Entity SEO for AI: How to Make LLMs Actually Know Who You Are

Summary

Most brands that are invisible in AI answers do not have a content problem. They have an identity problem: the sources a model learns from disagree about what the company actually is. Entity SEO is the work of making that definition consistent and machine-readable everywhere it appears. This guide explains what an entity means in machine terms, the five places models learn who you are, how to audit your own footprint in an afternoon, and how to tell whether the fixes worked.

The Short Answer

Entity SEO for AI means making sure every place a model can learn about your company describes you the same way: same name, same category, same one-line definition, same founders, same location. Language models do not retrieve your homepage and read it top to bottom. They assemble an answer from a compressed internal representation of the world plus a handful of retrieved sources. When those sources disagree about what you do, the model either hedges, generalises, or leaves you out entirely.

The practical version fits on a sticky note. Pick one sentence that defines your company. Then make that sentence true and visible everywhere a machine can read it: homepage, About page, structured data, LinkedIn, Crunchbase, review directories, podcast show notes, conference speaker bios. Consistency is the whole game, and it is cheaper than most teams expect.

What an Entity Actually Is, in Machine Terms

An entity is a node: a distinct thing with a stable identifier, a set of attributes, and relationships to other nodes. Google formalised this years ago with the Knowledge Graph. Large language models do something looser but functionally similar. Somewhere in the weights there is a cluster of associations attached to your brand name, and that cluster has a shape.

The attributes that matter commercially are boring ones: what category you belong to, when you were founded, where you are based, who runs you, what you cost, who you compete with, who uses you. The relationships matter just as much. Phrases like competes with, was founded by, and is used by are how a model navigates from a question to a candidate list of answers.

Here is the consequence that costs teams the most money. When someone asks for the best tools in a category, the model first builds a candidate set of entities whose category attribute matches, then filters that set on reputation signals. If your category attribute is fuzzy, if you are filed as marketing software rather than AI visibility tracking, you never enter the candidate set. You lose before any ranking happens, and no amount of content quality rescues you from that.

The Five Places Models Learn Who You Are

Your entity is assembled from a surprisingly small number of source types. Ranked roughly by leverage per hour of effort:

• Your own site, especially the first screen of the homepage, the About page, and your Organization and Product structured data. This is the only source you fully control and, ironically, the one most often written in vague marketing language a machine cannot parse.
• Structured public databases: Wikidata, Crunchbase, LinkedIn company pages, app marketplaces and developer directories. Unglamorous and high-leverage, because they are clean, parseable, heavily mirrored, and well represented in training data.
• Review and comparison platforms such as G2, Capterra and Product Hunt. These supply your category label at least as much as they supply your rating, and the category field is the part that decides whether you are considered at all.
• Editorial and community text: listicles, newsletters, Reddit and Hacker News threads, podcast show notes, Slack and Discord archives that get indexed. This is where a model learns the informal definition of you, the one that sounds like the cheaper alternative to X or the one that does Y well.
• Your founders and employees. Speaker bios, guest posts, and interview intros carry a one-line company description that gets copied verbatim across dozens of pages.

The fifth one is the sleeper. A founder bio written once and reused across forty event pages, podcast feeds and syndicated posts will quietly outvote a homepage you rewrote last week. If your positioning has changed in the last eighteen months and you have not rewritten your bios, there is a good chance the model is still describing the company you used to be.

How to Audit Your Entity Footprint in an Afternoon

You do not need a tool to start. You need a document and about three hours.

• Write your intended definition sentence first, in plain language, in the form: [Brand] is a [category] that [does what] for [who]. If your team cannot agree on this sentence, stop here. No amount of distribution fixes an internal disagreement.
• Ask five assistants, at minimum ChatGPT, Gemini, Perplexity, Claude and Copilot, two questions each: what is [brand], and what category is [brand] in. Record the answers verbatim, not your impression of them.
• Diff each answer against your sentence. Mark three things separately: wrong category, outdated description, and missing entirely. They have different fixes.
• Note the sources each assistant cited. Those pages are your priority list, because they are demonstrably being retrieved.
• Search your brand name and its common variants, collect the top twenty pages that describe you, and write down the category label each one uses. Count the labels. The most frequent label is what the model believes, regardless of what your homepage says.
• Check every structured profile you can edit for the correct category, current one-liner, and working links back to your site.

Fixing a Weak or Confused Entity

Once you know where the definitions diverge, the fixes are mostly mechanical. They are also unglamorous enough that they tend to stay on the backlog, which is precisely why they are still available as an advantage.

• Lead with a literal definition on your homepage. A model can parse Refine is an AI visibility tracking platform that shows how often ChatGPT, Gemini and Perplexity recommend your brand. It cannot do anything useful with Own the answer.
• Ship Organization schema with sameAs links to every profile you control. This is the single most direct way to tell a machine that these scattered accounts are one entity rather than five.
• Designate one canonical About page as the source of truth, and point your press kit, bios and directory profiles at it.
• Fix the category dropdown everywhere it exists: G2, Capterra, Crunchbase, LinkedIn, app marketplaces. Dropdown fields take minutes and carry disproportionate weight because they are unambiguous.
• Rewrite founder and employee bios once, then actually redistribute the new version to the podcasts, events and publications that hold the old one.
• If you meet notability requirements, get a correct Wikidata item. It is a primary substrate for machine knowledge and it propagates widely.

Sequence matters less than coverage. What you are trying to do is raise the frequency of the correct description across the whole corpus until it becomes the majority reading. A single authoritative page rarely does that on its own.

When the Model Confuses You With Someone Else

Brands with generic or shared names hit a harder version of this problem, and it comes in two flavours. Conflation is when the model merges you with a similarly named company and returns a hybrid that is half true. Displacement is when a larger entity simply absorbs the query, and asking about your brand returns information about them.

The fix for both is disambiguating co-occurrence. Models learn to separate entities by seeing them appear alongside distinguishing context, not by being told they are different. So publish and earn content where your brand name sits next to unambiguous qualifiers: your exact category, your founders' names, your city, your product names, your integrations. Over enough documents, the cluster splits.

What does not work is denial. Publishing a page explaining that you are not the other company mostly succeeds at placing both names in the same sentence, which is the opposite of what you want. Add distinguishing context; do not argue with the model.

How to Know It Worked

Entity work is slower than most GEO tactics, and it is worth setting expectations before you start. Retrieval-driven surfaces such as Perplexity, ChatGPT search and AI Overviews can reflect changes within weeks, because they read the live web. The model's internal parametric knowledge, the thing that answers when nothing is retrieved, updates only on a training cycle, which means months. Both matter, and they move at different speeds.

Track four things rather than one. Category-match rate: how often models file you in the right category. Definition accuracy: how close the model's one-liner is to yours. Source mix: which pages the models actually cite when they describe you. And unprompted mention rate: how often you appear in category-level questions where nobody named you. The first two tell you whether the entity is clear. The last two tell you whether that clarity is converting into recommendations.

The pattern to look for is sequential. Category accuracy improves first, then description accuracy, then unprompted mentions in category prompts. If category accuracy has moved but mentions have not, the entity is fixed and the problem has shifted to reputation. That is a different project, and a much more pleasant one to have.