{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T16:23:04Z","timestamp":1776702184503,"version":"3.51.2"},"posted":{"date-parts":[[2026]]},"group-title":"SSRN","reference-count":0,"publisher":"Elsevier BV","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>The emergence of Large Language Models (LLMs) as primary information intermediaries has created an urgent need for frameworks that address how digital entities achieve visibility within AI-generated responses. This paper introduces the GEO Enterprise Framework, a 10-layer practitioner model for Generative Engine Optimization derived from action research conducted during a 7-day implementation sprint (March 17-23, 2026). The sprint produced 206 commits across 8 repositories, generated 61 pages and 92 indexable URLs, implemented 29 Schema.org types within a single JSON-LD @graph architecture, and achieved an Entity Consistency Score improvement from 20% to 80%-all at zero infrastructure cost using free-tier platforms. The framework formalizes several novel constructs: the Invisible Excellence Paradox, whereby technically optimized sites generate zero organic traffic without editorial authority; Algorithmic Citability, the structural property enabling LLM citation; and the Entity Consistency Score (ECS), a quantitative metric for cross-platform identity alignment. Empirical results reveal a critical gap between technical optimization and actual LLM citation, suggesting that structured data and semantic markup are necessary but insufficient conditions for algorithmic visibility. The paper contributes to the nascent GEO literature by providing both a theoretical framework and reproducible implementation data, bridging the gap between academic research and practitioner application in the post-search, agent-mediated information economy.<\/jats:p>","DOI":"10.2139\/ssrn.6460680","type":"posted-content","created":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T15:26:37Z","timestamp":1776698797000},"source":"Crossref","is-referenced-by-count":0,"title":["Algorithmic Authority: A Practitioner Framework for Generative Engine Optimization Based on a 7-Day Implementation Sprint"],"prefix":"10.2139","author":[{"given":"Alexandre","family":"Caramaschi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","container-title":[],"original-title":[],"deposited":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T15:26:37Z","timestamp":1776698797000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ssrn.com\/abstract=6460680"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":0,"URL":"https:\/\/doi.org\/10.2139\/ssrn.6460680","relation":{},"subject":[],"published":{"date-parts":[[2026]]},"subtype":"preprint"}}