How to Get Your Company Cited by Perplexity and Gemini

Perplexity's own documentation describes the product as searching the internet in real time and attaching numbered citations to every answer. Google's guidance for its generative features describes the same idea, calling it grounding: pulling relevant pages from the Search index and generating a response with clickable links to the pages that support it.

Both engines also reward the same shape of page. An engine building an answer under a time budget needs a passage it can lift cleanly: question-shaped headings, a direct answer in the first two sentences under each, one idea per section, and a stated date all raise the odds that a passage is extractable. The largest controlled study of this, Aggarwal and colleagues' GEO benchmark across 10,000 queries, found that adding authoritative quotations raised citation visibility by up to 40% and adding statistics by around 30%, while keyword stuffing offered little to no gain (about 10% worse than baseline on one Perplexity.ai metric in the same paper). For a fuller walkthrough of that page shape, see how to structure a page so AI engines cite it.

Both engines also lean on third parties for judgement questions. When the question is "what is X," a brand's own page is a plausible source. When the question is "which X is best," both engines reach for pages that already rank the options — on Gemini with Google Search grounding, one July 2026 study of 100 "best [vertical] in [city]" queries found directory and ranking sites cited in 78% of answers, and on Perplexity, citation data puts review aggregators such as G2, Gartner, NerdWallet, PCMag, TripAdvisor and Yelp among the most-cited domains for commercial intent.

Finally, both are volatile: independent research shows 40% to 60% of cited domains change month to month across the major platforms. A citation is a state, not a possession, which is why the checklist below ends with a refresh line.

PerplexityBot is Perplexity's own crawler, which its documentation says is built to surface and link websites in Perplexity search results and is not used to train foundation models. Gemini in Google Search (AI Overviews and AI Mode) instead uses the ordinary Google Search index fetched by Googlebot, and the Gemini app, when grounding is on, uses Google Search as its retrieval tool and returns sources as structured grounding metadata. Practically, a page that ranks well in Google has a head start on Gemini surfaces, and no start at all on Perplexity unless PerplexityBot can reach it.

The crawler rules are the point most teams get wrong, and getting it wrong can silently remove a brand from one engine while it optimises for the other. Google's Search Central documentation is explicit that its AI features use Googlebot, and that the separate Google-Extended token controls only whether content is used for Gemini model training and grounding — it has no effect on Google Search, including AI Overviews and AI Mode. Perplexity documents two agents: PerplexityBot for indexing, and Perplexity-User, which fetches a page when a user asks a question about it and, because a person requested the fetch, generally ignores robots.txt. Perplexity also publishes IP ranges and a WAF whitelisting guide, since a security rule that blocks unknown bots is enough to remove a site from its results. A broader rundown of which crawlers to allow or block lives in this guide to AI crawlers and search visibility.

The number of sources per answer also differs sharply. One 2026 AI visibility index built on 126 million US prompts reports that Gemini cites an average of three sources per response, drawing on a small pool that includes Wikipedia, Reddit and YouTube, while Perplexity typically shows four to eight. Three slots is a much tighter door than eight, so on Gemini the question of whether a brand is the single best source for a sub-question matters more.

Which third parties matter also differs. Perplexity's citation profile is unusually concentrated on Reddit, with one 2026 index putting Reddit at 20% to 24% of Perplexity citations and 99% of those pointing to specific threads rather than subreddit pages. Google's AI surfaces show a more self-referential tilt, with roughly 43% of AI Overview citations going to Google-owned properties including YouTube by one analysis. So "be present where the engine already looks" resolves differently by engine: on Perplexity, that means community threads and review aggregators; on Gemini, that means YouTube, Google Business Profile and the pages Google already ranks. For engine-specific detail, see how Perplexity picks the sources it cites and how Google AI Overviews chooses what to cite.

Neither vendor publishes a source-selection specification. Google says plainly that there are no additional requirements to appear in AI Overviews or AI Mode, that llms.txt files and special markup are ignored, and that rewriting content for AI is unnecessary — a point covered in more depth in what llms.txt actually does. Perplexity publishes crawler and WAF guidance but no ranking guidance. Everything beyond access is inferred from measurement.

Two retrieval engines, two doors

The other half is less encouraging. A June 2026 analysis of 15 SaaS brands found 84% to 93% of AI citation weight sitting on third-party sites for their categories, and a separate study of 100 B2B "best software" queries found that when Google cited a brand's own self-ranked listicle, it recommended a competitor 69% of the time. Publishing a "best [category]" page with the publisher at the top earns a citation but not a recommendation.

The sorting rule that follows: own-site work for factual and procedural questions on both engines; third-party presence for evaluative questions on both engines; and an acceptance that the third parties worth pursuing differ by engine. Comparison and listicle formats that name competitors honestly took a large share of commercial-intent citations in one analysis of a million citations — a comparison that concedes where a rival wins is citable, while one that never does reads as a brief for the competitor the engine likes better.

Retrieval is also language-scoped on both engines: a brand whose facts for a given market exist only in English is competing for that market's answers with whatever local-language pages the engine can find, including inaccurate ones. Producing native-language pages that a native reviewer has checked is the difference between being a candidate and not — a point developed further in building a multilingual content pipeline AI engines cite.

Lifewood Data Technology runs managed AEO and GEO programmes, and the access audit described below is the first thing done on every one of them, so this section discloses that interest directly. Two observations from that work hold regardless of who performs it.

The first is that most "we are invisible on Perplexity" cases turn out to be access cases, not content cases. A WAF rule inherited from a previous security review, a CDN bot-fight setting, or a robots.txt file written when "AI crawler" meant "training crawler" is enough to remove a site from one engine while it performs perfectly on the other. Because the two engines run different crawlers with different robots behaviour, the same site can be fully visible on Gemini and absent from Perplexity without anyone having decided that on purpose. Server logs settle the question in an afternoon; content changes take weeks and would not have helped.

The second is about multilingual programmes specifically. Google's guidance on managing multilingual sites is unchanged by AI features, and Perplexity searches in the language of the question, so a brand's Thai-market facts, for example, need to exist in Thai to compete for Thai-language answers. For teams weighing whether to run this in-house, what to look for in AEO and GEO services sets out the criteria.

1. Confirm access, per engine, from logs. Grep for Googlebot, PerplexityBot and Perplexity-User in server logs for the last 30 days. If any is absent from a page that matters, that engine cannot cite it. Check robots.txt, CDN bot settings and WAF rules separately.
2. Do not confuse Google-Extended with Googlebot. Blocking Google-Extended removes a site from Gemini app grounding and training only — it does nothing to AI Overviews or AI Mode. Decide each deliberately.
3. Serve the main content in HTML. Google says it can process JavaScript but calls it more complex; Perplexity's fetcher works on a shorter time budget than a full render. Content behind a click, a tab or a script should be treated as absent.
4. Put the answer in the first two sentences under a question-shaped heading. Both engines extract passages, so the extractable span needs to be obvious and self-contained.
5. Add evidence, not adjectives. Quotations from named sources, dated statistics and a visible methodology are the changes the 10,000-query GEO benchmark measured as effective; keyword density was measured as harmful.
6. Date everything and refresh on a schedule. Both engines prefer recent pages for commercial and evaluation questions. A refresh cadence needs to change the content, not just the date stamp.
7. Earn presence on the third parties each engine trusts. For Perplexity, that means genuine participation in the Reddit threads where buyers already discuss the category and complete profiles on the review aggregators it cites. For Gemini, that means a complete Google Business Profile, YouTube content with transcripts, and inclusion in independent "best of" lists.
8. Reconcile facts across the web. One index found that on Gemini, the overlap between brands mentioned and domains cited can be as low as 30% — the engine can name a brand from third-party evidence without citing it at all, so the name, category, pricing and claims on third-party pages need to agree with the brand's own.
9. Measure engine by engine, not as one number. A visibility score averaged across engines hides an access failure on one of them. Tracking a fixed prompt set on each engine separately, and reading the sources rather than just the mention, catches that. Lifewood's own approach to this is set out in how to build an AEO and GEO content strategy for ChatGPT and Gemini.
10. Publish comparisons that name competitors honestly. A comparison that concedes where a rival wins is citable on both engines; one that does not tends to help the competitor instead.