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Entity and brand consistency checklist

A checklist for giving a company and its people one name and one set of facts across their site, markup, profiles, and the sources AI assistants read.

By , founder of AEO HQ

Published · Updated

An entity and brand consistency checklist tests whether a company and the people behind it appear under one name, with one set of facts, everywhere that search engines and AI assistants read about them: the company's own site and markup, the profiles it controls, and the sources others maintain. This one has 14 checks in six groups. The problem it guards against is documented: language models often "incorrectly merge information belonging to different entities" (opens in a new tab) that share a name. No study we found tests whether consistency work changes AI recommendations, so each check states how strong its evidence is.

An entity is a distinct thing, such as a company, a person, or a product, that search systems try to tell apart from others. This checklist belongs to AEO HQ's guide to AI search optimization. It expands the entity group of the AEO checklist, whose two checks cover one name and description everywhere (check 5.1) and Organization markup on the home page (check 5.2), and it does not repeat them. The sources were read on September 27, 2026.

Scope

  • Covers: the names, descriptions, and core facts of a company, its products, and its founders and authors, on the company's site, in its structured data, on the profiles it controls, and in sources others maintain, such as Google's knowledge panels and Wikipedia.
  • Does not cover: earning mentions and reviews, which are covered in brand mentions and AI recommendations and in the AEO checklist's third-party checks; crawler access and indexing, in the technical SEO checklist for AI search; and page content, in the GEO checklist.
  • For: founders and marketers at B2B companies, especially those whose company or founder shares a name with someone else.
  • Pass criteria are AEO HQ's recommendations. The facts behind them link to their sources.
  • Evidence labels:
    • Strong: official platform documentation, or several independent studies that agree.
    • Moderate: laboratory studies, a single study, or correlations.
    • Weak: one small test, or a source with a conflict of interest.
  • What the evidence covers: the evidence that assistants confuse or misdescribe entities is moderate. For the fixes, it is mostly platform documentation: in AEO HQ's research review, we found no study that tests whether entity markup, profile links, or consistent descriptions change which companies AI assistants recommend.

Prerequisites

  • An entity record: one document that lists the company's official name; other names it goes by, such as abbreviations and former names; its legal name; a one-line description; the canonical domain; the founding date; founders and key people with their titles; the address or service area; contact details; company identifiers such as registration, VAT, DUNS, or LEI numbers, where they exist; the official profile URLs; and the logo file. It extends the fact sheet in the AEO checklist's prerequisites.
  • Admin access to the site's code or content system, and to each official profile.
  • A list of branded questions to ask AI assistants (check 6.2).

Quick checks to run first

If time is short, AEO HQ recommends starting with these five checks:

  1. Check 1.1: other entities that share the name are known.
  2. Check 1.2: Google shows the official name as the site name.
  3. Check 2.2: Organization markup carries identifying details.
  4. Check 3.1: the official profiles and the site link to each other.
  5. Check 6.2: assistants are tested for merges and wrong facts.

1. The name

2. The company's own site

3. Profiles the company controls

The AEO checklist's check 5.1 compares names and descriptions across profiles. The checks below cover the rules that platforms document.

4. Sources others maintain

What reviewers, directories, and the press say about a company is covered by the AEO checklist's third-party checks. The checks below cover how the company handles those sources.

5. People

6. Upkeep and testing

Version and change log

  • Version 1.0, September 28, 2026: First published. Sources checked on September 27, 2026.

Next steps

AEO HQ's Instant AEO Audit ($499) covers a small part of this checklist: it records which schema.org types the home page's JSON-LD declares, such as Organization, but it does not validate the markup or compare facts across profiles. Its limits are listed in our methodology.

Sources

  1. Lee, Y., Ye, X., & Choi, E. (2024). AmbigDocs: Reasoning across documents on different entities under the same name. In Proceedings of the First Conference on Language Modeling (COLM 2024). https://doi.org/10.48550/arXiv.2404.12447 (opens in a new tab)
  2. Sun, K., Xu, Y., Zha, H., Liu, Y., & Dong, X. L. (2024). Head-to-Tail: How knowledgeable are large language models (LLMs)? A.K.A. will LLMs replace knowledge graphs? In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) (pp. 311–325). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.naacl-long.18 (opens in a new tab)
  3. Google. (2025, December 10). Provide a site name to Google Search. Google Search Central. https://developers.google.com/search/docs/appearance/site-names (opens in a new tab)
  4. Google. (2026, April 14). Redirects and Google Search. Google Search Central. https://developers.google.com/search/docs/crawling-indexing/301-redirects (opens in a new tab)
  5. Microsoft Bing. (n.d.). Bing Webmaster Guidelines. Retrieved September 27, 2026, from https://www.bing.com/webmasters/help/webmaster-guidelines-30fba23a (opens in a new tab)
  6. Google. (2026, September 8). Organization schema markup. Google Search Central. https://developers.google.com/search/docs/appearance/structured-data/organization (opens in a new tab)
  7. Linehan, L. (2026, May 11). We tracked 1,885 pages adding schema. AI citations barely moved. Ahrefs. https://ahrefs.com/blog/schema-ai-citations/ (opens in a new tab)
  8. Google. (n.d.). Guidelines for representing your business on Google [Google Business Profile Help]. Retrieved September 27, 2026, from https://support.google.com/business/answer/3038177 (opens in a new tab)
  9. Google. (2026, July 10). Optimizing your website for generative AI features on Google Search. Google Search Central. https://developers.google.com/search/docs/fundamentals/ai-optimization-guide (opens in a new tab)
  10. Google. (2025, December 10). AI features and your website. Google Search Central. https://developers.google.com/search/docs/appearance/ai-features (opens in a new tab)
  11. Google. (n.d.). Tips to improve your local ranking on Google [Google Business Profile Help]. Retrieved September 27, 2026, from https://support.google.com/business/answer/7091 (opens in a new tab)
  12. Google. (n.d.). Get verified on Google [Knowledge Panel Help]. Retrieved September 27, 2026, from https://support.google.com/knowledgepanel/answer/7534902 (opens in a new tab)
  13. Google. (n.d.). Submit feedback on content about you [Knowledge Panel Help]. Retrieved September 27, 2026, from https://support.google.com/knowledgepanel/answer/7534842 (opens in a new tab)
  14. Wikipedia:Conflict of interest. (n.d.). In Wikipedia. Retrieved September 27, 2026, from https://en.wikipedia.org/wiki/Wikipedia:Conflict_of_interest (opens in a new tab)
  15. Allen-Zhu, Z., & Li, Y. (2024). Physics of language models: Part 3.1, knowledge storage and extraction. In Proceedings of the 41st International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 235, pp. 1067–1077). https://proceedings.mlr.press/v235/allen-zhu24a.html (opens in a new tab)
  16. Google. (2026, September 8). Learn about article schema markup. Google Search Central. https://developers.google.com/search/docs/appearance/structured-data/article (opens in a new tab)
  17. Min, S., Krishna, K., Lyu, X., Lewis, M., Yih, W.-t., Koh, P. W., Iyyer, M., Zettlemoyer, L., & Hajishirzi, H. (2023). FActScore: Fine-grained atomic evaluation of factual precision in long form text generation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics. https://arxiv.org/abs/2305.14251 (opens in a new tab)
  18. Schulte, J., Bleeker, M., & Kaufmann, P. (2026). Don't measure once: Measuring visibility in AI search (GEO) (arXiv:2604.07585) [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2604.07585 (opens in a new tab)

How to cite this page

Maxwell, P. (2026). Entity and brand consistency checklist. AEO HQ. Last updated September 28, 2026. https://www.aeohq.ai/articles/entity-checklist

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