The playbook
How to rank in AI search
Short answer
To rank in AI search, get mentioned and cited inside AI-generated answers: lead pages with a direct answer, earn citations from sources models trust (Reddit, G2, Wikipedia, roundups), strengthen your entity with schema, build comparison pages, and make sure AI crawlers can retrieve you. Then measure your Share of Answer and fix the gaps.
The six principles of AI visibility
Lead with the answer
Put a crisp, self-contained 40–60 word answer at the top of every page. LLMs lift these verbatim into their responses.
Earn trusted citations
AI weights third-party sources — Reddit, G2, Wikipedia, reputable roundups — often more than your own domain. Get mentioned where models look.
Strengthen your entity
Consistent name, description and schema markup across the web help models associate buyer queries with your brand.
Build comparison pages
“X vs Y” and “alternatives” pages are surfaced constantly for high-intent buyer prompts. Own them.
Be retrievable
If AI crawlers can't access and cleanly parse your pages, no amount of quality gets you cited. Retrievability is the prerequisite.
Measure and iterate
Model outputs drift. Track your Share of Answer over time and fix the specific prompts where you're invisible.
Rank in a specific engine
Each AI engine sources answers differently. Pick yours for a tailored playbook.
OpenAIHow to rank in ChatGPTChatGPT answers from a blend of parametric knowledge (its training data, where your brand either exists as a known entity or doesn't) and live retrieval via its search tool for recent or specific queries. When it browses, it surfaces cited links; when it answers from memory, it names brands it has seen discussed repeatedly across the open web. It disproportionately trusts community and third-party sources over any single brand's own site.Read →Perplexity AIHow to rank in PerplexityPerplexity runs a live web search on every query, reads the top results, and generates an answer with numbered inline citations — it rarely answers from parametric memory alone. Its source selection tracks closely with what ranks and what its crawler can access, favoring pages that directly and completely answer the question. Because it always cites, being retrieved is a prerequisite for visibility.Read →GoogleHow to rank in Google AI OverviewsAI Overviews are grounded in Google's live search index — the model summarizes and synthesizes pages that already rank well for the query, then links to a handful as sources. Selection is tightly coupled to organic ranking, snippet-eligibility, and E-E-A-T signals rather than a separate corpus. Pages that already win featured snippets are frequently pulled into Overviews.Read →GoogleHow to rank in Google GeminiGemini answers from its own model knowledge and, when grounding is enabled, from live Google Search results with linked sources. It draws on Google's entity understanding, so brands recognized in the Knowledge Graph and ranking in Search are more likely to be named and cited. Grounded responses behave much like AI Overviews; ungrounded ones reflect training-data familiarity.Read →MicrosoftHow to rank in Microsoft CopilotCopilot runs on OpenAI models grounded by Bing's live search index, generating answers with inline citations to Bing-surfaced pages. Its source pool is Bing's ranking results, so Bing SEO — often neglected in favor of Google — directly drives Copilot visibility. It favors authoritative, well-structured pages that answer the query cleanly.Read →AnthropicHow to rank in ClaudeClaude relies heavily on parametric knowledge from training — it names brands it has seen described consistently and authoritatively across the web — and can also retrieve and cite live sources when its web search tool is enabled. Without search, familiarity and reputation in training data drive whether you appear. It favors factual, well-sourced, unambiguous information.Read →GoogleHow to rank in Google AI ModeAI Mode uses a query fan-out technique: it breaks your question into multiple related searches, runs them across Google's index in parallel, and synthesizes a conversational answer with links. Because it pulls from many sub-queries, visibility depends on covering an entire topic cluster well, not ranking for a single term. It leans on the same index, E-E-A-T, and entity signals as core Search.Read →DeepSeekHow to rank in DeepSeekDeepSeek's models (V3/R1) answer primarily from training data, naming brands that appear frequently and consistently across the web corpora they were trained on; a web-search mode adds live retrieval when enabled. Absent search, familiarity and reputation in training data determine whether you're mentioned. It favors clear, factual, well-corroborated information.Read →xAIHow to rank in GrokGrok blends parametric knowledge with real-time retrieval from X posts and live web search, citing sources when it browses. Its distinctive edge is direct access to the X firehose, so current discussion and sentiment on the platform influence what it surfaces. For fast-moving or opinion queries it leans on X; for factual ones it draws on training data and web results.Read →MetaHow to rank in Meta AIMeta AI is powered by Meta's Llama models and grounds answers with live web search (partnered with Google and Bing), citing sources for many queries. It also draws on Llama's training knowledge for well-known entities. Its enormous reach comes from being embedded across Meta's messaging and social apps rather than a standalone destination.Read →AmazonHow to rank in Amazon RufusRufus answers shopping questions using Amazon's own data — product catalog, listing details, customer reviews, ratings, and community Q&A — not general web search. It synthesizes across listings to recommend products and compare options within Amazon. Visibility is therefore an e-commerce listing and reviews problem, not a website-SEO one.Read →
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