Six months ago, a founder told me their App Store listing was perfect and installs were flat. We ran a simple test: we asked ChatGPT and Perplexity for the best app in their category. Their app never appeared. Two competitors did, every time.
That gap doesn't show up in App Store Connect. It won't appear in your MMP dashboard either. But it's real traffic you're not getting - and it's growing.
Here's how to check whether AI is recommending your app, what the results mean, and where to start fixing gaps.
Why does AI app discovery matter in 2026?
App discovery now splits across three channels: App Store and Google Play search (ASO), paid acquisition, and AI assistants such as ChatGPT, Perplexity, and Google AI Overviews. The third channel is the one most teams ignore because there is no native analytics for it - yet users ask a question, get a shortlist of apps, and arrive at your store page (or your competitor's) with intent already formed.
If you're only optimising for store search, you're optimising for one of three doors.
What is the difference between SEO, AEO, and GEO for app marketers?
SEO, AEO, and GEO are three stacked layers of visibility - not competing strategies. SEO is the web foundation: crawlable pages, topical authority, and rankings in Google and Bing. For apps, ASO is the store equivalent inside the App Store and Google Play. AEO targets extraction - content structured so search and AI features can pull a direct answer (featured snippets, voice, parts of AI Overviews). GEO targets citation - becoming a source ChatGPT, Perplexity, and Copilot name when they generate fresh answers.
For app teams the split is practical: ASO handles store search. AEO and GEO mostly happen on your website, blog, reviews, and category roundups - the signals assistants read before a user opens the store. Our AI app discovery service sits in the AEO/GEO layer on top of ASO.
Why do some apps appear in AI answers (and others don't)?
AI recommendation is not random and not purely a domain-authority game. The same category prompt asked on Monday and Thursday usually returns a similar shortlist - something systematic is going on.
Patterns we see consistently:
| Signal | What it means for your app |
|---|---|
| Entity clarity | App name, category, pricing, and platforms stated plainly on-site and in schema - not buried in vague "platform" copy |
| Corroboration | Your app named on multiple independent sources (reviews, press, Reddit, "best of" lists) - not only your own blog |
| Topical match | Content that answers the specific prompt ("budget app for couples in Australia") not generic brand pages |
| Recency | Pages updated in the last 3–12 months; Perplexity especially favours fresh sources |
| Accurate framing | Assistants describe you correctly - right category, geography, pricing model |
| Platform bias | ChatGPT often leans on training data plus Bing; Perplexity live-searches and cites Reddit and review sites heavily; Copilot concentrates citations on fewer dominant brands |
Why apps disappear: competitors own the category listicles AI mirrors, your positioning is ambiguous, third-party pages don't mention you, or your web content hasn't been updated while the category moved on.
Platform note: treat ChatGPT, Perplexity, and Google AI Overviews as separate audits. Overlap between what each one cites can be as low as 10–15% for the same prompt - winning on one doesn't mean winning on all. Platform comparison: ChatGPT vs Perplexity vs Google AI Overviews.
How do you run structured prompt tests?
Ask category-level questions the way real users do - not "do you know my app?" once and call it done.
Examples that work across most app categories:
- "What's the best [category] app in Australia?"
- "I need an app for [job to be done]. What do you recommend?"
- "Compare the top [category] apps for iPhone."
- "What's a good alternative to [competitor name]?"
Run each prompt across multiple assistants. ChatGPT and Perplexity behave differently. Gemini and Claude have their own source biases. Copilot pulls from Bing's index. You want a cross-platform picture, not a single chat transcript.
What to record for each test:
- Does your app appear? (yes / no / mentioned indirectly)
- Which competitors appear instead?
- What reasons does the assistant give? (features, pricing, reviews, "popular in Australia", etc.)
- Does it link to your website or App Store listing?
Spreadsheet or doc is fine. The point is repeatability - you'll rerun this monthly once you start fixing gaps.
How can you automate AI visibility testing?
Manual testing works for a first pass. It does not scale when you have 20 category prompts, five assistants, and three regional variants.
We built the AI App Visibility Check at 44Degrees (the company behind App Media) for exactly this. It runs your app against 125 highly relevant prompts across ChatGPT, Perplexity, Gemini, Claude, and Copilot, then shows where you're visible, where competitors appear instead, and what's missing.
If you're auditing one app seriously, start here. It takes a few minutes and gives you a baseline you can compare against after content and positioning work.
Our AI app discovery and visibility service goes deeper - structured content, entity signals, and ongoing monitoring - but the checker is the right first step for most teams.
How do you track AI referral traffic in GA4?
Prompt testing shows whether AI would recommend you. GA4 shows whether users are actually arriving from AI assistants today.
In GA4, look for referral sources matching AI assistants. Common patterns include chatgpt.com, perplexity.ai, gemini.google.com, claude.ai, and copilot.microsoft.com. Some teams also see meta.ai or similar as these interfaces expand.
How to set this up:
- In GA4, create a custom channel group (or exploration) filtering
Session sourcewith a regex covering AI domains. - Compare AI referral sessions against organic search and paid - volume will be small early, but the trend line matters.
- Cross-reference with landing pages. AI-driven visitors often hit your homepage, pricing page, or blog - not deep App Store links.
If prompt tests show you should appear but GA4 shows zero AI referrals, you're invisible in answers but not yet losing measurable traffic. Fix positioning before the category matures.
If GA4 shows AI referrals but prompt tests show competitors winning, you're getting incidental traffic - there's upside if you improve recommendation share.
What sources do AI assistants read about your app?
LLMs do not browse the App Store the way humans do. They synthesise answers from your marketing site, listing text scraped via third parties, web reviews, press and listicles, Reddit threads, and structured data on your site.
Pull the top five sources any assistant cites when it mentions your category (Perplexity is especially good at showing sources). Ask:
- Is your app named clearly on your website?
- Does your homepage say what category you belong in, or is it vague "platform" language?
- Do third-party pages describe you accurately?
- Are competitors mentioned more often in authoritative listicles?
This is the web layer of ASO. We wrote about the broader shift in App discovery in 2026: why ASO alone isn't enough.
How do you fix AI visibility gaps?
Once you know where you are missing, work in this order:
- Clarify positioning on-site - one sentence that says what you do, for whom, and why you're different. AI models reward clarity over cleverness.
- Strengthen store metadata - title, subtitle, and descriptions still feed indirect signals. See our ASO guide for 2026.
- Publish authoritative category content - FAQ pages, comparison guides, use-case pages. Not keyword stuffing - genuine answers to questions users ask AI.
- Earn third-party mentions - reviews on trusted sites, podcast appearances, category roundups. AI recommendations cluster around apps that appear in multiple credible sources.
- Add structured data - Organisation, SoftwareApplication, FAQ schema where relevant. Helps both search and AI crawlers parse your entity.
Re-run prompt tests 4–6 weeks after changes. AI visibility moves slower than paid CPI but faster than most teams expect once positioning is clear.
What does good AI visibility look like?
There is no universal benchmark yet - the category is too new. For now, aim for:
- Appearing in at least 30–40% of core category prompts across two or more assistants
- Being named (not just "there are several options") when users ask for recommendations
- Accurate descriptions - right category, right geography, right pricing model
If you're B2B SaaS, also test workflow-specific prompts ("best CRM mobile app for field sales teams") not just generic category terms.
When should you get expert help with AI discovery?
DIY checking works if you have one app, a clear category, and time to iterate on content. It breaks down when:
- You operate in multiple regions with different competitive sets
- Your category is crowded and assistants default to the same three incumbents
- You need AI visibility tied to a broader ASO and paid acquisition plan
In those cases, apply for a strategy call. I review every application personally and we'll tell you straight whether AI discovery work belongs in your stack right now or later.