AI Engine Optimization (AIEO) Analytics Framework: 16 Vectors of Generative Authority

The following paper proposes an Artificial Intelligence Engine Optimization (AIEO) Analytical Framework, implemented through the IBRL GEO Inspector, to position entities within Large Language Models (LLMs), reduce excessive computational demands, obtain global latent-space recommendations, and minimize language-model cognitive friction during indexing and Retrieval-Augmented Generation (RAG) processes. Drawing on Sweller’s (1988) Cognitive Load Theory, the framework reduces cognitive friction in language models, minimizes structural entropy, limits Document Object Model (DOM) bloat, and resolves semantic ambiguity that can degrade transformer-based inference engines. The research is grounded in the Kūkan-Ha architectural philosophy, which prioritizes structural silence, algorithmic efficiency, and zero-latency ingestion to evaluate 16 distinct machine-oriented topologies and semantics. These include DOM tree depth analysis limits, multimodal alignment (alt-text equivalences), agent interoperability protocols (/llms.txt), AI routing friction, and deterministic knowledge graph injections via JSON-LD schemas. The proposal details how to audit an entity's reputation using transformer self-attention mechanisms to quantitatively classify its digital footprint into critical blindness, semantic dilution, or an optimized vector. The thesis establishes a roadmap for enterprises to master the generative inference space and strategically position their brand, product, or service across Gemini, ChatGPT, Claude, DeepSeek, and other LLMs.Keywords: AI Engine Optimization (AIEO), Generative Engine Optimization (GEO), Cognitive Friction, Retrieval-Augmented Generation (RAG), Kūkan-Ha Architecture, Kūkan-Ha Framework. IBresearchlab.com / isaiasblanco.ai