language model interprets a clinical question through the COOL semantic graph. Explicit information requirements are compiled into Clinical Quality Language (CQL) over COOL, a logical representation of a query, bound to a physical backend (e.g., as a FHIR query or SQL query), and executed in code. Returned records are normalized into COOL evidence with source provenance before a second language model reasons over them. Deterministic calculations and citation-identifier validation make the resulting answer inspectable. We describe this architecture through a reported creatinine demonstration for a synthetic patient (Demo Patient A), in which a record containing 204 FHIR resources yielded 69 evidence items. The demonstration illustrates the pipeline and its retained artifacts as well as how the system works; however, further evaluation is needed to determine whether it actually improves the saliency of relevant clinical information or clinical reasoning accuracy compared with other approaches. The research hypothesis is that a sufficiently constrained semantic model improves context relevance, reasoning accuracy, and verifiability. Separating logical expressions from backend bindings also provides a basis for sharing clinical logic across institutions.