Who Actually Gets Cited in AI Answers About Your Category

Conversations about AI visibility usually end at your own website: what to fix, what to add, how to structure the headings. That is reasonable, and it skips the fact everything else rests on. A model's answer is usually a compilation of other people's pages, not yours.

You have probably seen the global rankings: Reddit, YouTube, LinkedIn and Wikipedia at the top of the most cited sources. That is exactly how the leaderboard looks in a Semrush analysis covering 30 million sources. The data is real and, at the same time, of little use in your work, because it is an average across the whole internet, while you sell in one category, in one market, to people asking a dozen or so specific questions.

This piece is a procedure. You come out of it with your own list of sources cited in your category, and a decision about what to do with each of them:

• why global source rankings fail as a plan of action
• how to build your own map of cited domains in a single afternoon
• three categories of sources and what can realistically be done with each
• why being on a list is not the same as being cited
• where the money gets wasted

There is no single list of sources that AI likes

Categories differ so much that a shared list makes no sense.

In B2B software, answers lean on comparison directories and user threads. In health, models gravitate towards guidelines and medical editorial sites, because additional constraints apply to sensitive topics. In local services, maps, reviews and business directories come to the fore. In technical industries such as building materials, trade portals and manufacturer sites get cited, because that is the only place with actual specifications.

Then there is the difference between engines. Perplexity shows its sources next to the answer and links generously. Google AI Overviews runs on its own index and a different set of signals. ChatGPT surfaces one thing from search and fetches another live when the question calls for it. The same brand is often highly visible in one system and absent from another - and that is not a measurement error, it is the actual state of things.

The conclusion is inconvenient but simple: the map of sources has to be yours. Someone else's will serve as a reference point at best.

How to build your own map in a single afternoon

You need no tools and no budget for this. You need discipline in taking notes.

1. Write down 15-20 questions a customer asks before buying. Questions, not keywords. "How does X differ from Y", "what does X cost for a company of 20 people", "what should I choose if Z matters most".
2. Ask them in ChatGPT, Gemini, Claude and Perplexity, and run the same phrases through Google to see the AI Overviews. Five systems, one set of questions.
3. Record the cited domains, not the answers. The answer is interesting but fleeting. The list of sources is the part you can count.
4. Repeat the whole set on another day. Model answers vary, so a single pass mostly tells you what the model happened to pick that afternoon.
5. Count which domains come back in both passes. That is your real map of sources, rather than a ranking from an article.
6. Mark the places where your competitors appear and you do not. That is your task list.

The exercise is usually followed by silence. Not because the results are bad, but because they are nothing like what the team assumed. Brands that have invested in a blog for years discover that the model cites three comparison sites and one forum thread.

We do exactly this in the report in the Pro package, only at a larger scale. Instead of a dozen questions asked by hand, we run a fixed set of prompts through ChatGPT, Gemini, Claude, Perplexity and Google AI Overviews. We extract the cited domains from the answers, classify them by source type, and then set your presence against your competitors: where they get cited and you do not. Same procedure, made repeatable and countable.

Three categories of what you will find on the list

Once you have the list of domains, sort it by a single criterion: how much say you have in each place.

• Your own - website, blog, documentation, video channel. Full control, and usually a smaller share of citations than the team assumes.
• Not yours, but controllable - directory profiles, business listings, review aggregators, product databases, encyclopedic entries. They do not belong to you, but you can complete the data, correct outdated facts and keep the description consistent.
• Not yours and not controllable - trade media, forums, user threads, editorial rankings. You do not set the content here. At most you can be useful and countable enough that someone wants to name you.

The biggest neglect usually sits in the middle category. These are the places where filling in the data is all it takes for a model to have something to say about your company - and they sit for years with an empty description from three years ago and a price list nobody updated.

Being on the list is not the same as being cited

This is the mistake that costs the most money.

The model cites a page, not you. If you make it into a "best X in 2026" ranking but your entry is three sentences of generalities with no price, no specification and no indication of who it is for, the model has nothing to describe you with. It will cite that page, name the two brands described concretely, and skip you, even though you are formally in the text.

The same thing works in reverse. One well-written entry in a comparison site - with a price, a scope, its limits and a note on who the product is not for - gives the model material for a full sentence in an answer. A sentence about you.

The practical conclusion: instead of fighting for one more appearance, improve the quality of the ones you already have. Price, scope, who it is for, what you do not do, how you differ from the nearest alternative. Those are comparable facts, and models answer in comparisons.

Reviews and directories: dull, measurable, underrated

Reviews deserve their own paragraph, because that is the most thankless and most frequently postponed part of the work.

Review aggregators and trade directories get cited because they hold structured data and a freshness nobody else offers. A company with no profile in such a place is not "neutral" - it simply does not exist in the set the model builds its sentence about the category from.

Three things that matter and require nobody's approval:

• completeness of the profile - description, categories, contact details, a current price or at least a range
• freshness - a handful of reviews from recent months weighs more than a hundred from four years ago
• consistency of the description with what you say about yourself on your own site and in social media

The last point matters more than it looks. If you describe yourself in three different ways in three places, the model gets confirmation of none of them.

What you cannot buy

Since some sources are beyond your control, the temptation is to buy them. It helps to know where that does not work.

• Paid placements on sites nobody cites. If a domain never showed up in your map, an article there will not change your presence in answers. You will have paid for a publication, not for visibility.
• Bulk mentions without context. A brand name dropped into a text about nothing builds no association with the category. Models learn from co-occurrence, so a mention without context carries no information.
• Promises of "adding you to AI". There is no panel where you submit a brand to ChatGPT or Gemini. There is only what the models find and process.

It is also fair to say where our knowledge ends. An Ahrefs study of 75,000 brands found that branded mentions correlate with presence in AI answers considerably more strongly than classic backlinks, and in the December 2025 update the strongest single signal turned out to be mentions on YouTube. Those are correlations, not proof of causation - and anyone selling them to you as a guarantee is claiming more than is known.

What this means for the quarter ahead

• Pick at most two surfaces with the biggest gap against your competitors. Spreading across eight will produce nothing anywhere.
• Improve the quality of existing appearances first, then go after new ones.
• Complete the controllable profiles - the cheapest work on the whole list.
• Repeat the measurement later with the same set of questions. Without a baseline you cannot tell an effect from the models' own variability.

Per-engine measurement and the gap against competitors are the backbone of the report. We check how often your brand appears in the answers of each of the five systems separately, which domains get cited alongside it, and which brands are named instead of you. The Pro package adds a gap analysis of mentions and links plus the GEO audit of your site - the other half of the same equation.

A map of sources is not impressive. You cannot put it on a slide as a line going up and to the right. It is, however, the one thing that turns the conversation about AI visibility from guesswork into a task list - which is why it is worth starting there, rather than with another blog post.

Sources

• Search Engine Land (Semrush, 30M sources) - AI search cites Reddit, YouTube, LinkedIn most
• Ahrefs - Branded web mentions vs. backlinks for AI visibility (75,000 brands)
• Ahrefs - YouTube mentions: the strongest AI-visibility signal (December 2025)