How to Get Your Brand Cited by Claude: The Anthropic AI Search Playbook

Most AEO strategies published in 2024 assume AI engines all pull from the same sources. They optimize for Bing indexation because ChatGPT and Copilot use Bing. They work for those engines. They miss Claude entirely.

Claude uses Brave Search for live queries. Brave operates its own independent index, crawled by its own bot, with its own submission interface. A site thoroughly indexed by Bing can return almost no results in a Brave site search. Brands running comprehensive Bing-focused AEO campaigns and wondering why their Claude citation rate stays at zero are missing this technical gap.

This is the Claude-specific playbook. The ChatGPT citation playbook covers Bing-dependent engines. This covers what is different for Claude.

How Claude retrieves information

Claude operates two distinct citation pathways:

Live Brave Search (when search is enabled): For queries that benefit from current information — vendor comparisons, pricing, current tools, recent updates — Claude runs a Brave Search query and cites the most relevant indexed pages. If your site is not indexed by Brave, this pathway is entirely closed.

Training corpus (for knowledge questions): For questions with stable answers — “what is topical authority,” “how does FAQPage schema work,” “what is the difference between AEO and SEO” — Claude draws on its training data. Content that met Claude’s quality signals during previous training runs can produce citations without a live search step.

The practical implication: brands that want consistent Claude citations across both query types need to address both pathways. Brave indexation for live queries. Content quality and structure for training corpus citations.

Step 1: Verify and fix Brave indexation

Open search.brave.com and run site:yourdomain.com. Compare the result count to your actual published page count. For most sites, the discrepancy is significant — some return under 20% of their actual pages.

To submit your sitemap: search.brave.com/webmaster. Add your domain, verify ownership (same process as Google Search Console), and submit your XML sitemap URL.

Brave does not have a URL-by-URL submission tool as of 2025. Sitemap submission and natural crawling are the primary mechanisms. This means Brave indexation builds more slowly than Bing, and is another reason to address it early rather than retroactively.

Allow BraveBot explicitly in robots.txt alongside the other AI crawlers:

User-agent: BraveBot
Allow: /

User-agent: ClaudeBot
Allow: /

User-agent: GPTBot
Allow: /

User-agent: PerplexityBot
Allow: /

User-agent: Bingbot
Allow: /

Note the distinction between BraveBot (Brave’s indexing crawler) and ClaudeBot (Anthropic’s training crawler). Both need to be allowed. Many sites block all non-major-search-engine bots by default through security plugins or Cloudflare settings. Use the robots.txt checker to confirm which bots your site is currently allowing.

Step 2: FAQPage schema — Claude’s highest-extractability format

Of all schema types, FAQPage produces the most direct citations in Claude responses. The acceptedAnswer text is structured, clearly attributed to the source URL, and extractable without the surrounding prose context that live search results carry.

{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "mainEntity": [
    {
      "@type": "Question",
      "name": "What is AEO and how does it differ from SEO?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "AEO, Answer Engine Optimization, is the practice of structuring content so AI engines cite your brand in generated answers. SEO targets keyword rankings in traditional search results. The technical foundation overlaps but the content structure requirements differ: AEO requires answer-first formatting, FAQPage schema, and entity definition. SEO requires topical authority and technical crawlability. The most effective approach builds one system that satisfies both."
      }
    }
  ]
}

The accepted answer text should be 40 to 100 words, contain a direct factual answer, include at least one specific data point, and avoid any marketing language. Claude’s extraction is precise: vague answers produce low-confidence citations with hedged language; specific answers produce direct citations with source attribution.

Every service page and high-traffic content page should have 3 to 5 FAQ schema entries targeting the specific questions your buyers ask. The broader schema markup for AI search guide covers the full JSON-LD stack.

Step 3: Entity signals Claude uses

Claude’s training corpus includes entity knowledge: what a brand is, what it does, what category it operates in. Brands with weak entity signals either do not appear in Claude responses or appear with uncertain language (“I believe” rather than a direct statement).

Entity clarity for Claude requires:

Organization schema with a precise description field:

{
  "@context": "https://schema.org",
  "@type": "Organization",
  "@id": "https://yoursite.com/#org",
  "name": "Your Brand",
  "url": "https://yoursite.com",
  "description": "One sentence: product category, target buyer, specific outcome.",
  "foundingDate": "2022",
  "sameAs": [
    "https://linkedin.com/company/yourbrand",
    "https://github.com/yourbrand"
  ]
}

The description field is pulled directly into entity representations in AI training data. It needs to be precise enough to distinguish your brand from every other brand in your category. “We help SaaS companies grow” is not a distinguishing description. “Customer success automation for B2B SaaS companies with 50 to 500 seats” is.

Consistent naming across surfaces. If your company name in the schema is “Acme AI,” your LinkedIn is “Acme AI Inc.,” and your GitHub is “acme-ai-hq,” Claude’s entity model may treat these as separate entities or have low confidence in the connection. Consistent naming across your schema, LinkedIn, GitHub, and any press mentions strengthens entity coherence.

Step 4: Content structure that Claude extracts reliably

Claude users in professional settings write longer, more specific queries than typical search users. Common patterns from enterprise and developer contexts:

• “Explain the difference between [X] and [Y] for [specific use case]”
• “What are the most important factors when evaluating [vendor category] for [company type]”
• “How does [approach] compare to [alternative approach] in terms of [specific metric]”

Content targeting these patterns performs better in Claude citations than content targeting generic short-tail queries. Specificity is the signal. A guide titled “AEO for Legal Tech: Getting Cited by AI Tools Used by Corporate Counsel” will be cited for more Claude queries than a guide titled “Introduction to AEO.”

The structural requirements for content Claude extracts predictably:

Answer in sentence one. The direct response to the question the page targets must appear before any context, qualification, or background. Claude pulls from the beginning of content blocks when constructing citations.

Specific data over general claims. “Our median client sees first citations within 60 days” is citable. “Our clients see great results” is not. Every factual claim should have a number attached where possible.

Question-match headings. H2 and H3 headings written as questions or matching question phrasing give Claude precise extraction anchors. “How long does AEO take?” as an H2 will be extracted for Claude queries asking that exact question. “Our Process” will not.

This content structure overlaps exactly with what produces topical authority for traditional search. The same cluster of specific, expert-level posts that earns Google citations earns Claude citations when the Brave indexation layer is in place.

Step 5: Comparing Claude to other engines

Signal	Claude	ChatGPT	Copilot	Perplexity
Live search index	Brave Search	Bing	Bing	Bing + multiple
Training corpus weight	High	High	Moderate	Low
Bing indexation required	No	Yes	Yes	Yes
Brave indexation required	Yes	No	No	No
FAQPage schema value	Very high	High	High	High
LinkedIn signals	Moderate	Low	High	Low

The Microsoft Copilot playbook covers the Bing-specific optimization path for Copilot, which is the enterprise AI engine most different from Claude in its technical requirements.

Measuring Claude citation performance

No native analytics exist for Claude citations. Three indirect signals are worth tracking:

Monthly manual sampling. Ask Claude your 20 most important buyer queries, with and without the search tool enabled. Document which responses cite your domain and what text is extracted. Repeat monthly. Changes in citation frequency and the queries you appear for reveal whether the Brave indexation and content structure work is producing results.

Brave Search Console impressions. Brave Webmaster Tools shows impressions and clicks for your domain across Brave queries. Rising impressions for target queries correlate with improving Claude live-search citation probability.

Branded Google search growth. Buyers who encounter your brand in Claude responses frequently search your brand name on Google as a follow-up. Unexplained growth in branded impressions in Google Search Console is one of the more reliable secondary indicators of increasing AI citation frequency across engines.

Run the AI Visibility Score to benchmark your current citation status across Claude, ChatGPT, Perplexity, and Google AI Overviews before building a channel-specific optimization roadmap.

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Related reading

• ChatGPT citations — Bing-based engines: ChatGPT and Perplexity
• Microsoft Copilot optimization — Bing indexation, LinkedIn signals, enterprise citation mechanics
• schema markup for AI search — complete JSON-LD implementation by page type
• AEO strategy — multi-engine implementation including Claude, ChatGPT, Perplexity, and Copilot

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Need a citation gap analysis across Claude and the other major AI engines? Start with an audit and get a channel-by-channel breakdown of where your brand currently appears and what is blocking the gaps.