Which Domains Do AI Engines Cite? How to Map the Sources That Shape Your Category
AI engines cite the same handful of domains across your category. Learn what a cited-domain map is, the three buckets that matter, and how to build one to guide GEO.
A cited-domain map is a ranked list of the websites that AI engines actually pull from when they answer questions in your category — sorted into three buckets: your own domain, your competitors’ domains, and everyone else (review sites, Reddit, Wikipedia, editorial roundups). It is the single most actionable artifact in Generative Engine Optimization, because it tells you not just whether you’re winning AI answers but where the answers come from — and therefore where to earn coverage.
Think of it as your GEO backlink profile. In classic SEO you study which sites link to your competitors so you know where to build links. In GEO you study which sites the engines cite when they recommend a brand, because those pages are the raw material every answer is built from. This guide explains what the map contains, how to read each bucket, and how to build one — by hand or automatically.
What a cited-domain map actually contains
When Perplexity, ChatGPT, Gemini, or Claude answers a question with live web search, it grounds the answer in a set of retrieved pages and surfaces them as citations — the little source links under or beside the response. Every citation has a domain. Aggregate the citations across many prompts and many runs, count the domains, and you get a frequency ranking: the sites the engines lean on to talk about your category.
That ranking is more useful than any single answer for two reasons. First, AI answers are non-deterministic — one run cites one set of pages, the next run cites a slightly different set, so a single screenshot tells you almost nothing. Second, the domains are remarkably stable even when the exact URLs are not. The same review platforms, the same community threads, and the same reference sites recur across prompts and across engines. That stability is what makes the map worth building: it points at durable targets, not one-off links.
The three buckets
Every cited domain falls into one of three categories, and each one implies a different move.
| Bucket | What it is | What it tells you | What to do |
|---|---|---|---|
| Yours | Citations of your own primary domain | Your content is directly quotable and retrievable | Protect it, expand the pages that get cited |
| Competitors’ | Citations of brands you compete with | Rivals own retrieval real estate you don’t | Study what they publish; compete on those queries |
| Other | Everything else — review sites, Reddit, Wikipedia, news, docs, editorial | The third-party pages that shape answers about everyone in your category | Earn presence there; this is where the leverage is |
The “other” bucket is almost always the largest, and it is where most brands are losing without realizing it. Independent research keeps finding the same pattern: across ChatGPT, Perplexity, Gemini, and Google’s AI answers, a small set of high-authority third-party domains dominates citations. Search Engine Land’s coverage of one large study found that AI search engines cite Reddit, YouTube, and LinkedIn most, with Wikipedia and major review platforms close behind. A three-month study of most-cited domains by Semrush reached a similar conclusion. The takeaway is uncomfortable but freeing: for most categories, the pages that decide AI answers are not on your website at all.
That is why this article is the hub for three deep dives, one per recurring “other” source:
- The Reddit effect — why community threads punch so far above their weight, and how to participate without astroturfing.
- Do G2 and Capterra still matter? — review platforms as high-authority sources AI reads, and the profile hygiene that gets you cited.
- Wikipedia and AI answers — whether your brand needs a page, and the honest notability bar.
How to build a cited-domain map by hand
You can assemble a rough map manually. It is tedious, but doing it once teaches you what the automated version is measuring.
- List your prompts. Write down 10–20 questions a buyer would actually ask an AI engine about your category: head terms (“best session replay tool”), comparisons (“Kitbase vs PostHog”), and problem queries (“how do I track AI crawler traffic”). If you’re not sure what buyers ask, start with finding the prompts buyers ask AI.
- Run each prompt across engines. Ask Perplexity, ChatGPT (with search on), Gemini, and Claude. Because answers vary, run each prompt several times, not once.
- Record every citation. For each answer, copy the source links. Strip each URL down to its registrable domain (e.g.
g2.com,reddit.com,yourcompetitor.com). - Tally and bucket. Count how often each domain appears. Label each one yours, a competitor’s, or other.
- Repeat on a schedule. A one-time map is a snapshot; the value is in the trend, so redo it periodically and watch which domains rise and fall.
The manual method breaks down exactly where it gets interesting: sampling. To separate signal from the noise of non-determinism you need many runs per prompt, across four engines, repeated over weeks. That’s hundreds of API calls and a spreadsheet that never stops growing — which is the whole reason to automate it.
How to build one with Kitbase
Kitbase AI Visibility builds the cited-domain map for you. You define your brand (name, primary domain, and the aliases engines might use), add your competitors, and enter your prompts. On paid Kitbase Cloud plans an analysis runs automatically every 24 hours: it queries Perplexity, Gemini, Claude, and ChatGPT through their official APIs, extracts every citation from every answer, and aggregates them into the map — each domain classified as yours, a competitor’s, or other, and tagged with a source type (UGC, review-site, news, reference, social, docs, editorial, or vendor) so you can see what kind of source shapes your category.
Two details make it more than a scraper:
- You can drill into any domain to see the exact URLs the engines cited and which engines cited them — so “review sites matter” becomes “these three G2 category pages get cited by Perplexity and ChatGPT.”
- Brand edits apply retroactively. Add a competitor you missed and Kitbase re-matches your entire stored history against the new brand list — no re-running analyses, no extra AI spend — so their domain moves from the “other” bucket to the “competitor” bucket across your whole timeline at once.
flowchart TD
A["AI answers across<br/>your prompts"] --> B["Extract every<br/>cited domain"]
B --> C{"Classify"}
C -->|"Your domain"| Y["Yours<br/>→ expand cited pages"]
C -->|"A competitor's"| K["Competitors'<br/>→ study & compete"]
C -->|"Everything else"| O["Other<br/>→ earn presence here"]
O --> R["Review sites"]
O --> D["Reddit / community"]
O --> W["Wikipedia / reference"] What to do with each bucket
A map you don’t act on is trivia. Here’s the play for each bucket.
Yours — defend and extend. The pages of yours that already get cited are proof of what the engines find quotable about you. Find out which ones they are, keep them fresh, and model new pages on them. If your best comparison or documentation content isn’t getting cited, check that AI crawlers can actually reach it — a robots.txt rule or a JS-only render can make a great page invisible. Crawler detection shows you which bots read which pages.
Competitors’ — reverse-engineer. When a rival’s domain shows up in answers you want to win, read the specific pages being cited. Are they comparison pages? Deep how-to guides? Data-backed posts? Publish something better and more directly answer-shaped. This is the core of head-to-head brand prompts.
Other — earn your way in. This is the highest-leverage bucket and the least like SEO. You can’t publish your way onto Reddit or G2; you participate, get reviewed, and get discussed. The three spoke articles above cover each channel. The meta-move: whatever third-party page ranks highest in your “other” bucket is your top marketing priority this quarter, because it’s shaping answers about you whether you show up or not.
Above all, treat the whole exercise as a trend, not a leaderboard. The point isn’t today’s ranking; it’s watching a domain climb after you invest in it — the proof that your GEO work is landing. The tactics that move the “yours” bucket are the subject of how to write content AI engines cite; the measurement discipline behind reading these numbers is covered in AI share of voice.
FAQ
What’s the difference between a citation and a mention? A mention is your brand name appearing in the answer text; a citation is a source link to a domain. The cited-domain map is built from citations only. You can be mentioned without being cited (the engine names you from memory) and cited without being mentioned (it links a page that discusses you). Track both — see AI visibility metrics.
Do all AI engines cite the same domains? No. Retrieval differs by engine, so per-engine maps diverge — Perplexity leans heavily on community content, for example, while others weight reference and review sites differently. That’s why a good map is built per engine as well as combined, and why you should measure across all four rather than spot-checking one.
Why do third-party sites outrank my own domain in citations? Because engines optimize for perceived authority and independent corroboration. A review platform or community thread carries third-party credibility your own marketing pages can’t. It’s the same reason PR and reviews have always mattered — GEO just makes the target list explicit.
How often should I rebuild the map? Continuously if you can, monthly at minimum. Retrieval is live, so the map shifts as the web changes and as you publish. A stale map sends you chasing domains that no longer shape your answers.
Want to see which domains shape AI answers in your category? Start your free trial — 7 days, no credit card required — and get your cited-domain map across four AI engines.