From SEO and ASO to GEO: Staying Visible When Customers Ask ChatGPT Instead of Google
Thomas Henz
More and more people no longer ask Google for the “best app for …”, but ChatGPT, Claude, Gemini or Perplexity. Anyone who is not recommended there does not exist for those users, no matter how good the store ranking is. Here is what that means for app providers and which three levers matter now.
Picture the following scene: a potential customer asks ChatGPT which app she should use for time tracking in her craft business. She gets three recommendations with short explanations, taps a link and lands directly in the App Store. Your app was not among them, even though it ranks second in classic App Store search and the website is easy to find on Google.
This is not a future scenario, it happens every day. AI assistants are becoming a discovery channel of their own for apps, and they work by different rules than Google search and the store algorithm. The discipline that deals with this is called Generative Engine Optimization (GEO): optimizing so that generative AI systems know an app or product, describe it correctly and actively recommend it.
Is SEO being replaced by GEO?
No. GEO does not replace SEO and ASO, it builds on them. But anyone who keeps treating landing page, store listing and app as three separate construction sites will systematically lose visibility in this channel. A look at how AI assistants find apps in the first place shows why.
How ChatGPT and friends pick apps
An AI assistant has no access to Apple’s or Google’s ranking algorithm. Its recommendations come from two sources:
1. Training knowledge: associations the model learned from public web content during training. If your app is mentioned in expert articles, comparisons and forums in the context of a problem, that connection is anchored in the model.
2. Live research: for current questions, the assistants search in real time and mainly draw on four types of sources: the store listings including reviews, “best apps for …” roundup articles from third-party sites, the provider’s website and community sources such as forums or Wikipedia.
The decisive difference to classic search: the model cross-checks these sources against each other. If the store listing says “project management for teams” but the landing page says “the all-in-one solution for digital work”, a contradiction arises. And contradictions reduce the model’s confidence. In case of doubt, the app simply drops out of the recommendation because the assistant can describe a different app more safely. Consistency across all channels is therefore no longer a marketing finishing touch, it is a ranking factor.
That leads to three concrete levers.
Lever 1: The landing page, from shop window to data source
Your app’s landing page used to have two jobs: get found on Google and move visitors to download. Now a third one is added: explain in a machine-readable way what the app does. Because AI assistants read it as a source of facts, not as advertising space.
First of all, that means clarity of language. A slogan like “work, rethought” is worthless to a language model. The first sentence of the page should say in plain English (and ideally German too) what the app solves and for whom: “App for digital time tracking for craft businesses with 5 to 50 employees.” Only after that may the brand world follow.
The technical part is structured markup. With a JSON-LD block of type SoftwareApplication you hand the assistant price, platform, category and rating as clean data instead of running text it has to interpret:
{
"@context": "https://schema.org",
"@type": "SoftwareApplication",
"name": "CraftTime",
"operatingSystem": "iOS, Android",
"applicationCategory": "BusinessApplication",
"description": "Digital time tracking for craft businesses",
"offers": { "@type": "Offer", "price": "9.90", "priceCurrency": "EUR" },
"aggregateRating": { "@type": "AggregateRating", "ratingValue": "4.7", "ratingCount": "312" }
}Add an FAQ section (with schema markup as well) that answers real user questions, exactly the questions people put to an assistant: how much does the app cost? Does it work offline? Is it GDPR compliant? Every cleanly answered question is a potential answer the assistant can quote verbatim.
And: prices, feature descriptions and positioning have to match the store listing right down to the wording.
Lever 2: The store listing, ASO learns semantics
The stores themselves are moving towards AI search too. In June 2025 Apple rolled out its search update, which ASO tool vendors describe as the biggest measured change in years: instead of pure keyword matching, search increasingly recognizes user intent and deliberately shows different result types for the same query. And with “Ask Play”, Google is bringing a Gemini assistant straight into the Play Store.
For your listing that means keyword stuffing keeps losing value, while clearly worded benefit descriptions win. In the end the same plain-language logic applies as on the landing page. We described how the fundamentals of app store optimization work in a separate guide. Two elements become particularly important because they work twice over (in the store and as a source for AI assistants):
Reviews and your responses to them. Assistants read reviews to summarize an app’s strengths and weaknesses. An unanswered complaint thread about crashes thus becomes the public justification for why the app is not recommended. Anyone who visibly responds to criticism and communicates fixes co-writes that summary. Hard quality metrics such as the crash rate feed into this as well, because instability shows up in reviews, and reviews show up in AI answers.
Mentions on third-party sites. “Best apps for …” articles, expert blogs and comparison portals are highly weighted sources for AI assistants. Classic PR and content work thereby gains a measurable new purpose: every reputable mention increases the probability of appearing in AI recommendations.
Lever 3: The app itself becomes addressable for agents
The most underrated lever lies in the app itself. Both platforms are currently building the infrastructure for AI assistants to not just recommend apps, but to operate them directly:
On iOS that is the App Intents framework: developers declare individual app functions (“create a time entry”, “show the weekly report”) as actions that Siri, Spotlight and Apple Intelligence can invoke in natural language.
On Android, the new AppFunctions API takes over from Android 16 onwards: functions are marked in code via annotation as “tools” for assistants such as Gemini, and are conceptually the mobile counterpart to the tool interfaces that ChatGPT integrations also run on. The app effectively becomes a server that an agent addresses on the user’s behalf.
For decision makers the point of this development is strategic: when users say “log my site hours for today” and the assistant simply does it, the app whose functions the assistant can invoke wins. Anyone who exposes their core functions early as intents or AppFunctions gains a lead that pure marketing cannot catch up with. That is development work, not a question of ad budget. Much of it is still at preview stage, which is exactly why now is the right time to prepare your own app architecture for it.
GEO checklist for apps: the 7 most important points
- Consistency audit: do the landing page, App Store listing and Play Store listing describe the app identically (benefit, target group, price)? Eliminate contradictions.
- Plain-language test: does the first sentence of the landing page say what the app does and for whom, without marketing speak? As a test: ask ChatGPT what your app does and compare the answer with reality.
- Structured data: add and validate
SoftwareApplicationandFAQPagemarkup on the landing page. - Expand the FAQ: answer the ten most frequent real customer questions in plain language, on the website and in the store description text.
- Review management: respond to negative reviews, name the problems you fixed, keep an eye on the crash rate.
- Check third-party sources: which “best apps” articles in your category feature your app, and which feature the competition? Build mentions in a targeted way.
- Clarify agent readiness: with your development team (or your agency), assess which 3 to 5 core functions can be exposed as App Intents / AppFunctions.
Conclusion: the decision is made before the store
In 2026 an app is still installed in the App Store or on Google Play. But the decision about which app gets installed is increasingly made before that: in a chat with an AI assistant. What wins there is not the biggest ad budget, but being consistent, machine-readable and credible across all channels, with an app that is technically ready for a world in which software is operated by agents.
The lead is built exactly at this intersection of web, store and app development. Anyone who thinks about all three levers from a single source, instead of sending an SEO agency, an ASO freelancer and a development team off separately, has a structural advantage.
Would you like to know how visible your app is to AI assistants and how agent-ready your architecture is? We will take a look with you: from the landing page through the store listing to App Intents and AppFunctions. Get in touch.
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