From invisible behind competitors to the top recommendation when collectors ask AI for help.
“We built Ultracker for collectors, but AI assistants couldn't see any of it. Now when someone asks ChatGPT for a Pokémon TCG tracker, we're the first recommendation.”
Ultracker is a feature-rich Pokémon TCG collection manager with 40,000+ cards indexed, daily price updates, Pokédex tracking, digital binders, and advanced search. The app is a go-to tool for serious collectors — but AI assistants had no idea it existed.
The core problem: Ultracker's richest features — card search, collection views, Pokédex heatmaps, price charts — are all rendered client-side with JavaScript. When AI crawlers visited ultracker.app, they received a shell with navigation links and footer text, but none of the actual product content that makes Ultracker valuable.
Citation rate growth across major AI platforms after implementation.
Single CNAME change to route ultracker.app through Appear's edge. Zero downtime, zero code changes. Human visitors continued using the app normally.
Appear crawled the app and generated AI response profiles for the homepage, feature pages, Pokédex section, and pricing. All 8 core features were captured in structured, extractable format.
Added SoftwareApplication schema with category, pricing model, platform details, and feature list. Refined AI profiles to highlight what differentiates Ultracker: Pokédex mode, price tracking, and the 40K+ card catalog.
Tracked AI crawler visits and citation accuracy. Iteratively refined descriptions based on how AI platforms were representing Ultracker in answers. Achieved 81/100 AI visibility score.
Appear exposes the feature depth, product structure, and differentiation that modern AI systems need to cite with confidence.
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