An AI discoverability audit is mostly a positioning audit. Can someone understand what your app does, who it is for, why it is credible, and when to choose it?
Do this audit like a skeptical buyer, not like the founder. If a stranger reads your App Store page, homepage, docs, and reviews, can they explain the app in one sentence without inventing missing context?
Check the category
Your App Store page and website should name the actual job: budget planner, cycling weather tracker, screenshot generator, ASO workspace, journaling app, or keyboard utility.
Category clarity is the foundation. If your title says one thing, screenshots imply another, and the homepage talks in broad outcomes, the app becomes harder to retrieve and harder to recommend. Pick the category language users actually search for and repeat it consistently.
Check the proof
Look for public evidence: screenshots, reviews, changelog, docs, pricing, comparisons, examples, and support pages.
Proof should answer the basic trust questions. Does the app exist? What does it look like? Who uses it? What changed recently? What does it cost? What happens after install? What are the limitations? Thin pages that avoid these questions leave the answer to competitors.
Check the alternatives
If users compare you with known tools, publish clear comparison content. If you avoid comparisons, AI answers may rely only on competitor positioning.
Check repeated language
Search engines and AI systems both benefit from consistency. Use the same core nouns across App Store metadata, screenshots, website headings, docs, and comparison pages. This does not mean keyword stuffing. It means not describing the same app five different ways.
Fix one weak surface first
Do not turn the audit into a giant rebrand. Choose the weakest public surface: homepage positioning, App Store screenshots, docs, pricing explanation, alternatives page, or review proof. Improve that, then check whether the app is easier to describe.