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Guide · AI answers for apps

How to get your app recommended by ChatGPT

When someone asks ChatGPT for “an app like yours”, the page it leans on most is your store listing. Then it looks at the web around you. Here are five moves that make your app the one it names, backed by the data on what ChatGPT actually cites.

· 7 min read

An answer names a few apps and cites a page for each, most often a store listing. The app names here are made up for illustration.

Where ChatGPT’s app recommendations come from

The best public data so far is AppTweak’s analysis of more than 125,000 ChatGPT responses to over 9,000 app prompts, all non-branded questions like “best app to track my running”, in the US in May 2026. [1] In the answers that recommended apps, this is what ChatGPT drew on:

  • App Store listings37.6%
  • Google Play listings9.6%
  • The open web: your own site, review sites, articles27%
  • No source cited: what the model already knows22.6%
What ChatGPT’s app recommendations drew on, US, May 2026. [1]

So the store listings together were 47.5% of citations, more than any other kind of page. Two more findings matter if you’re trying to get in:

  • The top of the list is backed by sources, the bottom isn’t. Apps in places 1 to 4 had a source behind them 80 to 85% of the time; by place 8 it was under 25%. [1] A weakly sourced app low on the list is the easiest one to replace.
  • It depends on the category. Listings were 65.8% of citations for weather apps but 29.3% for food and drink and 31.9% for finance, where other web pages made up 45.7% and 44.3%. [1]
  • Places 1 to 480–85%
  • Place 5about 68%
  • Place 7under 50%
  • Place 8under 25%
How often an app at each place in ChatGPT’s list had a source behind it. The write-up gives no figure for place 6. [1]

Keep in mind what this is: one company’s study (AppTweak sells an AI visibility tool for apps), of ChatGPT only, in one country, at one point in time. AI answers change as the models change. It’s still the clearest picture there is, and the advice below follows from it. Our guide to where ChatGPT’s app recommendations come from goes through the study in detail.

1. Turn your listing into the answer

If the listing is the page that gets read, the first lines of your description are your pitch to the model. AppTweak’s advice is to open with one sentence in this shape: [2]

[App name] is a [type of app] that helps [who it’s for] [do what] by [how].

Then:

  • Pick three to five use cases and say them the same way everywhere in the listing.
  • Name concrete features instead of brand language. “Plans your week from your calendar” beats “your productivity superpower”.
  • Tie each feature to what it gets the user, and say who the app is a good fit for.
  • Add facts a model can repeat: platforms, offline support, integrations, certifications, real user numbers.
Two versions of the same App Store listing. Before: the subtitle reads Adventure awaits, and the description opens with Your ultimate outdoor companion. After: the subtitle reads Offline hiking maps, and the description opens by saying who the app is for, what it does and how.
The same listing before and after, following AppTweak’s pattern. [2] The app is a made-up example.

This lines up with Apple’s own advice: open the description with a short, informative paragraph, since the first sentence is the one people see, then list the main features. [3] Two things to know about the other fields:

  • The keyword field (100 characters) helps App Store search but isn’t shown on your public listing page, so a model reading that page never sees it. [3]
  • The name and subtitle (30 characters each) are on every page that mentions your app. A subtitle that says what the app does (“Habit tracker for ADHD”) works for both the store and the model. [3]

On Google Play the same goes for the short and full description: Google asks for a clear, factual account of what the app does and what makes it different, without repeated keywords. [4] The guides to your App Store listing and your Google Play listing go field by field.

2. Open your website to AI search

A quarter of citations came from the open web, and that share is higher in categories like finance. None of it can include your site if the assistant’s crawler is blocked. Each company documents its own:

  • ChatGPT: OpenAI says sites that block OAI-SearchBot “will not be shown in ChatGPT search answers”. GPTBot is a separate crawler, for training. [5]
  • Claude: Anthropic says blocking Claude-SearchBot “may reduce your site’s visibility and accuracy in user search results”. ClaudeBot is for training. [6]
  • Perplexity: PerplexityBot is what surfaces and links sites in Perplexity’s answers. [7]
  • Gemini: Google-Extended controls training and grounding in Gemini apps and doesn’t affect Google Search. [8]
  • OAI-SearchBotSearch: allowed
  • Claude-SearchBotSearch: allowed
  • PerplexityBotSearch: allowed
  • GPTBotTraining: your call
  • ClaudeBotTraining: your call
Search crawlers decide whether your site can be cited. Training crawlers are a separate choice. [5] [6] [7]

You can let the search crawlers in and still keep your pages out of training. A robots.txt that does that: 

User-agent: OAI-SearchBot
Allow: /

User-agent: Claude-SearchBot
Allow: /

User-agent: PerplexityBot
Allow: /

# Optional: opt out of training
User-agent: GPTBot
Disallow: /

User-agent: ClaudeBot
Disallow: /

Check your CDN too. Some firewall and bot-protection settings block AI crawlers whatever robots.txt says, so look at your logs or your CDN’s bot report for real visits.

You may also hear about llms.txt, a Markdown file at your site’s root that sums up the site and links its key pages for language models. It was proposed by Jeremy Howard in 2024. [9] It’s cheap to add, but treat it as a proposal: we haven’t seen OpenAI, Anthropic or Google say their assistants use it to choose what to recommend. More in our guide to llms.txt, AI crawlers and your app’s website.

3. Give AI a page worth citing

Where AppTweak found brand-owned and aggregator pages leading in a category, its advice was to build an owned product page and FAQ. [1] For most apps that means one page, linked from your home page, that says in plain words:

  • what the app is and who it’s for, in the same words as your listing;
  • what it does, feature by feature, with what each one gets the user;
  • platforms, price and plans, and links to the App Store and Google Play;
  • how it differs from the apps people compare it with, honestly;
  • answers to the questions people ask before they download.

Keep that text in the HTML, not only in images or behind a script, so a crawler that doesn’t run JavaScript still sees it.

4. Show up where AI looks

Part of the open web share is other people’s pages: “best apps for…” round-ups, review sites, Reddit threads. For games, AppTweak counted about five times as many Reddit citations as for apps. [1] The useful work here is finding which of these pages the answers in your category actually cite, then getting onto them: pitch the authors of round-ups with something new, answer real questions in the communities where your users are, and keep your app’s details current on review sites. Don’t post fake reviews or paid praise; it breaks the rules of most of these sites, and it isn’t worth the risk. Our guide to getting into the round-ups and threads AI cites covers this.

5. Know where you stand, every week

You can’t improve what you don’t look at. Write down 10 to 20 questions a customer might ask before they’d download an app like yours, without your app’s name in them: “best budgeting app for couples”, “an app to learn Japanese kanji offline”. Then ask them in ChatGPT, Gemini, Claude and Perplexity, and note for each:

  • whether your app is named, and in what place;
  • which apps are named instead of you;
  • which pages the answer cites, especially when it names a competitor and not you.

That last list is your to-do list: those are the pages that need to mention you, or that you need to beat. Answers change from one run to the next and as the models change, so one check is a snapshot, not a measurement. Ask the same questions every week and watch the trend. Our guide on how to check if ChatGPT, Gemini and Claude recommend your app has a step-by-step method, and what to measure explains the numbers worth tracking.

Sources

  1. Pierre-Antoine Roy, Which sources do AI search engines cite when recommending apps, AppTweak, 30 September 2026. Data: more than 125,000 ChatGPT responses, US, May 2026.
  2. Pierre-Antoine Roy, How to optimize your app store listing for AI search engines, AppTweak, 16 August 2026.
  3. Apple, Creating your product page, Apple Developer.
  4. Google, Metadata policy, Play Console Help.
  5. OpenAI, Overview of OpenAI crawlers.
  6. Anthropic, Does Anthropic crawl data from the web, and how can site owners block the crawler?
  7. Perplexity, Perplexity crawlers.
  8. Google, Google’s common crawlers, Google Search Central.
  9. Jeremy Howard, The /llms.txt file, 3 September 2024.

Sources checked .