You checked ChatGPT and saw a competitor. You checked Perplexity and saw yourself. You checked Google AI Overviews and saw neither of you.
That's confusing, and it's also completely normal. Each of those surfaces runs its own retrieval pipeline, weighs its own signals, and renders sources its own way. There is no single AI ranking factor to chase.
So platform specific AEO is not a nice-to-have refinement. It's the actual shape of the work, and the reason your results look inconsistent from one surface to the next.
Here's what you'll get from this guide. A short, honest map of how each of the five major answer engines finds sources, what each one favors, and how it shows citations to the reader. Then a priority order, so you know where your first ninety days should go instead of spreading yourself across five surfaces at once.
We'll stay at the map level here. The deep step-by-step for any single engine lives in its own playbook, and you'll find the link when you need it.
If you've been trying to get cited in AI answers with one generic checklist, this is the piece that explains why the results felt so uneven.
Take a breath. You're closer to a strategy than you think.
Why one AEO plan stops working across five engines
Answer engines are passage-first and entity-first, not page-first. That single sentence explains most of what follows.
None of these systems rank your article the way Google's ten blue links used to. They retrieve candidate documents, break them into chunks, pick specific passages that answer a specific sub-question, then attribute those passages back to a URL. Your page is raw material. The passage is the product.
The five engines diverge on almost everything downstream of that. They differ on which index they retrieve from, how heavily they weigh freshness, which source types they trust, how many sources they cite, and whether citations appear inline or in a panel at the bottom.
There is real overlap. Wikipedia, Reddit, and major news brands show up across all of them. Past that shared core, the preferences split fast.
Two facts make the split concrete.
Roughly 99 percent of URLs appearing in Google AI Overviews also rank somewhere in Google's top 100 organic results. That's a reverse-engineered figure rather than an official one, but it's a strong working signal: AI Overviews mostly re-sorts what Google already trusts.
Perplexity works nothing like that. Around 93 percent of the pages it cites have fewer than ten referring domains. Topical depth beats link-graph strength there, which means a small site with genuine subject depth can win a Perplexity citation it would never win in Google.
Same content, two very different outcomes. That's the whole argument for AEO by engine in one comparison.
There's a second reason the split matters, and it's less obvious. Citation rendering differs too. Perplexity puts a clickable number right next to the claim. Google AI Overviews shows cards under a summary block. Gemini stacks a sources panel at the end. Copilot uses footnotes. ChatGPT drops markers that resolve to a list at the bottom.
Those are not cosmetic differences. They decide whether a citation sends you traffic or just quietly shapes what the model believes about your brand. Both are worth having. They are not worth the same thing.
Your first action: stop asking "are we visible in AI search?" and start asking "on which engine, for which prompt?" The second question has an answer you can act on.
ChatGPT: triangulation beats being the single best page
ChatGPT rarely crowns one winner. It picks citation neighbors inside a topic cluster and lists them side by side, competitors included.
That changes the goal. You're not trying to outrank one page. You're trying to be one of the handful of brands that keeps appearing across the sources ChatGPT already leans on.
A few mechanics worth holding onto.
Only about 18 percent of ChatGPT conversations actually trigger a web search. The rest get answered from the model's own memory. So your visibility has to work on two layers at once: what the model already absorbed about your brand, and what its search subprocess can fetch live.
When citations do appear, expect 3 to 6 of them, rendered as numbered markers that link down to a Sources block at the bottom of the answer. Two thirds of cited turns carry between one and four unique sources.
Turn one matters more than any other turn. The opening answer in a conversation is about 2.5 times more likely to carry citations than the tenth answer, and roughly four times more likely than the twentieth. Sources cited early tend to anchor everything after them.
Citation share is also wildly uneven. The top ten domains capture only about 12 percent of all citations, so there's no small club to break into. Wikipedia alone accounts for around 5 percent of citations and appears in 18 percent of citation-bearing conversations. Reddit sits near 3 percent. Together with wire services and .gov sources, those families explain roughly a quarter of everything ChatGPT cites.
What actually moves the needle here is triangulation: your brand showing up across review sites, directories, comparison lists, trade press, and community threads. Practitioner analysis puts the lift from brand mentions and third-party validation at around 44 percentage points. Treat that as directional, not a promise. The direction is what matters.
Formatting still helps. Roughly 90 percent of top-cited pages state the answer inside the first 100 words. Lead with the answer, then explain it.
Query specificity changes what ChatGPT reaches for, too. Broad navigational questions pull homepages and aggregator listings. Specific questions pull deep URLs, service pages, and product-level content. Most citations come from the hyper-specific long tail, which is good news if you don't have a famous domain.
Your first action for ChatGPT: pick your five most important buyer prompts and list every third-party page that currently answers them. Your job is to get named on those pages, not just on your own.
Perplexity: the citation-first engine with a freshness tilt
Perplexity is the friendliest of the five if your content is well structured. It treats attribution as a first-class output rather than an afterthought.
It runs hybrid retrieval, keyword matching plus dense embeddings, then passes candidates through multiple reranking layers before an LLM writes the answer. Around 5 to 10 pages get retrieved per query, and only 3 to 4 survive into the cited list. A quality gate drops weak candidates entirely, and if too few pass, the system simply goes back and retrieves again.
Three signals get overweighted, and all three are things you control.
Recency comes first. About 70 percent of top-cited pages were updated within the last 12 to 18 months, and there's a measurable boost for anything published or refreshed in the last 30 days.
Factual density comes second. Numbers, dates, named entities, and explicit comparisons make a passage easy to quote. Vague prose gives the model nothing to lift.
Structure comes third. Clean headings, short paragraphs, one claim per section, and JSON-LD markup. Pages carrying structured data show a 47 percent top-three citation rate against 28 percent for pages without it.
Perplexity also applies vertical-specific authority. GitHub and Stack Overflow for technical questions. G2, Capterra, Trustpilot, and Clutch for product evaluation. LinkedIn and Reddit for people and community topics. If your category maps to one of those, that's where your third-party effort belongs.
One caveat you should know. A Columbia Journalism Review audit found roughly 37 percent of Perplexity's cited answers misattributed the claim to the wrong source. Aggressive citing cuts both ways. It isn't a reason to skip Perplexity. It's a reason to make your pages so cleanly extractable that the right claim lands on the right URL.
Why this engine deserves early effort: citations render as clickable inline numbers, so a top-three placement drives real referral clicks. And because recency counts, a refresh of an article you already published can move it from uncited to cited inside a single crawl cycle.
Your first action for Perplexity: take your five strongest existing pages, add a visible last-updated date, refresh the numbers, and tighten each section so it opens with one quotable claim.
Google AI Overviews: passage picking on top of your organic index
AI Overviews is the summary block Google injects above the classic results. Its citation logic is the most predictable of the five, because it rides on machinery you already understand.
The pipeline starts with a candidate pool of roughly 200 to 500 documents drawn from Google's open web index. A filtering pass strips low-authority and low-relevance pages. A fan-out pass issues sub-queries to cover the facets of the question. Then a Gemini reranking pass selects the specific passages that make it into the summary. Final output: 5 to 15 cited sources per block.
The ranking signals are Google's usual set. E-E-A-T, topical authority, structured data, internal linking, named authors, freshness. Nothing exotic.
What's different is passage extractability. The reranking pass picks a passage, not a page. Two articles can have identical topical strength, and the one with clearly demarcated, self-contained answers wins while the one with the same substance buried in flowing prose loses.
Entity coverage matters too. Wikidata, Wikipedia, Crunchbase, and sameAs markup all tighten the engine's confidence that you are who you say you are.
There's also a lane most brands ignore. Google's May 2026 update brought Reddit threads directly into the AI Overviews and AI Mode source panels, alongside YouTube and forum posts. For "what do real users think" questions, firsthand community content now sits next to publisher content. On Google's surfaces specifically, YouTube has overtaken Reddit as the top social source.
Worth separating AI Overviews from AI Mode while we're here. AI Overviews is the summary block on the results page. AI Mode is the chat-style tab that generates a longer answer. In a study of 730,000 responses, AI Mode lacked citations only 3 percent of the time against 11 percent for AI Overviews, and AI Mode fans a single query out into 8 to 12 sub-queries. Same index, same signals, two surfaces. Treat them as one citation strategy and two measurement targets.
Here's the honest constraint. AI Overviews does not invent candidates. If your page isn't already earning trust in Google's index, the summary block won't discover you. The lift happens upstream, on a 6 to 9 month arc.
Your first action for AI Overviews: take your highest-intent page and rewrite each H2 so the first two sentences answer the heading completely, with no setup.
Gemini: Google's signals, delivered in a sources panel
Gemini is the most Google-shaped engine of the five. If you're already doing careful SEO, you have a head start here, and that should feel like relief.
Its grounding feature calls the Google index at answer time and attaches the source passages it used. Same index that feeds web search and AI Overviews, which means Gemini leans toward Google's organic winners. A large majority of URLs surfaced in grounded Gemini answers also rank in Google's top 100.
Rendering is different from Perplexity's, and the difference matters for your expectations. Consumer Gemini shows a sources panel at the end of the answer with domain, title, and a short snippet, plus occasional chips at the top for the most-cited domain. Inline numbered markers are less common. Most consumer answers carry 3 to 5 web sources.
What Gemini overweights is classic trust. Named authors with credentials, real About and contact pages, editorial standards, and mentions in established outlets. It ranks passages rather than whole pages, so one sharp paragraph on a strong domain can win the citation even when the rest of the page is unremarkable.
Entity clarity is the other lever. sameAs links, Wikidata IDs, and schema.org markup all make it easier for the model to anchor a claim to your brand rather than to a similarly named company.
Gemini also ships a Double-Check feature that lets users test individual sentences against a live search, flagging high and low confidence claims. Pages that hold up under that scrutiny carry more weight than pages cited once and never revisited.
Your first action for Gemini: audit your top ten pages for author bylines with real credentials, and add organization schema with sameAs links to every profile your brand actually controls.
Microsoft Copilot: the least contested surface on the board
Copilot is the engine most marketing teams never track, which is exactly why it's worth your attention.
It grounds through Bing's index and adds Microsoft's knowledge graph and structured-data pools on top. Every consumer surface, Bing chat, Edge, Windows, and Microsoft 365, shares that same backbone. Citations render as footnote numbers in the body with a matching reference list underneath, at a density similar to ChatGPT's 3 to 6.
Because Bing's index is smaller and less crowded than Google's, on-page depth and publisher discipline go further. That's the opportunity in one sentence.
Four things carry weight.
Bing index eligibility comes first. Pages flagged under Bing's webmaster guidelines for cloaking, hidden text, doorway pages, or manipulated links lose grounding visibility across every Copilot surface.
Structured data comes second, and it's treated as stronger evidence than prose. Organization, Product, FAQ, Article, and BreadcrumbList all lift grounding eligibility. Bing reads FAQ markup as fact-grade evidence, so use it deliberately.
Topical authority and freshness come third. Named authors, dated updates, and clean citation hygiene get pages into grounding more reliably than undated generic content.
Knowledge graph alignment comes fourth. Microsoft owns LinkedIn, so an accurate company page pulls harder here than it does anywhere else. Entity disambiguation through schema helps for the same reason.
One perk you won't find elsewhere: Bing's AI Performance data exposes the grounding sub-queries the system fans out for a single prompt. That's visibility into query fan-out no other engine gives publishers.
Copilot's referrer share is smaller than ChatGPT's or Gemini's. The rules are stable, the competition is thin, and the citations stick. Low competition, high yield.
Your first action for Copilot: verify your site in Bing Webmaster Tools, confirm Bingbot isn't blocked in robots.txt, and add FAQ schema to your two highest-intent commercial pages.
Your priority map: where AI search visibility by platform actually starts
You do not need to work all five engines at once. You need an order. Here's a defensible one for the next ninety days.
Perplexity, very high priority. Inline clickable citations, a recency tilt that rewards refreshes fast, and vertical authority boosts that favor narrow depth over broad coverage. This is where effort turns into visible results quickest.
Google AI Overviews, very high priority. The largest absolute surface, and the one displacing traditional clicks. Most of the work is upstream Google SEO plus entity hygiene plus passage extractability. Slower, but the ceiling is highest.
ChatGPT, high priority. The biggest audience, the lowest per-conversation citation rate. Win here by triangulating across trusted third-party sources and by structuring content so it answers the buyer's very first question.
Copilot, high priority and low competition. Smaller share, well-defined rules, durable footnotes. Schema and Bing hygiene do most of the work.
Gemini, medium priority. Heavily correlated with your AI Overviews performance. Strengthen the Google-side signals and Gemini tends to follow.
The sequence itself is simple enough to run.
- Baseline all five engines on the same prompt set before you change anything.
- Choose the ten prompts your buyers genuinely ask. That's your battleground, not your keyword list.
- Ship depth-first, citation-formatted content on the highest-value prompts. Answer up top, named author, organization and FAQ schema, real data.
- Build the external layer: review-site profiles, trade press, community threads where your category actually lives.
- Re-audit at 60 to 90 days and watch which engines moved on which clusters.
Two numbers should shape how you spend. Comprehensive guides with data tables show the highest citation rate across platforms at 67 percent. And URLs cited by multiple engines score 71 percent higher on content-quality measures than single-engine citations.
Read those together and the strategy gets simpler. Shared plumbing wins. Clear passages, entity markup, FAQ schema, and honest data tables pay off on every surface at once, and the deep per-engine tactics sit on top of that foundation rather than replacing it.
Notice what that does to the sequencing question. Doing platform specific AEO well is not five separate content programs running in parallel. It's one well-built foundation plus a short list of engine-specific moves layered on top, in the order above.
There's also a rhythm difference worth planning around. Engines that scale recency, Perplexity most of all, reward refreshing article-level facts every 60 to 90 days. Engines that lean on Google's long-cycle index, AI Overviews and Gemini, reward cumulative topical authority built over months. One calendar can serve both, as long as you know which pages are on which clock.
Your first action here: pick one engine from the top of this list. Just one. Momentum matters more than coverage.
The four numbers that tell you it's working
You can't manage AI search visibility by platform on screenshots. You need four metrics, tracked per engine and per prompt.
Citation Rate. The share of tracked prompts where an engine cites your domain as a source. This is the core AEO number. Check it weekly.
Mention Rate. The share of prompts where the engine names your brand at all, link or no link. Mentions without citations are the early signal that you're entering the model's frame.
Share of Voice. Your slice of the citation pie relative to competitors on the same prompts.
Visibility Trend. Period-over-period movement in all three. This is the one that tells you whether the work is compounding.
Worth knowing: many trackers separate "used" from "cited." Plenty of content informs an answer without earning a link, so citation rate alone undercounts your real influence. If your goal is to get cited in AI answers, watch both, and treat a rising mention rate as the leading indicator it usually is.
Running this for one engine is a spreadsheet weekend. Running it for five, weekly, with per-page attribution, is its own unbudgeted quarter. That gap is the reason DeepSmith tracks Citation Rate, Mention Rate, Share of Voice, and Visibility Trend across ChatGPT, Gemini, Perplexity, Claude, and Google AI Mode, then feeds the gaps straight into content production. See where you're invisible, then close it, without switching tools between the two halves of the job.
Your first action: write down your ten buyer prompts today. Everything else in AEO depends on that list existing.
Where to go from here
One engine's signals are not a strategy anymore. Retrieval differs, rendering differs, recency weight differs, and source preferences differ. A plan tuned to one surface will quietly underperform on the other four.
That's the case for treating AEO by engine as the default posture rather than an advanced move. Brands that hold durable share of voice in AI answers are the ones treating each engine as its own retrieval problem, then shipping against each in turn.
The good news is that you don't start by writing more. You start by measuring where you're cited today, on which engine, for which prompt. The gaps tell you what to build, and that list is always shorter than the one in your head.
Pick one engine. Fix one prompt cluster. Come back in thirty days and check the number.
If you want to see how your brand actually shows up across the engines your buyers use, start a 7-day free trial and get real data and real drafts before you pay.
