AI Beacon/Entity mapping
Before AI can recommend your company, it has to know which company you are. Category, products, audience, attributes, competitors and the sources behind all of them. AI Beacon shows you what each engine has settled on, and where they contradict each other.
Linked to your site, your Crunchbase record and your LinkedIn page.
Different sector, older web presence, stronger local citations.
No confident match, so the answer describes the category instead of you.
Entity mapping is the record of how each AI platform models your brand as a thing rather than as a string of text: the category it assigns you, the attributes it believes you have, and the other brands and concepts it links you to. Where those models disagree with each other, or with how you actually position yourself, that divergence is what needs correcting.
Last reviewed 24 August 2026 by the SEORCE team.
Each one depends on the last. Most brands invest heavily in the fourth and have never checked the first.
Disambiguation against every other organisation with a similar name. If this resolves to the wrong entity, nothing after it can be right.
The category assigned to you, which determines which questions you are considered a candidate for at all.
The attributes attached: scale, market, capabilities, price position. This is where most divergence from your actual positioning appears.
Only now does the question of whether to recommend you arise. Everything teams usually optimise for sits at this step.
A similar name, an older web presence, a stronger local record. The model resolves to them, and everything downstream is about a different business.
You see which candidate each model picks for your name, and how confidently it picks it.
Category, size, market, who you serve. Each platform holds its own version and none of them show you the working behind it.
Where the models agree, where they differ, and where they simply hold nothing at all, laid out attribute by attribute.
Content landing on an unresolved entity is effort spent on a stranger, and no amount of publishing corrects it.
Entity strength is scored first, so the order of work matches the order the model actually reasons in.
Entity conflicts always trace back to sources. Reading your own markup, your site and the third-party records together is what turns a disagreement into a fix.
Four engines put you in one category, three in another.
Your Organization schema is on one template and missing from the rest.
Your about page and your product pages describe different companies.
Branded search splits across two spellings of your name.
Every engine that gets you wrong is reading the same three sources, and all three contradict your homepage. The disagreement between models is not noise. It is an exact map of which of your own records need correcting first.
Cross-platform comparison is the most useful view here. Six platforms agreeing and one dissenting is a source problem. All seven agreeing on something wrong is a you problem.
sameAs links to Wikidata, your official profiles and any authority record tie your pages to an entity already recognised elsewhere. You can check what you currently declare with the free entity markup checker.
Your site, your profiles and your directory listings should describe you identically. Inconsistency across your own properties is the most common cause of a weak entity.
An attribute asserted in marketing copy is weak. The same attribute demonstrated in a case study, a specification or a customer name is material an answer can use.
Start with the free entity check on your own markup, then see the model each AI platform actually holds.