Can ChatGPT or Claude Do Your ASO? We Checked an AI Keyword Strategy Against the Live App Store

No: a general AI agent cannot do your App Store keyword research on its own. ChatGPT, Claude, Gemini, Codex and Cursor do not search the App Store when you ask them for keywords. They write a confident table of search volumes and difficulty scores from what they absorbed in training, and Apple has never published App Store search volumes for them to absorb. We tested it on October 31, 2026: we asked two current Claude models for a keyword strategy, then measured every keyword they picked on the live US App Store with RespectASO. The "easy wins" they recommended were not easy, a keyword they warned against was one of the most reachable on the list, and the two models disagreed with each other on volumes by up to a factor of two.

The test: one prompt, two models, no tools

We gave Claude Sonnet 5.5 and Claude Opus 5.5 the same request, with every tool turned off so each answered the way a chatbot or coding agent does when nothing connects it to the App Store:

I'm launching a white noise and sleep sounds app on the US App Store. Give me my App Store keyword strategy: the 10 best keywords to target, and for each one your estimate of monthly US App Store search volume, keyword difficulty (1-100), and why. Then write my app name (30 characters max), subtitle (30 max) and 100-character keyword field.

Both answers looked professional: a ranked table, a reason for every keyword, a plan to "win the easy terms first, then climb", and metadata that fit Apple's character limits exactly. Then we ran all 14 keywords the two models chose through RespectASO's free edition, which reads that day's real App Store search results for each one and scores the apps actually ranking there.

What the AI said, and what the App Store showed

Difficulty runs from 1 to 100, and the measured column is RespectASO's score. The last two columns are plain facts from that day's US results: the median rating count of the top ten apps, and how many of those ten carry the keyword in their name. n/a means that model did not pick the keyword.

Keyword	Sonnet said	Opus said	Measured	Top 10 median ratings	Top 10 with it in the name
white noise	85	80	75	170,757	7
sleep sounds	75	78	78	113,862	7
sleep machine	55	n/a	68	123,436	1
sound machine	n/a	58	54	170,769	1
sleep aid	80	62	70	31,452	4
rain sounds	65	60	59	19,219	7
fan noise	n/a	40	58	13,956	8
fan sound	40	n/a	51	13,094	2
white noise machine	n/a	65	60	8,159	4
baby sleep sounds	n/a	60	49	3,830	4
pink noise	50	38	49	2,233	5
baby sleep	70	n/a	43	1,701	5
brown noise	60	55	51	1,416	4
ocean waves	45	n/a	37	1,175	2

Measured on October 31, 2026 in the US storefront with RespectASO 3.1.2, free edition. Search results change every day, so a run on another day will differ in the details.

On the head terms the models were roughly right, which is exactly why their answers feel trustworthy: everyone knows "white noise" is crowded. The damage is in the middle of the list, where an indie app actually decides its first months.

1. The "easy wins" were the trap

Opus put "fan noise" at difficulty 40 with the verdict "Steady demand, weak competition. Easy to rank for." It was one of the three keywords its plan started with. On the real App Store, eight of the top ten apps for "fan noise" carry it in their name, and the median one has 13,956 ratings. That is a keyword other developers fight for on purpose. Sonnet made the same call on "fan sound". A new app that follows that plan spends its first launch window, the weeks when ranking matters most, on keywords it cannot reach.

2. A reachable keyword was ruled out

Sonnet scored "baby sleep" at 70, among the four hardest on its list, and its plan was to win the easier terms first and climb toward the hard ones as ratings came in. Measured, it is 43, and the median app in its top ten has 1,701 ratings: one of the most reachable keywords in the whole list, with a clear audience behind it. The plan sent a new app to the back of the queue for the keyword it could win soonest.

3. The most valuable field went to the wrong word

Both models wrote a subtitle ending in "Machine", and Sonnet called "sleep machine" a term with "fewer strong competitors". Only one app in the top ten for "sleep machine" or "sound machine" has those words in its name, yet the median app ranking there has 123,436 and 170,769 ratings. Apple ranks giant apps for those searches anyway. A name-based guess cannot see that; only the actual results show it.

4. Search volumes with no source

Sonnet put "sleep sounds" at 30,000 searches a month; Opus said 40,000 to 60,000. "Brown noise" was 15,000 for one and 20,000 to 35,000 for the other. Same vendor, same question, same day. Neither number can be right in any checkable sense, because Apple does not publish App Store search volume anywhere. The only App Store popularity Apple gives out is relative: a 1 to 5 indicator in Apple Ads, "based on how many people are searching for it on the App Store" (Apple Ads), and its weekly top search terms through the Apple Ads Platform API (Apple Developer). A model that types "30,000" is generating a plausible number, not reporting one.

Why every general AI agent fails this the same way

This is not a Claude problem, and a different model would not fix it. It is what any general model does when it has no data source.

• No App Store connection. The models answer from training data. Anthropic lists a reliable knowledge cutoff of June 2026 for its current models (Anthropic); OpenAI lists April 30, 2026 for GPT-6 Astra (OpenAI); Google says Gemini 4's is March 2026 (Google DeepMind). The App Store you are launching into is today's.
• Web search does not close the gap. Codex, Claude, Gemini CLI and Cursor can search the web, in some setups only once it is switched on. Search finds articles about keywords, not measurements: no page lists App Store search volumes, and scoring the competition means reading the ratings of every app ranking for the keyword, in your storefront, on the day you decide.
• The vendors say so themselves. Anthropic: "Users should not rely on Claude as a singular source of truth" (Anthropic Help). Google: Gemini "can hallucinate and present inaccurate information as factual" (Google).
• SEO already ran this experiment. Ross Simmonds compared over 500 ChatGPT keyword volume estimates with Ahrefs and found gaps of up to 1,000 times: ChatGPT put "seo" at 6,600 monthly searches against Ahrefs' 191,000 (Ross Simmonds). SpyFu found ChatGPT estimating 5,000 to 10,000 monthly searches for a phrase that SpyFu and Ahrefs put at about 700 to 800 (SpyFu). And the web has far more public search data for a model to learn from than the App Store has ever had.

The stakes are high because search is how iOS apps get found: Apple reports that 70% of App Store visitors use search to discover apps, and that almost 65% of downloads happen directly after a search (Apple Ads). Your title, subtitle and keyword field are the 160 characters that decide which of those searches you appear in. Guessing them is an expensive shortcut.

What the AI got right, and how to use it

The models were genuinely good at the writing. Both produced an app name and subtitle of 30 characters or fewer and a keyword field within 100, with no spaces after commas and no word repeated from the name, exactly as Apple's product page guidance asks. Brainstorming, clustering ideas, drafting metadata and translating it into natural local phrasing are real strengths.

So the answer is not "stop using AI". It is: let the AI write, and let real App Store data decide. Every keyword the AI proposes needs three answers it cannot give you: how strong the apps ranking for it are today, how sought after it is, and what it is worth to your app at the rank you can actually reach.

Why RespectASO is the tool for that job

RespectASO answers those three questions for every keyword, from the live App Store, on your own Mac.

• Real results, every time. Each keyword is scored from the apps ranking for it that day in the storefront you choose, across all 175 App Store storefronts. The difficulty score reads their ratings, how fast they gain them, their stars, their age, and how many target the keyword in their name.
• Every number says where it came from. Popularity comes from RespectASO's estimate, calibrated against thousands of Apple's official values, or from Apple's own data through your Apple Ads account, and every value is labeled with its source. No invented "monthly searches".
• Scored for your app. The Opportunity score says what a keyword is worth to your app at the rank apps like yours actually land, so "baby sleep" shows up as the opening it is and "fan noise" as the fight it is.
• Free and open source. The free edition researches 3 keywords per search with no account, and the scoring code is public on GitHub for anyone to inspect.
• Private. Your research stays on your Mac. The free features send only your search terms, and only to Apple's App Store.

And if you love working with AI, RespectASO Pro gives you both halves in one place. The AI Niche Researcher and AI Competitor Analyzer use the model you choose (your own OpenAI, Anthropic, Google Gemini or OpenRouter key, or a local model on your Mac) to propose keywords and write metadata, and every keyword they propose is scored with real App Store difficulty, popularity and opportunity before you see it. With MCP, Claude, Cursor or any MCP client calls RespectASO's 32 tools directly, so your agent stops guessing and starts measuring. You can also paste up to 1,000 keywords at once, which is exactly what an AI brainstorm produces.

Check your own AI keyword list in five minutes

1. Download RespectASO and open the Keywords page.
2. Paste the keywords your AI assistant suggested, three at a time in the free edition, and pick your storefront.
3. Look at each keyword's difficulty and its top ten: how many ratings the leaders have and how many put the keyword in their name.
4. Drop the keywords whose leaders you cannot reach this year, and move the reachable ones into your title and subtitle.
5. Ask the AI to rewrite your metadata around the keywords that survived. Now its writing is working from facts.

AI made writing metadata fast. It did not make the App Store visible to a model that has never searched it. Before you ship the next keyword list an agent hands you, measure it. Download RespectASO, free.