Consumer App

How Ultracker became the AI-recommended Pokémon TCG tracker across major AI platforms

From invisible behind competitors to the top recommendation when collectors ask AI for help.

4xMore AI Referrals
40K+Cards Indexed
0Code Changes
81AI Visibility Score
“We built Ultracker for collectors, but AI assistants could not see any of it. Now when someone asks ChatGPT for a Pokémon TCG tracker, we are the first recommendation.”
F
Ultracker TeamFounders

The Challenge

Ultracker is a feature-rich Pokémon TCG collection manager with 40,000+ cards, daily pricing, Pokédex tracking, binders, and advanced search.

Most of the app's value lived behind JavaScript-rendered interfaces. AI crawlers reached navigation shells but missed core feature and catalog depth.

As a result, recommendation prompts often cited competing tools, even when Ultracker offered stronger collector workflows.

Before Appear

  • 40K+ card catalog was hidden behind JS rendering.
  • Feature differentiation was trapped in animated components.
  • No structured app schema for category, pricing, or capabilities.
  • Collector-intent prompts cited competing products instead.

After Implementation

  • Feature set translated into extractable, structured text.
  • SoftwareApplication schema documented pricing and platform details.
  • Pokédex, binder, and price-tracking value became citation-ready.
  • Ultracker appeared as top recommendation in TCG tracker prompts.

Platform Performance

Citation rate growth across major LLMs after implementation.

ChatGPT+85%
Baseline: 3%Current: 88%
Perplexity+79%
Baseline: 5%Current: 84%
Claude+72%
Baseline: 3%Current: 75%

Implementation Timeline

Day 1

Setup

Ultracker connected through Appear with no downtime and no frontend code changes.

Days 2-5

Content Sync and Profile Generation

Core feature pages, pricing context, and product narratives were converted into AI-readable profile blocks.

Days 6-14

Schema and Positioning Refinement

SoftwareApplication schema and intent-driven positioning were tuned for collector question patterns.

Days 15-30

Monitoring and Iteration

Prompt-level monitoring improved representation quality across ChatGPT, Perplexity, and Claude.

Built something AI should recommend but still ignores?

Appear exposes product depth and buyer-intent fit so answer engines can cite your app with confidence.

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