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AI SEO

AI SEO is the practice of optimizing a brand’s presence across both traditional search engines and AI-powered retrieval systems — including large language models, answer engines, and social search surfaces. It combines three disciplines:
  • Traditional SEO — technical site health, crawlability, backlink authority, and keyword targeting for Google and Bing
  • AEO (Answer Engine Optimization) — structuring content so AI-powered answer systems can extract, cite, and surface it accurately
  • GEO (Generative Engine Optimization) — building the entity authority and citation infrastructure that causes AI platforms to retrieve and recommend your brand
The term exists because traditional SEO alone no longer covers the full retrieval landscape. When someone asks ChatGPT, Perplexity, Claude, or Gemini a question, the ranking signals that determine Google placement are largely irrelevant. Different signals govern AI retrieval — and AI SEO addresses both.

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Why It Matters Now

Search behavior is fragmenting. A growing share of queries that would have gone to Google are now being answered directly by AI platforms, without a click. If your brand isn’t being retrieved and cited by those systems, you’re invisible to that portion of your market. The challenge is that most brands don’t know they have a retrieval gap. Traffic reports don’t show zero-click AI answers. Analytics don’t capture Perplexity queries. The absence isn’t visible in the data you’re already looking at.

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How It Differs from Traditional SEO

Traditional SEO optimizes for ranking position in a list of links. AI SEO optimizes for retrieval — being the answer, the cited source, or the recommended brand inside a generated response. The underlying work has overlap: quality content, structured data, and authoritative citations matter in both contexts. But AI SEO adds entity optimization, citation architecture, and retrieval-layer targeting that traditional SEO doesn’t address.