Key points:
- AI assistants answering questions in chat mostly pull from crawled web content, not app store search indexes. A strong web presence around your app likely matters more here than your store listing does.
- No official Apple, Google, OpenAI, or Perplexity page describes exactly how their assistants pick which apps to recommend. Be wary of anyone stating this with false confidence.
- One vendor, AppTweak, has published its own research claiming app store listings account for a large share of citations in ChatGPT app recommendations. This is single-vendor data, not independently checked. Treat the exact numbers as a rough signal, not settled fact.
- The ChatGPT App Directory is a real, documented, in-chat integration. It’s a completely different thing from getting mentioned in a plain text answer. Don’t confuse the two when planning what to build.
Someone asks ChatGPT, or Gemini, or Perplexity, “what’s a good app for X.” An answer comes back naming two or three apps. Yours isn’t one of them. This is a genuinely new kind of discovery. Still forming. Worth understanding honestly instead of chasing whatever a vendor blog claims works.
The real mechanism looks different from ASO
Store search draws on a private index that Apple and Google control. An AI assistant answering a general question mostly doesn’t touch that index at all. It pulls from the same kind of crawled web content that powers a search engine’s own AI answers. Articles. Review sites. Forums. Your own public web pages.
That means the metadata work that moves your App Store rank, your title, your keyword field, your screenshots, mostly doesn’t reach this channel at all. What matters instead looks more like old-fashioned digital PR and content work. Does the open web actually contain clear, accurate, citable text about what your app does and who it’s for.
Get this one distinction right before you spend a single rupee here.
What’s actually documented, versus what’s vendor-claimed
There’s a real academic paper behind the general idea. A 2024 paper, “GEO: Generative Engine Optimization,” coined the term. It ran real tests showing content CAN be tuned to appear more often in AI-generated answers. That research is about general web content, though. Not apps specifically.
The app-specific numbers you’ll see quoted come almost entirely from one ASO vendor, AppTweak. It published research claiming that, across a large sample of ChatGPT app-recommendation replies, App Store and Google Play listings together made up roughly 47% of cited sources. Split unevenly between the two stores. It also claims a real share of recommendations came with no cited source at all. Those are real published numbers. They’re also self-reported by one company, with no outside audit of the method. Treat them as a rough signal, not a fact worth building a whole strategy on.
| Claim | Source | How solid |
|---|---|---|
| AI-tuned content can shift generative-answer visibility | Academic paper (GEO, 2024) | Peer-reviewed, but about web content, not apps |
| Store listings get cited often in ChatGPT app answers | One ASO vendor’s own study | Real numbers, but unaudited and single-source |
| A confirmed rule for how assistants pick apps | No one has published one | Not documented by any platform |
Don’t confuse this with the ChatGPT App Directory
There’s a genuinely real, separate thing worth knowing about. The ChatGPT App Directory, built on OpenAI’s own Apps SDK, lets an app run right inside a ChatGPT chat. Early confirmed partners include Spotify, Canva, and Booking.com. This is real. It’s documented. It needs actual engineering work to build.
It is NOT the same as getting named when someone asks ChatGPT a plain question about apps in your category. That’s a text answer, not an in-chat app, and the two need completely different work. Don’t let a pitch that blends both into one “AI ASO package” confuse what you’re actually paying for.
Real-world scenario: A budgeting app’s team noticed a few new signups mentioning they’d asked an AI assistant for a recommendation and got pointed to a rival app instead, despite their own app having better reviews. They checked their own web presence and found almost nothing. No blog. Thin App Store text. No outside articles reviewing the app anywhere. Their rival, by contrast, had several detailed comparison posts and a real blog with specific feature write-ups. All content an AI assistant could actually cite. The budgeting app’s team started publishing real, useful posts about the exact problems their app solves. Within a couple months, users again reported the app coming up in AI assistant answers. This time by name.
What to actually do, given the uncertainty
- Build real, crawlable content about what your app does and who it’s for. Not store-listing copy. Real posts, comparisons, and specifics an AI system can actually cite.
- Don’t skip outside coverage. Reviews, comparisons, and mentions on other sites are exactly the kind of content these systems pull from.
- Treat any single vendor’s number on this topic as a rough signal, not proof. The field is too new, and too thin on real platform documentation, for false confidence to help you here.
- Keep your actual App Store listing solid regardless. It’s still the base for normal store search, which stays the bigger discovery channel today by far.
None of this replaces the basics. AppRankr’s ASO Inspector still starts with getting your real listing right. That’s the one channel with a fully documented, controllable set of levers, unlike this one.