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In 2024, there were 8.3 billion daily searches on Google, but not all of those searches were made by humans.
Crawlers make up a huge portion of online searches. They sort information and index it to create a database that informs the search results humans see when we use search engines.
Both traditional search engines and newer AI search engines use these crawlers to parse and understand the information available on the Internet. Understanding exactly how these LLMs collect information will help guide your brand in ensuring your content is crawlable and therefore available to be served in AI search results.
Humans and crawlers from traditional search engines like Google still dominate online search behavior. But, as search patterns change with the increased expansion of LLMs and AI search platforms, this distribution will shift. OpenAI’s GPTBot and Anthropic's Claude bot already have a combined volume of requests that equals about 20% of GoogleBot’s, and those requests are only likely to increase.
Let’s lay the groundwork by looking at how Google’s crawlers work. Google’s crawler, GoogleBot, catalogues pages across the internet to index content, then lists them on search engine results pages (SERPs) when someone enters a query.
AI crawlers work similarly, but they’re crawling pages to gather information and content for LLMs and other AI search platforms. Just like Google and Bing have different crawlers, so do LLMs. OpenAI has GPTBot, for example. Since many of these AI crawlers are newer, they’re still being improved to ensure that LLMs can access reliable, high-quality content.
Crawlers find websites to crawl from a “seed,” or a list of known URLs. They then find hyperlinks to other URLs and crawl those next. Crawlers decide which websites to crawl based on a few factors:
“Crawling” is the technical term for using a software program to access a website and acquire data. When AI crawlers “crawl” a website, they download and index content from that source. The goal behind indexing content is to learn what as many websites on the internet are about so an AI answer engine can retrieve the content when it’s relevant for a user query.
Each LLM has its own AI crawlers that parse through and index information on the Internet to make it available for queries. Some AI search platforms have two different types of crawlers: one type of crawler that gathers data for the AI model to be “trained” on, and another to assist with Retrieval-Augmented Generation (RAG).
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RAG refers to when an AI model utilizes a crawler to parse real-time data to inform a response. In comparison, training data crawlers create a database that informs the machine learning model but cannot account for updates or information changes.
Most AI models use a combination of training and real-time data to provide a targeted and relevant answer to search queries. For example, ChatGPT uses real-time information for some requests and pulls from its set training data for other requests.
Just as crawlers for traditional search engines find the content displayed in search results, AI crawlers find the content that AI search platforms use to present their consolidated summaries of information.
AI crawlers help LLMs provide specific and on-demand answers more efficiently than ever. Answer engines remember context and participate in conversations to provide comprehensive responses based on relevant materials. None of these mechanisms would be possible without AI crawlers.
Pulling from the database created by content crawlers indexed from the Internet, LLMs present information most relevant for a certain query. Crawlers create a library of resources LLMs can rely on to answer a user query.
Website developers can choose to block training or RAG bots if they want to. Crawlable websites are more likely to be featured in AI results. Your website content must be accessible to AI crawlers and optimized to provide accurate and helpful information so that LLMs will rely on it to address user queries.
First, AI crawlers must be able to access, scan, and catalog the content on your website. This ensures that your brand and any relevant information can be used by LLMs and presented to users in query responses. Review your llms.txt and robots.txt files to ensure you’ve allowed the crawlers access to your site.
AI crawlers process code differently than traditional search engines. This impacts how they see and understand your digital content. There’s a clear divide in JavaScript rendering capabilities among AI crawlers: Googlebot fully renders JavaScript, but most AI crawlers cannot.
Even though ChatGPT and Claude crawlers fetch JavaScript files, they don’t execute them. 11.5% of ChatGPT’s fetches and 23.84% of Claude’s requests are for JavaScript files. This creates a blind spot where client-side rendered content becomes invisible to AI.
For marketers, this difference in rendering capabilities makes server-side rendering critical for essential content if they want AI crawlers to see it. Key information should be delivered in the initial HTML response to ensure AI crawlers can access it. Creators can still use client-side rendering to enhance features like interactive UI elements.
Content in the initial HTML response has a better chance of being indexed since AI models prioritize HTML content. Appropriate heading structures, semantic elements, and accurate image alt attributes help AI systems understand page context.
Traditional search engine crawler developers have spent years refining their crawling strategies. AI crawlers are newer, however, and have different efficiency patterns. Understanding the nuances of AI crawler efficiency reveals how to optimize sites for better AI visibility.
AI crawlers have not yet developed the sophisticated URL selection and validation of traditional search engine crawlers. As a result, AI crawlers fetch more 404s than traditional search engine bots.
There are many potential reasons for the high rate of 404 errors. They can indicate that AI crawlers often attempt to fetch outdated assets from static folders or signal that AI crawlers may have limited time budgets for processing a site. With higher 404 fetching rates, crawlers are wasting these resources.
Speed is the crucial variable. AI systems often operate with 1-5 second timeouts for retrieving content. Slow response times can lead to incomplete content or complete abandonment. In comparison, pages that load faster with key information higher in the HTML structure ensure that AI crawlers can efficiently process the most important content before timing out.
For marketers and site owners, here’s a helpful checklist to address efficiency challenges:
Once you’ve optimized your site for AI crawlers, monitoring your visibility is the next important step. Answer engine optimization platforms like Goodie can provide insights into how LLMs understand and serve your content.
Goodie helps brands succeed using sentiment analysis and competitor benchmarking, among other metrics, to optimize AI brand visibility. It lists ways to improve content to boost brand visibility by leveraging robust reporting and analytics features.
It’s no longer enough to rely on traditional SEO to maintain brand visibility. If you want your brand to be visible on LLMs, your content needs to be clear, crawlable, and consistent. This holds for your site and every digital touchpoint, including FAQ pages, support sites, and social content.
Brands that adapt early by understanding how AI crawlers discover and prioritize content show more frequently, accurately, and in a relevant context in front of users ready to make decisions. Optimizing for AI is a shift in how we think about brand discoverability in a world where AI and humans skim the internet.
The way content is found is changing. Make sure your brand is part of what’s seen next.
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