How to Get Your App Recommended by ChatGPT and Other AI Assistants

ChatGPT, Claude and Gemini recommend the apps the web already calls the best for a given job. When someone asks an assistant for the best habit tracker, it answers with three to ten names, and those names come from store listings, reviews, comparison articles and forum threads it has read. There is no form to submit your app and nothing to pay. To get your app recommended by ChatGPT and other AI assistants, you make it the clear answer to one specific question, in your store listing and in the places assistants read, and you measure whether it worked.

Start by seeing where you stand: the free AI visibility checker asks assistants the questions people ask about your niche and shows how often your app is named, and which apps are named instead.

Why this matters now

More people now ask an assistant “which app should I get” before they open a store search. Developers see it in their referrers: ChatGPT shows up as a traffic source in App Store Connect, and onboarding surveys that ask “how did you hear about us” get “an AI recommended it” as an answer. The store charts still matter, but a second front door has opened, and it has a different lock.

The difference from store search: a store ranks apps by its own signals and shows hundreds of results. An assistant gives a short list and a reason for each name. Being tenth in store search gets you some taps; not being in the assistant’s list of five gets you nothing from that conversation.

The three signals the recommended apps share

Run the checker on a few niches and the same pattern shows up in the apps that get named:

1. They say what they are, in the words people use. The phrase people ask for, “habit tracker”, “budget planner”, “sleep sounds”, sits in the app’s name or subtitle. Assistants lean on exact wording; an app called “Flow” that is, in fact, a habit tracker is harder to connect to the question than “Streaks – Habit Tracker”.
2. They carry weight. Tens of thousands of ratings, years in the store, regular updates. An assistant reads those as “established”, and it prefers to recommend things that many people already vouch for.
3. They are written about for that job. “Best habit tracker apps” articles, Reddit threads where people answer the question, review sites, the app’s own page that answers the question directly. The more places say “X is the best habit tracker”, the more the assistant repeats it.

Most apps that are not recommended miss the third signal, and many miss the first.

Seven steps to get recommended

1. Pick the one question you want to win

Not “productivity apps”. A question a person would type: “best habit tracker app for iPhone”, “best budget app for couples”, “best plant identifier app”. An assistant answers a specific question with a specific list; you want to be in the list for the question your users ask. Pick one, two at most.

Use the free keyword tool to see which apps rank for that phrase in the store, and the app niche finder to see how many apps compete there and how many make money. The question you choose should have both: people asking it, and room for you in the answer.

2. Put the phrase in your name and subtitle

The app’s store name and subtitle are the first thing an assistant reads about it. “Streaks” alone tells it nothing; “Streaks – Habit Tracker” answers the question. Keep the brand, add the job. On Google Play the same goes for the title and short description.

This is also plain ASO, and it is the one step that helps store search and assistants at the same time. The App Store optimization guide covers the rest of the listing.

3. Make your description answer the question in the first lines

Assistants read the description the way a person skims it: the first two or three lines. Those lines should say who the app is for and what question it answers, in plain words: “A habit tracker for people who gave up on complicated ones. Add a habit, tap once a day, see your streak.” Features can wait until the fourth line.

4. Build the ratings mass

Ratings are the most visible trust signal an assistant has. Ask for a rating after a moment the user is happy about, not on first launch: the third completed habit, the first exported report, the week-long streak. Reply to reviews; assistants read those too, and a developer who answers reads as alive.

5. Get named in the lists people already cite

This is the step that moves recommendations most. Find the articles and threads that answer your question today: search “best [your phrase] apps” and read the first two pages; search Reddit for the same question. Then:

• Write to the authors of the lists with a short, specific note: what your app does that the listed apps don’t, and a link. Many lists are updated yearly and welcome a new entry that fits.
• Answer the question yourself where it is asked, honestly: on Reddit, in forums, in Q&A sites, naming your app as one option among the others. Assistants read those threads.
• Give reviewers a reason: a free code, a short walkthrough, a data point about your users they can quote.

Count the mentions. Five independent pages that call your app a good habit tracker beat fifty tweets.

6. Publish the answer on your own site

A page on your site titled with the question, “Best habit tracker app for iPhone: how [Your App] compares”, that answers it fairly, names the alternatives and explains where yours fits. Assistants with web access read these directly; the ones without them read the pages that cite them. Keep it plain, keep it current, link to the store listing.

7. Measure, then repeat for the next question

Run the AI visibility checker on your app today and note the share of answers that name it. Do steps 2 to 6, then check again in a month. Answers from assistants with live web access move within weeks; answers from a model’s training move when the model is updated, so part of the gain arrives late. When the first question is won, pick the next one.

What the checker shows you

The checker asks assistants the questions people ask about your niche, five per phrase and more than once each, because assistants do not answer the same question the same way twice. You get:

• Your score: the share of all answers that named your app, and where in the list you landed.
• Who gets recommended instead: the apps named most often, matched to GetAppNiche’s catalog so each comes with its store page and, in Pro, its monthly revenue and downloads.
• What to fix first: which of the three signals you are missing, from the numbers.

The same page carries a monthly study of which apps assistants recommend in 25 App Store and Google Play categories, next to the app that earns most for that question by GetAppNiche data. In many categories the app that earns most and the app assistants recommend most are not the same app, which is the gap this guide is about.

What not to do

• Don’t stuff “AI” or “ChatGPT” into your listing. Assistants recommend apps for the job they do, not for mentioning assistants. The exception is an app that is actually about AI.
• Don’t buy mentions. A burst of paid posts on sites nobody reads does not change what assistants say; a handful of real reviews and threads does.
• Don’t optimize for one assistant. The signals above are the same for all of them. The ranking inside each list differs, the set of apps rarely does.

Where this fits with the rest of your growth

Store search, charts and assistants all read the same listing and the same reputation. The App Store optimization guide covers the listing; app keyword research covers the phrases; this guide covers the part of your reputation that lives off the store. Do them in that order, and check your AI visibility once a month. GetAppNiche Pro tracks it for you every week, with your own questions, and shows what every recommended app makes, at $49/month or $249/year; see pricing.