Will ChatGPT Recommend Your App? A Checklist

If someone asks ChatGPT, Perplexity, or Claude for an app recommendation in your category, does your app have a realistic shot at being mentioned? This checklist covers what's actually checkable and fixable — not vague "be more visible" advice.

We built this into a free tool — the AI Visibility Checker — but you can walk through it manually with your own listing in about ten minutes.

1. Listing quality — does the AI have enough to work with?

• At least 5 screenshots. Visual assets are referenced by review sites and comparison articles AI systems draw on. A listing with zero or one screenshot signals an unfinished or abandoned app.
• Description is substantial — aim for 1,500+ characters. A two-sentence description gives a model nothing to summarize or quote.
• Updated in the last 90 days. Freshness is a citation-priority signal. An app that hasn't shipped in 8 months reads as abandoned, to humans and models alike.
• Category is set correctly. Obvious, but frequently wrong on listings that started as one thing and pivoted.
• Release notes are descriptive, not just "bug fixes."

2. AI answerability — is the description written to be quoted?

• No excessive keyword repetition. If one word appears in more than roughly 8% of your description's words, it reads as stuffing — and models weight that as a low-quality signal, the same way spam filters do.
• The description opens with what the app does, not a greeting. "Welcome to [App]!" is a wasted first sentence. "A habit tracker built for ADHD brains" is a sentence a model can lift directly into an answer.
• Features are listed, not buried in prose. A short bulleted or numbered list is dramatically easier for a model to extract accurately than a wall of adjectives.
• No unverifiable claims stated as fact. "#1 app in the world" without a citation is the kind of claim well-tuned models are trained to be skeptical of repeating.

3. Reputation — the trust signal models can't easily fake-check

• Rating above 4.2. Below that, a model has more reason to hedge or skip the recommendation.
• Meaningful review volume — hundreds, ideally thousands. Low review count reads as "not established" regardless of rating.
• Content rating is set. A missing content/age rating is a small but real red flag for family/education-category recommendations specifically.

4. Web presence — your footprint beyond the store listing

• You have a developer website at all. An app that exists only inside the App Store, with zero presence anywhere else on the web, gives a model almost nothing to cross-reference.
• robots.txt doesn't block AI crawlers. Check for GPTBot, ClaudeBot, PerplexityBot, and Google-Extended specifically — many sites accidentally block all bots.
• You have an llms.txt. See our guide to llms.txt for app developers if you don't have one yet — it's a markdown file, not an engineering project.
• The homepage has structured data (Organization/SoftwareApplication JSON-LD). This is one of the clearest, most machine-readable ways to say "this is a real company, here's what we make."

5. Cross-platform consistency

• Same app name on both stores, if you ship on both. Inconsistent naming makes it harder for a model to confirm two listings are the same app when cross-referencing.

What to do with a failing checklist

Prioritize in this order: crawler access (a robots.txt block is a hard zero, fix it first), then description structure (cheap, immediate), then reputation (slow to move, but compounds), then web presence (highest effort, highest ceiling — a real developer site with its own GEO-optimized content is the single biggest lever most indie apps are leaving unused).

Run the free checker against your own listing to see exactly which of these you're passing today.