Brand visibility in AI answers begins with whether systems can consistently interpret structured information. If interpretation fails at the infrastructure level, recommendations become inconsistent or disappear entirely.
How AI Search Works | The Process of LLM Information Collection and Interpretation
How AI search works refers to the complete process by which LLMs collect and learn from structured information on the web, then synthesize and output that information as a single response to a user's question.
Whether AI systems recognize your brand is determined not by your search ranking, but by whether AI can accurately read and interpret your site's content.
Understanding how AI search works is the foundation for designing an effective GEO strategy.
What You'll Learn in This Article
The two pathways through which AI search collects information
What signals LLMs use to determine trustworthiness
Why approximately 27% of websites are invisible to AI
1. The Two Pathways AI Search Uses to Collect Information
AI search collects information through two distinct pathways.
AI Search Information Collection Pathways
Pathway
What It Does
Primary Users
Training crawlers
Bots that collect data for LLM training by crawling websites and retrieving content used in model learning
All LLMs (during training phase)
Real-time search
When answering a user's question, the system retrieves information from the web in real time
Select versions of ChatGPT, Gemini, and others
In both pathways, structuring content so AI can accurately read and interpret it forms the foundation of GEO strategy.
2. The Reality of AI-Bot Traffic
As I've been reading international research on AI search, the breakdown of AI-bot traffic was one of the most striking data points I encountered.
According to research published by LightSite AI (February 2026), analysis of millions of AI-bot requests across dozens of domains found that approximately 90% of observed AI-bot traffic originates from training crawlers.
AI-Bot Traffic Breakdown
※ Created in-house based on publicly available information (Source: LightSite AI, February 2026)
What this ratio tells us is that the majority of AI visibility is determined not by real-time search, but by how information is ingested during the training phase.
Structuring content in a way that training crawlers can accurately read is considered a critical foundation for AI visibility.
3. What Signals LLMs Trust
The LightSite AI research also identified the signals that help LLMs interpret and retain brand information reliably.
According to the research, AI systems respond more consistently and accurately when entity relationships, product context, and brand positioning are clearly structured.
Stas Levitan, Founder of LightSite AI, stated:
Brand visibility in AI answers begins with whether systems can consistently interpret structured information. If interpretation fails at the infrastructure level, recommendations become inconsistent or disappear entirely.
The view that AI brand visibility starts at the infrastructure level — before any optimization tactic — is gaining traction internationally.
The following three elements are considered effective for building the signals LLMs trust:
Entity clarity: Clearly stating relationships between company name, product names, and categories
Structured data: Organizing content in machine-readable formats that LLMs can parse reliably
Question-oriented URL structure: Pages with URLs formatted as questions tend to show higher AI-bot engagement rates
4. Why 27% of Websites Are Invisible to AI
The LightSite AI research also surfaced an often-overlooked issue that significantly impacts AI visibility.
Approximately 27% of websites unintentionally block at least one major LLM bot, often due to security configurations.
When a bot is blocked, it cannot retrieve information — meaning that brand is underrepresented or absent from the AI's training data.
Share of Websites Unintentionally Blocking LLM Bots
※ Created in-house based on publicly available information (Source: LightSite AI, February 2026)
Confirming that your site is not blocking LLM bots is considered a prerequisite for any GEO strategy.
Reviewing your robots.txt settings and security configurations to ensure major LLM crawlers — such as GPTBot and Google-Extended — have access is a recommended first step.
5. What AI Search Mechanics Mean for GEO
Understanding how AI search works makes it possible to prioritize GEO efforts more effectively. Three key implications stand out.
Content structure is the top priority: LLMs are considered to interpret well-structured content more reliably. Adding definitions, FAQs, and comparison tables is considered directly linked to improved AI search visibility.
Training-phase visibility matters most: With approximately 90% of AI-bot traffic coming from training crawlers, optimizing for the learning phase — not just real-time search — is considered essential.
Check your crawler access settings: Given that approximately 27% of sites unintentionally block LLM bots, auditing your site's crawler accessibility is a recommended first step.
For a practical guide to GEO implementation, see How to Get Started with GEO.
With the GEO tool Genview, you can monitor how often your content is being cited by AI systems.
Frequently Asked Questions
Q: Is AI searching the web in real time to generate answers?
A: It depends on the AI platform. Some versions of ChatGPT and Gemini have real-time search capabilities, but LightSite AI research found that approximately 90% of observed AI-bot traffic comes from training crawlers. Optimizing for the training phase is considered just as important as optimizing for real-time search.
Q: How can I check whether LLMs are able to read my site?
A: Start by reviewing your robots.txt file to confirm that major LLM crawlers — such as GPTBot and Google-Extended — are not blocked. Given that approximately 27% of websites unintentionally block at least one LLM bot, regular checks are recommended.
Q: Does adding structured data improve visibility in AI search?
A: Yes, it is considered to improve visibility. LightSite AI research found that AI systems interpret and retain information more reliably when entity relationships, product context, and brand positioning are clearly structured. Adding definitions, FAQs, and schema markup is considered effective.