Hey PH Community !
We've been heads down building. Four new things in the works. I want to know which one matters most to you.

RASE v1.0 App Store Intelligence

Tracks how your mobile app appears in AI answers (ChatGPT, Perplexity) and in store search. If you build apps, this tells you where you're visible and where you're invisible.

You're building a product.
Your focus is code, features, user experience.
Not meta descriptions.
Not FAQ schema.
Not internal linking.

But content still needs to get done. Docs, landing pages, blog posts, metadata. And if you ignore it, nobody finds your product.

So you have a choice. Spend hours on content yourself. Hire someone who doesn't understand your product. Or let an OS handle it.

We're building ROSE ( Rankfender Fullstack Optimization Engine ) as a Git based library. An SDK you install directly into your repo. It runs on every commit. Checks your metadata. Validates your heading structure. Suggests internal links. Even auto fixes the small stuff.

Yesterday I showed you how to audit your AI visibility. Today I'm going to show you exactly what to do with those findings.

After analyzing 50,000+ AI answers at Rankfender, we've identified clear patterns. Certain content types get cited 3x more often than others.

Here are the 7 content types that win AI citations with real examples you can steal.

First, the data:

Most content never gets cited by AI. I know because we tracked 14,000+ pages across 200+ domains.

But here's what's interesting: a small subset of pages consistently win 80% of all citations. And they follow a pattern.

We reverse-engineered that pattern. Then we applied it to our own content.

The result? Our citations tripled in 90 days.

Hey Product Hunt,

I'm Imed, founder of Rankfender.

We help brands track and improve how they appear in AI answers across ChatGPT, Perplexity, Gemini, and more. In 120+ languages.

We've now tracked over 3,000 brands, analyzed 75,000+ AI answers, and helped founders recover millions in lost revenue from AI errors.

For 20 years, SEO was a human game.
You wrote for people, optimized for Google's crawlers, and built backlinks by convincing other humans to link to you.
The inputs were human. The outputs were human.

GEO is different. You're optimizing for language models that extract and synthesize. The inputs are structured data, schema markup, comparison tables. The outputs are citations, not clicks.

So where does the human fit now?

What the data says about AI's performance:

You spent thousands translating your site.

French. German. Spanish. Japanese. Maybe more.

Here's the painful truth: Your site is translated. Your AI visibility isn't.

When a German user asks ChatGPT in German about your category, you're invisible. When a French prospect searches Perplexity in French, your competitor shows up.

We built a model to generate 1,000 questions that people actually ask.
Not random prompts.
We scraped 50,000 real user queries from search logs, forum threads, and support tickets across 12 industries.
We clustered them by intent and generated 1,000 representative questions.

We asked those same 1,000 questions to 5 AI models: ChatGPT (GPT-4), Gemini (Ultra), Perplexity (Pro), Claude (4.5 Sonnet), and Llama (3).
We ran the experiment daily for 30 days. We tracked every citation at the source level.

The goal: measure citation overlap.
How often do these models cite the same source for the same question?

The dataset:

Hi all, I ve been working on a small iPhone game called Petopia: Merge & Collect and wanted to share it here.

It s a casual merge game about combining and collecting cute pets. I m still improving it, so I d really appreciate any honest feedback on the gameplay, UI, progression, or anything that feels off.

App Store: https://apps.apple.com/us/app/pe...