AI search and LLMs bring a new era of app discoverability

The app discovery journey is becoming more fragmented as AI search engines rapidly emerge as another discovery layer between user intent and app install.

Users are beginning to ask ChatGPT, Gemini, or Claude which app they should download. These systems interpret intent, retrieve information from across the web, and synthesize recommendations in a single response. After receiving that shortlist, users then open an app store to confirm the app aligns with their needs before installing.

This new discovery layer does not eliminate the app stores as the primary conversion environment. But it changes how consideration is formed. Visibility is increasingly influenced by how AI engines understand your app, how clearly it aligns with specific user intents, and how consistently it is represented across the web.

Key takeaways

What is AI app discovery?

AI app discovery refers to how AI search engines recommend apps in response to user intent.

Instead of returning a list of links like traditional search engines, AI search engines such as ChatGPT, Gemini, and Claude generate synthesized answers that directly suggest specific apps. These recommendations appear inside AI-generated responses as curated shortlists tailored to the user’s question.

AI app discovery is intent-driven, not keyword-driven. Users describe what they want to achieve, such as “a budgeting app for freelancers,” “a dating app for serious relationships,” or “a calorie tracker that works offline.” The AI search engine interprets that intent, expands the query internally, retrieves relevant information from across the web, and determines which apps best match the underlying job to be done.

AI app discovery is recommendation-based, not ranking-based. Unlike traditional search engines that present ten blue links, AI search engines evaluate multiple sources and suggest a small set of apps as solutions. The output is structured as guidance rather than search results.

AI app discovery also sits upstream of App Store conversion. In many cases, users first receive a shortlist from an AI assistant and only then open the App Store or Google Play to evaluate screenshots, ratings, pricing, and positioning before installing. The app stores remain the primary conversion environments, but AI search increasingly shapes which apps enter consideration.

Want practical guidance on improving your app’s visibility in AI search?

Join us for our upcoming webinar, How to make your app discoverable in ChatGPT & LLM search. Together with industry experts, we’ll unpack how AI search engines interpret intent, evaluate signals, and surface app recommendations across categories. You’ll walk away with actionable insights for your app’s AI discoverability.

How do AI search engines decide which apps to recommend?

AI search engines rely on two primary inputs when recommending apps: model training knowledge and live web retrieval.

Understanding this distinction is important. Visibility in AI-generated answers depends both on how your app is represented across the internet and on what content the system can access when responding to a specific query.

Input 1: Model training knowledge

AI search engines rely on large language models (LLMs) trained on massive amounts of public text, including websites, forums, blogs, reviews, and online discussions.

During training, these models learn associations between:

• App names and use cases
• App names and audiences
• App names and attributes

If an app is frequently mentioned in conversations about a specific task, the model develops a stronger association between that app and that intent.

This means online community discussions, comparison pages, and editorial mentions contribute to how AI search engines “understand” your app. However, once a model is trained, that internal knowledge does not change until the next version is released.

Input 2: Live web retrieval

In addition to what they have been trained to know, most AI search engines fetch live web content before generating an answer.

When a user asks for an app recommendation, the system:

1. Interprets the intent behind the question.
   
2. Searches the web for relevant content.
3. Extracts the most relevant passages.
4. Synthesizes a shortlist of apps.

This retrieval input favors content that clearly and directly addresses user intent. Structured comparisons, question-based explanations, and unambiguous descriptions are easier for AI search engines to extract and synthesize than vague or purely promotional language. AI search engines typically retrieve and evaluate specific passages (“chunks”) from documents rather than processing entire websites end-to-end in real time.

Why this matters for app discovery

An app can be recommended by an AI search engine because:

• It is strongly associated with a specific use case in the model’s training knowledge.
• It appears clearly and consistently in retrieved web content.
• Or both.

This explains why AI visibility does not perfectly mirror App Store rankings. Download volume may indirectly influence AI visibility because popular apps generate more reviews, comparisons, and editorial coverage across the web. However, clarity of positioning, strength of web representation, and alignment with user intent directly influence recommendation likelihood. To ensure your app is matching user intent within the app store, check out our blog on How AI is changing relevance in app store search.

Do AI search engines use app store rankings to recommend apps?

AI search engines do not directly use app store ranking positions as a primary decision input when recommending apps.

Unlike the App Store or Google Play, which rank apps based on keyword relevance, downloads, engagement, and conversion signals, AI search engines focus on intent alignment and web-based evidence when generating recommendations.

AI search engines do not have access to internal App Store algorithms or proprietary ranking data. They rely on publicly available web content. In some cases, they may retrieve web versions of app store listings (particularly Google Play listings) which means publicly accessible app metadata can still influence AI-generated answers indirectly.

For a deeper look at app store ranking signals, see our articles on the top App Store ranking factors and top Google Play ranking factors.

This does not mean App Store performance is irrelevant. Popular apps tend to be mentioned more frequently across the web: in reviews, comparisons, discussions, and editorial coverage. That broader visibility strengthens both model training associations and retrieval likelihood.

However, ranking highly for a keyword in the App Store does not automatically translate into frequent inclusion in AI-generated recommendations.

At a high level, AI search engines evaluate apps based on:

• How clearly the app aligns with the user’s specific intent
• How strongly the app is associated with that use case in model training knowledge
• How consistently the app appears in relevant web content

This explains why AI visibility and App Store rankings are correlated but not identical. An app may dominate a high-volume keyword inside the App Store yet appear infrequently in AI recommendations if its positioning is unclear or its web presence is weak. Conversely, a niche app with strong intent clarity and consistent web mentions may surface often in AI-generated answers even without top App Store rankings.

Example: App Store rankings vs ChatGPT recommendations

To illustrate this difference, on February 13 (the day before Valentine’s Day) we compared:

• The top dating apps in the U.S. App Store (per AppTweak data)
• ChatGPT’s response to “What are the top dating apps in the U.S.?”

There is meaningful overlap between the two lists. Major players like Tinder and Hinge appear prominently in both, indicating that overall popularity still influences AI-generated recommendations.

However, the lists are not identical. Some apps that rank highly in the App Store appear lower or not at all in ChatGPT’s recommendations, while others surface more prominently than their store rank alone would suggest.

This difference reflects the distinct inputs at play: App Store rankings tend to prioritize downloads, conversion, and keyword performance, whereas AI search engines prioritize intent alignment, training associations, and web-based representation.

For ASO and UA teams, this distinction is critical. ASO remains essential for conversion and in-store discoverability. But AI search introduces an additional recommendation environment where ranking signals alone are not sufficient to drive visibility.

Conclusion

AI search introduces a new way for apps to be evaluated and recommended. It’s not replacing the app stores, but it is reshaping how users arrive there.

ASO remains essential for conversion and in-store visibility. At the same time, users are increasingly turning to AI search engines to find the right app for their needs. These systems evaluate apps through a different lens, interpreting intent and synthesizing recommendations based on the signals they retrieve and associate.

For ASO and UA teams, this means app discoverability now depends on more than ranking position alone. Understanding how AI search engines assess and surface apps is becoming an important extension of traditional ASO strategy.

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