Technical AEO

How AI Crawlers Work: What ChatGPT, Perplexity, and Google Actually See on Your Site

Apr 12, 202510 min read

Understanding how AI systems crawl and index your content is essential for AEO. Here's exactly how each major AI platform discovers, processes, and decides whether to cite your pages.

The crawl-index-retrieve cycle for AI systems

AI answer engines follow a three-phase process similar to traditional search engines, but with important differences at each stage:

InfographicAI Crawlers — Who Reads What

The 3-Phase Crawl → Index → Retrieve Cycle

Perplexity

PerplexityBot

·Real-time web retrieval
·Bing index + Sonar model
·Cites every answer
·Freshness-weighted

ChatGPT

GPTBot + ChatGPT-User

·Training crawl + Browse mode
·Selective live retrieval
·Context-window limited
·Authority-weighted

Google AI

Googlebot + AdsBot

·Full index integration
·AI Overviews on 47% searches
·Rank correlated but separate
·E-E-A-T weighted

Claude

ClaudeBot

·Training data focused
·Web access via tools
·Constitutional AI filter
·Source diversity preference

Content Readability Matrix

Content Type	Perplexity	ChatGPT	Google AI	Claude
Semantic HTML (h1-h6, ul, ol, p)	Full	Full	Full	Full
JSON-LD Schema markup	Full	Full	Full	Partial
Inline images (no alt text)	None	None	Partial	None
CSS-hidden / display:none text	None	None	None	None
JavaScript-rendered content	Partial	Partial	Full	None
PDF documents	Partial	None	Full	Partial
Video transcripts (in HTML)	Full	Full	Full	Full

Source: RankAsAnswer crawler behavior research · 2025

1. Crawl

AI crawlers (PerplexityBot, GPTBot, Googlebot) discover and download your page content. They follow links from known pages, sitemaps, and direct URL submissions. Unlike traditional crawlers, AI-specific bots often have lower crawl budgets and may deprioritize sites without clear structured signals.

2. Index

Crawled content is processed, chunked into semantic units, and stored in vector databases or retrieval indexes. This phase is where Schema markup and structural signals matter most — they help the indexer correctly classify, chunk, and tag your content for retrieval.

3. Retrieve

When a user asks a question, the AI's retrieval system searches the index for the most relevant content chunks. Relevance scoring considers query-content semantic match, source trust signals (E-E-A-T, domain authority), and structural clarity. This is why machine-readable structure improves citation rates — it makes the retrieve step more reliable.

PerplexityBot: what makes it different

Perplexity's crawler (identified as PerplexityBot in server logs and robots.txt) is a real-time web crawler that refreshes content much more frequently than traditional search engine bots. This has several implications:

CharacteristicImplication for AEO

Real-time crawling capabilitySchema changes and content updates can appear in Perplexity results within days, not weeks

Heavy use of structured dataFAQPage Schema is parsed and used directly in Sonar model responses — the fastest path to Perplexity citation

Source quality scoringPerplexity's model scores sources for authority — DA, citation history, and Schema completeness all factor in

Direct URL citationPerplexity shows sources panels with direct page URLs — your AEO score directly correlates with whether you appear here

GPTBot: how OpenAI crawls for ChatGPT

OpenAI runs two relevant crawlers: GPTBot for training data collection, and ChatGPT-User for Browse-mode real-time access. These serve different functions:

▸GPTBot: Crawls for training data. Blocking this (via robots.txt) prevents your content from being included in future model training but doesn't affect Browse-mode citations
▸ChatGPT-User: Used in Browse mode. When a user asks ChatGPT to search the web, this bot crawls in real-time. It reads your current content, including Schema
▸Citation behavior: ChatGPT Browse-mode citations are more selective than Perplexity — they tend to cite fewer sources per answer, making it harder but more valuable when achieved

Google AI Overviews: a different model

Google AI Overviews (formerly SGE) doesn't use a separate crawler — it relies on Google's existing Googlebot crawl data. This has important implications:

How AI Overviews selects sources

Google AI Overviews primarily cites pages that already rank well in traditional Google search for the query. This means your classic SEO signals (PageRank, topical authority) still matter, but AEO signals (Schema, structure) increasingly influence whether a ranking page gets cited in the AI Overview versus the organic results.

The practical implication: for Google AI Overviews, AEO improvements on pages that already rank will have a larger impact than AEO improvements on pages that don't rank. Fix the traffic-getting pages first.

What AI crawlers can and can't read

Content typeReadabilityAEO implication

HTML text contentFullPrimary source for content extraction — must be well-structured

JSON-LD Schema (in head)FullRead before rendering — highest priority for structured signals

Meta tags (title, description)FullUsed for page context and entity identification

JavaScript-rendered contentPartialMay be missed by some bots — critical content should be in static HTML

CSS-hidden contentVariesOften ignored — never put citable content in hidden elements

Images (without alt text)NoneImages must have descriptive alt text to contribute any signal

PDFsPartialText is extractable but Schema is not — prefer HTML for citable content

Content behind loginNoneCannot be cited — public content must be publicly accessible

Signals that influence crawl priority

AI crawlers have limited budgets and prioritize pages that signal authority and freshness. These factors influence whether your pages get crawled and how frequently:

▸XML sitemap submission — a well-maintained sitemap with lastmod dates is the clearest crawl signal
▸Page load speed — slow pages are crawled less frequently and with lower priority
▸Internal link structure — pages with more internal links are treated as higher priority
▸External inbound links — high-DA inbound links signal authority and prioritize crawl
▸Valid Schema presence — pages with Schema are often treated as higher-quality signals

Controlling AI crawler access with robots.txt

You can selectively allow or block specific AI crawlers using robots.txt directives. The main crawler user-agents to know:

User-agentCompanyFunction

PerplexityBotPerplexity AIWeb search and citation indexing

GPTBotOpenAITraining data collection

ChatGPT-UserOpenAIBrowse mode real-time access

Google-ExtendedGoogleGemini and AI Overviews data

ClaudeBotAnthropicTraining and Claude web search

The default recommendation

Unless you have a specific reason to block AI crawlers, allow all of them. Blocking AI crawlers removes your content from the citation pool entirely. The only exception is blocking GPTBot if you don't want your content used in OpenAI training data — this doesn't affect ChatGPT Browse citations.

Freshness and re-crawl frequency

Content freshness is a significant factor in AI citation priority. Here's how to maximize your re-crawl frequency:

▸Update lastmod in your XML sitemap whenever you update a page — bots use this to prioritize re-crawls
▸Set Cache-Control headers appropriately — overly aggressive caching can cause crawlers to see stale content
▸Submit updated URLs via Google Search Console's URL inspection tool after major updates
▸High internal link counts to a page correlate with faster re-crawl — keep important pages well-linked

What is llms.txt?

How the new AI-specific robots.txt works and whether you need it.

Schema markup mistakes

Common errors that reduce what crawlers can extract from your Schema.

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