Be the answer. AI search optimization for apps.

Ask ChatGPT or Gemini which app to use and you don’t get a page of links. You get an answer: a few apps, often a line on each, and the sources the model relied on. AI search optimization is the work of being in that answer, and being described well in it.

SEO still matters, since assistants run web searches of their own and pages that rank well are easier for them to find. But the goal moves from a place on a results page to a mention in a written answer. That’s why AI search optimization is measured differently: how often each model names your app, where it appears among the apps named, and in what tone.

There’s a second difference. An answer is one reply, not a page of ten results: if a model names three apps, the fourth is simply absent. Most teams need both: SEO for the links, AI search optimization (or GEO) for the answers.

It’s the same work under another name. GEO means improving the content and sources generative AI draws on, so its answers mention and recommend you; it takes its name from the generative AI models that write the answers, and many people use it interchangeably with AI search optimization. You’ll also see answer engine optimization (AEO) and LLM SEO. They share one goal: getting AI answers to mention and recommend you. The terms are new; what they measure is the same: whether AI answers name your app.

For an app, generative engine optimization reaches well beyond a website. The models also read your App Store and Google Play pages, and the Reddit threads, Quora answers, YouTube videos and articles where people compare apps. GEO works on all of them, because any of them can end up behind an answer. Read more in What is GEO?

An assistant answers from what it learned in training and, often, from web searches it runs in the background while it writes. Those searches bring in pages, and the answer may cite them as its sources.

So the same question can name different apps in ChatGPT, Gemini and Perplexity, and an answer can change from one day to the next, which is why GEO is measured per model, not once for all of them. An app that’s discussed often, and well, in the sources a model trusts is more likely to be named; an app missing from them is easy to leave out. That’s where generative engine optimization starts: with the sources.

Wording matters too. “The best app to learn Spanish” and “an app to practice Spanish conversation” can bring different apps, so track the questions your users actually ask, in their own words. Good AI search optimization starts from their language, not yours.

Four measures tell you where you stand in AI search optimization:

• Visibility: how often each model names your app, across the questions you track.
• Average rank: where your app appears among the apps an answer names.
• Favorability: the sentiment of each answer towards your app.
• Sources: the pages behind each answer, and how often each one is cited: where your GEO work goes.

Follow them per model, over time and next to your competitors. One day’s answer is a snapshot; the trend tells you whether your GEO is working.

Start with the gaps: the questions where competitors are named and your app isn’t. The sources behind those answers show where generative engine optimization will pay off:

• Reddit and Quora: useful answers in the threads where people ask which app to use.
• YouTube: videos that show the app at work.
• Medium: articles on the problem your app solves.
• Your FAQ: clear answers to the questions people ask AI.
• Custom Product Pages: App Store pages for specific needs and searches.
• Your store pages: a clear App Store and Google Play description of what the app does and who it’s for, since the models read those too.

Then measure again, on the same questions, to see which GEO moves worked. Answers shift as their sources change, so AI search optimization is ongoing work, not a one-off fix.

ASO Agent checks your tracked questions every day in up to 7 AI models, depending on the plan: ChatGPT, Gemini, Perplexity, Google AI Mode, Google AI Overviews, Claude and DeepSeek. It reads each answer from the assistant’s web interface, as people see it, not through an API, and keeps every answer in full, with its sources and background searches, so your AI search optimization rests on the answers people actually get.

You choose the questions, or start from suggested prompts, and pick the models for each; pausing a question keeps its history.

It shows your visibility, average rank and favorability next to every competitor you add, and turns each gap into generative engine optimization (GEO) recommendations by channel, with drafts ready to review. It’s AI search optimization built for apps, with their App Store and Google Play pages, from €79/month.