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

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:

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:

Characteristic Implication for AEO

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 type Readability AEO implication

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-agent Company Function

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.