What Is The Best Way To Do Seo For Ai Search

• Traditional SEO tries to rank a page as a link in a results list.
• Four surfaces account for most AI-sourced attention: Google AI Overviews, ChatGPT, Perplexity, and Gemini.
• SEO foundations are the crawlability, indexation, and performance conditions that determine whether an AI engine can retrieve your page at all.
• Quotable content answers first and stays self-contained.
• Schema markup is often treated as a checkbox that guarantees citation.

Traditional SEO tries to rank a page as a link in a results list. AI search optimization tries to get a passage quoted inside a generated answer, where the user may never see a link at all. According to Semrush (cited via the 2025 study in AI Rankings School), the category is also known as generative engine optimization (GEO) or answer engine optimization (AEO). The practical difference is what wins. Rankings reward position; citations reward a passage that can be lifted cleanly, carries verifiable facts, and comes from a source the engine trusts. AI SEO also has two sides: using AI tools to speed up production, and earning visibility inside AI-generated answers. This guide focuses on the second. Fastlook's Citation Analytics and Brand Memory exist for this measurement gap, since a ranking report cannot show whether ChatGPT or Perplexity actually named your brand.

Traditional fundamentals still feed AI visibility, so treat AEO as an additional layer, not a replacement.

Four surfaces account for most AI-sourced attention: Google AI Overviews, ChatGPT, Perplexity, and Gemini. They do not reward the same things. According to Marketing Aid, Perplexity cited sources more often than other platforms in their analysis of 500 queries and included images, video, and text, so unique charts, diagrams, and a YouTube presence are worth building there. The same author reports Perplexity delivered the highest volume and best conversion rates among AI sources they measured, followed by ChatGPT, while Gemini sent small traffic without conversions. Google AI Overviews sit on top of the existing index, so the SEO foundations below carry more weight there. ChatGPT handles over 37.5 million search-like prompts daily, according to LLMrefs, which makes it the widest reach. Plan per platform rather than writing one generic version for all four.

Platform selection weights are not publicly documented by the engines themselves; these are observed patterns.

SEO foundations are the crawlability, indexation, and performance conditions that determine whether an AI engine can retrieve your page at all. According to AI Rankings School, crawlable pages, Core Web Vitals (LCP, CLS, INP), internal linking, and domain authority all still feed AI visibility. Many teams skip this layer because it feels outdated, yet it is the layer that gets a page retrieved in the first place. A page that a crawler cannot reach, or that loads slowly and shifts layout, is unlikely to be the page an AI engine quotes. The build order should therefore be sequential. First, make pages crawlable and indexed. Second, fix Core Web Vitals. Third, tighten internal links so authority flows to answer pages. Fourth, earn the references that signal trust. Only after those foundations hold does answer structure matter. Writing perfectly quotable passages on a page no engine can index is wasted effort, and this sequencing is the part most AEO checklists leave out.

Quotable content answers first and stays self-contained. Place the direct answer in the opening sentence of each section, then support it with a specific fact. Use a short list only where a real set of steps or options exists.

• Lead with the direct answer.
• Support it with a named, verifiable fact.
• Repeat the entity name instead of relying on pronouns.

For example, a section about answer engine optimization should name answer engine optimization in every sentence that needs it, so the passage still makes sense when an engine lifts it alone. According to OWDT, answer engine optimization emphasizes precise, structured, user-focused information that AI can use directly, and OWDT cites E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) as important for AI-generated results. Marketing Aid reported that adding AI-citation-oriented optimizations raised its Perplexity referral traffic by 67% and doubled newsletter signups from that source. This is one author's reported result, but it suggests that structure can move outcomes.

Schema markup is often treated as a checkbox that guarantees citation. It does not. According to Dagmar Marketing, FAQ, How-To, Product, and Organization schema should be added so AI systems receive a map of content types rather than inferring them. That map helps an engine classify a page correctly, but it does not substitute for a passage worth quoting. A FAQ block with thin answers carries the markup and still loses. Treat schema as a labeling layer over strong content, and keep the markup in line with the visible page, since mismatches undermine trust. Dagmar Marketing also recommends creating an llms.txt file to point AI systems at a site's most valuable content, which pairs naturally with schema because both tell a machine where the substance lives.

Measuring visibility inside AI-generated answers means tracking whether engines name your brand, link to your pages, and send converting visitors. These figures show direction, but they do not reveal whether your brand is cited for the queries that matter. Measure at three levels:

• Whether the engine names your brand for a target prompt.
• Whether the engine links to your page.
• Whether the referred visitor converts.

Fastlook's Citation Analytics tracks where a brand appears in AI answers across major engines. Fastlook's Lead Capture scores AI-sourced intent, so you can see the conversion step rather than only the impression.

A workable build order runs in four stages, and the order matters because each stage depends on the one before it. First, fix crawlability, indexation, and Core Web Vitals so pages are reachable. Second, rewrite priority pages so each page answers the buyer's question in its opening sentence, with specific sourced facts. Third, add schema and an llms.txt file that match the visible content on those pages. Fourth, measure citations per platform and iterate on the pages that are retrieved but not quoted. None of these stages guarantees a citation, and rankings and citations can move independently, so track both. The honest limit: engines change how they select sources, and no vendor can promise a fixed outcome, so the durable advantage is a site that is fast, well structured, and genuinely authoritative on the questions its buyers ask.

Related guides

• GenAI Search Visibility for SEO Teams: AEO Strategy Guide
• SEO for AI Search Results Ranking: AEO Strategy Guide
• SEO for AI-Driven Search Platforms: Strategy & Tools
• AI Search vs Google SEO: Key Differences & Strategy Shifts
• Gemini vs Other AI Search Engines SEO: 2024 Comparison