AI Search & SEO Research, Guides & Case Studies

AI search is reshaping how people find and consume information online. Large language models like ChatGPT, Claude, and Perplexity are increasingly used for research, recommendations, and answers that previously required traditional search engines. For businesses and content creators, this shift raises critical questions about visibility, citations, and how to optimize for a fundamentally different type of search.

My research in this space focuses on understanding how AI models use web content, which sources they cite, and what patterns correlate with visibility in AI-generated responses. This isn't speculation - it's data analysis examining actual citation patterns, content structures that get referenced, and the evolving relationship between traditional SEO signals and AI search visibility.

The AI search overlap with traditional SEO is significant but not complete. Topical authority, semantic clarity, and authoritative content matter in both contexts. But AI search introduces new considerations: how content is structured for extraction, whether information is presented in ways that LLMs can accurately parse and cite, and how entity relationships are established across a body of content.

I approach AI search optimization as an extension of semantic SEO principles. Clear entity definitions, comprehensive topical coverage, authoritative sourcing, and content structured for both human readers and machine understanding. The goal isn't to game AI systems but to create content that genuinely deserves citation - the same content quality signals that support traditional rankings. The websites I build are structured with these principles in mind, optimized for both Google and the AI search platforms that increasingly shape how users discover content.

Below you'll find my research, analysis, and strategic guides on AI search - examining real data on citation patterns, platform dynamics, and practical approaches to building visibility in this emerging channel.