NobleJackal GEO Framework

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Authors: Kaan Muraz
Cited by: 8

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

Fields of Study & Subjects

Sustainable Development and Environmental Policy Diverse Interdisciplinary Research Studies Sustainability and Ecological Systems Analysis

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
14 August 2026
Language
EN
Document type
Book