Uri Samet
The transition from traditional "Retrieve-and-Rank" search architectures to what can be termed as "Retrieve-and-Synthesize" in generative environments has structurally changed the paradigm of digital discovery. This paper proposes a "GEO-First" framework, Generative Engine Optimization as the primary strategic layer, to unify search visibility, online reputation management (ORM), and digital authority. The paper aims to analyze how a GEO-First approach facilitates a continuous discovery-to-branded-search loop by leveraging Large Language Models (LLM) retrieval patterns and Retrieval-Augmented Generation (RAG). Through a systematic review of empirical literature between the years 2024 and 2026, including metrics such as “Share of Model” (SoM) and citation density, this study identifies two operational pillars, Discovery and Sentiment, grounded in a foundational layer of Digital Authority. The analysis synthesizes retrieval and citation patterns across dominant platforms, including OpenAI’s GPT-5.1, Google’s Gemini 3, Anthropic’s Claude 4.5, and specialized systems like Perplexity AI and DeepSeek. The methodology follows a structured literature review approach, based on cross-platform visibility audits, citation correlation studies, and conceptual work on proper context attribution (e.g., the “MaxShapley” algorithm) to propose an integrated conceptual framework. Findings from the literature review suggest that narrative inclusion within the AI’s “retrieval set” acts as a catalyst for subsequent high-intent branded searches, provided that the sentiment integrity of the mention remains positive. The framework concludes that organic growth in the AI era requires a transition from traditional click-oriented metrics to influence-oriented KPIs, to ensure brand resilience in a zero-click, agentic landscape.