Five AI answer engines, five different shortlists.
Ask the same buying question five ways and you get five answers, five brand slates and five sets of sources. What each answer engine is actually for, which ones reach your buyer, and which ones merely cite well.
· · By the Askwords team
Ask whether you are visible in AI and you have asked five questions, not one. The five answers disagree, and rolling them into a single number hides the only thing you can act on: which answer engine is failing you, and why that one.
The five answer engines are not skins on one product. They retrieve differently, cite differently, and carry wildly different audiences. Knowing which is which stops you optimizing for the one that is easiest to measure instead of the one your buyers use.
Two ingredients, mixed in different proportions
Every answer engine blends two things: what the model already believes about your category from training, and what it retrieves from the live web at the moment of the question. The mix is the whole story.
AI Overviews, AI Mode, Perplexity
The answer is assembled from pages fetched now. Publish something genuinely better and you can move within weeks, but you have to be crawlable, and you have to be the clearest page on the question.
ChatGPT and Gemini, when not searching
The answer draws on associations built across the whole web over a long period. Slower to shift, and shifted by third-party mentions far more than by anything you publish yourself.
What each one is good for
The weights below are reach weights: our estimate of how much of a real buyer population each engine represents, used to keep one of them from dominating a score it does not dominate in life.
Google AI Overviews
It appears above the results for an enormous share of everyday queries, which means most buyers meet it without choosing to. It is conservative, leans on pages that already rank, and rarely names a brand it cannot support with a link.
Main lever: Rank well, then make the passage that answers the question quotable on its own.
ChatGPT
People come here to think out loud, so the questions are longer and more revealing than anything in your keyword data. It blends what the model already believes about your category with pages it retrieves live, which is why an old reputation can outlive a new website.
Main lever: Third-party pages and consistent brand facts, not just your own site.
Google AI Mode
A conversational layer over Search, growing fast. It fans one question into several behind the scenes, so being strong on a single phrasing helps less here than being consistently present across a topic.
Main lever: Topic coverage: several angles on one subject, not one page that tries to be everything.
Gemini
Grounded in Google’s own index and increasingly the default answer engine on Android hardware. It behaves more like AI Overviews than like ChatGPT, but it will make a firmer recommendation when asked directly.
Main lever: Everything that helps AI Overviews, plus clean structured facts about your business.
Perplexity
The smallest audience of the five and the most generous citer, often close to twenty sources on a single answer. That makes it a poor proxy for reach and an excellent laboratory for finding out which pages the web currently treats as authoritative in your category.
Main lever: Use it to read the competition’s evidence, not to measure your own reach.
Counting answer engines equally quietly lies to you
Imagine two businesses. One is cited by AI Overviews and absent everywhere else. The other is cited by Perplexity and absent everywhere else. Count answer engines equally and they score identically. In reality the first is being recommended to a vastly larger audience. The gap is orders of magnitude, not percentages.
So weight by reach. Google AI Overviews and ChatGPT together account for roughly two thirds of what a buyer is likely to see; the other three split the remainder. A visibility score that ignores this is a score you can improve without selling anything.
The honest caveat
Citations are a different currency from mentions
Perplexity will link twenty sources for one answer. AI Overviews will link a handful. ChatGPT may name four brands and link none of them. This has a practical consequence that catches people out: the engines you are easiest to cite on are not the ones where being cited matters most.
Use the generous citers as a research instrument. If Perplexity keeps citing the same three domains for your category question, those domains are the ones the wider web currently treats as authoritative, and that is a target list for coverage, not just a stat.
Cited on a big answer engine
Real distribution. Protect the page; it is doing sales work.
Cited only on a small answer engine
Your evidence works. It just has not reached the answer engines that matter yet.
Mentioned everywhere, cited nowhere
The web is describing you. Find out who, and whether they are describing you correctly.
One rule for a small team
If you have limited time, and you do, work in this order. It ranks by how much buyer attention each fix touches per hour spent.
- 1
Make sure answer bots can reach you
Nothing below this line matters if the crawlers behind these answer engines are blocked. It is a ten-minute check and it is occasionally the entire problem.
- 2
Win the Google answer engines first
AI Overviews, AI Mode and Gemini share the underlying index and largely share the fix. One body of work, three answer engines, about 62% of the weight.
- 3
Earn third-party coverage for ChatGPT
The memory-heavy answer engine responds to what other sites say about you. Reviews, roundups, directories, comparison pages: the pages you do not control.
- 4
Use Perplexity as a mirror
Read its citations to find out what evidence is winning in your category, then go and build a better version of it.
Read the split, never the average.
Keep reading
Sources and further reading
- Google Search Central: AI features and your website
- Google Search Central: Optimizing your website for generative AI features
- OpenAI: Bots, crawlers and how ChatGPT retrieves pages
Askwords measures a selected set of buyer questions against fresh answers from each engine. AI answers vary by model, time, context and personalization, so a check is evidence of the answers observed, not a universal judgement about a brand.