Today we’re adding measurement to an area that, until now, had no data: mobile app visibility inside AI-generated answers from LLMs.
Say hi to “AI Search for Mobile Apps”
We are happy to announce that LLM Pulse now tracks app brand mentions, share of voice, and citations of website + App Store and Google Play direct links across ChatGPT, Perplexity, Google AI Mode, and AI Overviews. This dataset didn’t exist before. It helps teams understand how LLMs drive discovery, recommendations, and user decisions. This feature is available starting from the Growth plan and above.
We also analyze brand reputation in every response we process, giving you a clearer view of how each model perceives and frames your product.
This new layer connects SEO, GEO (AI Search), and ASO in a single analytical framework. SEO covers classic web visibility. ASO remains essential for visibility + performance + reputation inside App Store and Google Play. GEO adds how LLMs interpret each brand, respond to transactional queries, and reference, among others, official store links.
The combination of these three areas gives teams a complete view of the discovery cycle: web, app stores, and generative engines. Until now, each channel was measured separately. Starting today, you can finally see how they relate and evolve together, because all three are deeply connected.
For brand, marketing, growth, and product teams, this unlocks more precise analysis. You can detect when your app begins to appear in relevant prompts. You can benchmark your share of presence against competitors. And you can verify whether LLMs are recommending the correct App Store and Google Play URLs.
This is a natural step in the evolution of digital visibility. And it provides a solid foundation for understanding how LLMs impact app discovery. That’s what we’re introducing today with LLM Pulse.
And, remember:
– Do not try and bend the spoon. That’s impossible. Instead… only try to realize the truth.
– What truth?
– There is no spoon.
FAQ
What is “AI Search for Mobile Apps” and how does it differ from traditional ASO and SEO?
AI Search for Mobile Apps focuses on how apps appear inside AI-generated answers from systems like ChatGPT, Perplexity and Google AI. Unlike ASO or SEO, which optimize listings and websites for traditional rankings, AI Search analyzes how LLMs reference, recommend and justify apps in real conversations. It reveals visibility, citations and sentiment in a discovery channel that sits between search engines and app stores.
How does LLM Pulse track app brand mentions, share of voice and citations across ChatGPT, Perplexity, Google AI Mode and AI Overviews?
LLM Pulse analyzes real AI answers across multiple engines and identifies how often each app is mentioned, which links are cited and how visible each brand is compared to competitors. It processes the structure of each answer, captures sentiment and aggregates the data into share-of-voice metrics. This provides a unified view of visibility across all major AI search systems.
Why is it important for mobile brands to monitor App Store and Google Play links inside AI-generated answers?
App Store and Google Play links indicate explicit recommendations, since LLMs only cite them when they consider an app relevant or trustworthy. Tracking these links helps brands understand how often AI systems direct users toward their app vs competitors. It also reveals how discovery is shifting from traditional search results to AI-curated answers.
How do SEO, ASO and GEO work together to give a complete view of the mobile app discovery cycle?
SEO brings users from Google, ASO converts them in the stores, and GEO (Generative Engine Optimization) shows how AI systems decide what to recommend before users even search or browse manually. Together, they form a full funnel: content influences AI engines, AI engines influence user intent, and store listings convert that intent into installs and usage.
Which teams inside a company benefit most from AI Search for Mobile Apps and what decisions can they make with this data?
Growth, marketing, product and ASO teams gain a clearer view of how their app is perceived by AI engines and how recommendations shift over time. With this data they can adjust messaging, improve store listings, refine content strategy and respond to competitor movements. It also helps decision-makers understand where to invest in acquisition and how AI-driven discovery impacts performance.
