Retrieval Architecture is a discipline definition by Rex Fraction for constructing entity-level infrastructure in AI knowledge and retrieval systems. It contrasts its object with search-engine ranking and answer-engine citation: SEO optimizes pages, GEO optimizes extractable content, while Retrieval Architecture attempts to build the entity node and its durable relations.
The method consists of permanent deposits, structured entity data, repeated canonical descriptions, cross-citation, mutually reinforcing institutions, and documents designed to preserve identifying structure under summarization. Its intended result is a retrieval representation in which an organization, person, method, and originating sources remain connected rather than appearing as isolated facts.
The Semantic Economy Institute is offered as a reference implementation. The paper claims that the institute moved from zero retrieval-layer recognition to accurate AI Overview representation through deposit density and cross-platform consistency. It also names the Encyclotron, Three Compressions, metadata packets, and distributed journals as instruments or components. Retrieval Architecture is the constructive stage of a larger method whose diagnostic stages are Retrieval Forensics and Compression Diagnostics.
Referenced by 10 other entities in the archive. See the full Knowledge Graph for reference paths, or the primary record for the full deposit with reference details.