AIFun — AI Visibility & Growth for Modern Websites

AIFun helps businesses get found on Google and recommended by AI search engines — ChatGPT, Perplexity, Google AI Overviews, Gemini, and Bing Copilot. We deliver done-for-you SEO, AEO (Answer Engine Optimization), GEO (Generative Engine Optimization), AI mentions building, local AI presence, AI SEO, ChatGPT visibility, white-label GEO for agencies, automation, and autoblogging.

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Frequently asked questions

What does AIFun do?

AIFun is a done-for-you AI visibility and growth provider. We make websites rank on Google and get cited by AI engines (ChatGPT, Perplexity, Gemini, Google AI Overviews, Bing Copilot) through SEO, AEO, GEO, AI mentions building, and content automation.

What is AEO (Answer Engine Optimization)?

AEO is the practice of structuring content — answer capsules, FAQ schema, clear entity definitions — so AI answer engines like ChatGPT, Perplexity, and Bing Copilot cite your site as a source.

What is GEO (Generative Engine Optimization)?

GEO is the off-site authority work — citations, brand mentions, and structured data on the third-party sources AI engines trust — that gets your brand recommended inside AI-generated answers.

How do I get cited by ChatGPT or recommended by AI search?

You need three things: an AI-ready site (clean SSR, schema, answer-formatted content), authoritative third-party mentions on sources AI engines crawl, and consistent brand signals across the web. AIFun handles all three.

Do you only work with Lovable websites?

No. AIFun works with any modern website — WordPress, Webflow, Framer, Shopify, custom React/Next, and Lovable. We have a dedicated practice for Lovable sites, but our core offer is AI visibility for any stack.

What is the difference between SEO and AEO?

SEO optimizes for Google's ranked list of blue links. AEO optimizes for AI engines that synthesize one answer and cite a handful of sources. Modern brands need both — AIFun delivers them together.

Last updated: 2026-06-27. Canonical URL: https://aifunn.com/llm.html