NobleJackal GEO Framework
Year: 2026
Type: book
Abstract: How clearly do AI search systems understand, verify and cite an organisation? GEO (Generative Engine Optimization) addresses that problem. This six-language framework gives teams a seven-layer method for examining entity clarity, evidence quality, machine-readable structure, question coverage, multilingual parity and measurement. NobleJackal GEO Framework is a proposed normative framework for evaluating claims about representation in generative systems through evidence, traceability, measurement, accountability and sustainable value. It is not a “how to do GEO” manual, an official industry standard, a certification programme or a claim of independent validation. Official web edition:https://noblejackal.com/geo-framework/?utm_source=zenodo&utm_medium=doi&utm_campaign=geo_framework The official web edition provides chapter-level reading, machine-readable JSON and Markdown, version and integrity records, and the publication’s explicit normative boundaries. This Zenodo record preserves the publication-locked English edition v1.0.0. Julian Gauss is the publishing name of Kaan Muraz. Suggested citation:Gauss, Julian (Kaan Muraz). NobleJackal GEO Framework: A Proposed Standard for Representation, Evidence and Sustainable Value. Version 1.0.0. NobleJackal, 2026. https://doi.org/10.5281/zenodo.21991762
Author: Muraz, Kaan
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7203684753