Last updated: September 28, 2026
AI indexing refers to the process by which AI systems crawl, retrieve, and process web content to use in generating responses. Unlike traditional search engine indexing, which organizes pages for ranked results, AI indexing feeds content into language models for training, retrieval-augmented generation, and real-time answer synthesis. Understanding which bots visit a site and why helps teams distinguish training collection from search discovery and user-triggered retrieval.
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How AI Indexing Differs from Search Engine Indexing
Traditional search engines like Google index pages to serve in ranked search results. AI indexing serves multiple distinct purposes:
- Training data collection: Crawlers like GPTBot and ClaudeBot collect content to train foundation models; their share of crawling varies by network, measurement window, and crawler classification
- Search retrieval: PerplexityBot supports Perplexity’s search index, while Perplexity-User can fetch pages in response to a user’s request
- User-action crawling: AI agents that browse the web on behalf of users, a category Cloudflare reported grew more than 15-fold in its 2025 network observations
Each type of AI indexing has different implications for how and when a brand’s content appears in AI responses.
Key AI Crawlers and Their Behavior
The AI crawler landscape is dominated by a few major players. Crawler shares depend on the network and measurement window. Cloudflare’s 2025 review measured Googlebot at an average 4.5% of HTML requests on its network, compared with 4.2% for AI bots; those figures do not describe every website or the whole web. Brands need to understand which crawlers are accessing their content and for what purpose.
Controlling AI Access to Content
Website owners can manage AI indexing through several mechanisms:
- robots.txt directives: Specify which AI crawlers can access the site using a robots.txt checker to verify configurations
- llms.txt files: An experimental proposal for providing AI systems with a structured summary of a site’s most important content, which can be generated using an llms.txt generator
- Provider-specific controls: Use each provider’s documented robots.txt token or opt-out mechanism. There is no universal meta tag that controls AI training across providers.
Diagnosing AI Indexing Gaps
Being crawlable by Googlebot does not automatically make a page accessible to other providers. Check provider-specific rules for OAI-SearchBot, Claude-SearchBot and other search agents, and manage training bots such as GPTBot and ClaudeBot separately. JavaScript support varies, so verify how important pages are fetched instead of assuming every crawler sees or misses client-rendered content.
Make important content available in the initial HTML or use server-side rendering consistently for users and crawlers. Also check whether the CDN or WAF blocks verified AI requests. A difference between Perplexity and ChatGPT results does not reveal whether a page was included in training data because both products can use web retrieval and source selection varies by query.
Optimizing for AI Indexing
Brands that want public pages available to AI search should keep those pages accessible to the relevant search and user agents, use clear HTML, and support claims with verifiable sources. No provider promises to prioritize original research, and crawler logs show access rather than inclusion or citation.
