As AI-driven search becomes the norm, visibility isn't about ranking anymore — it's about being cited. Large language models (LLMs) don't index pages the way traditional search engines do. They generate answers based on structured data, verified context, and the relationships between facts.
In this Visibility Brief: Deep Dive, Sam Davis, Global Head of Solutions Engineering at Yext, and Rebecca Colwell, SVP of Marketing, break down how LLMs actually generate answers, and what happens when your brand data isn't a part of the equation. They discuss the role of structured information, the growing risk of hallucinated responses, and how knowledge graphs are uniquely designed to meet the demands of AI search.
What you'll learn
How LLMs retrieve and generate answers
LLMs deconstruct queries, pull information from many sources, apply reasoning, and then return a tailored response. Understanding this process is key to influencing how your brand is represented in AI answers.
What happens when brands don't provide trusted data
LLMs don't stop when data is missing – they guess. Inconsistent or incomplete brand information opens the door to hallucinations or misinformation. Consistent, structured data reduces that risk.
Why knowledge graphs are built for AI search
Knowledge graphs connect entities and relationships – the same way LLMs process information. That structure makes them a preferred source for AI systems seeking accurate answers.
How structured data powers visibility across every channel
Your knowledge graph isn't just for AI. It fuels listings, local pages, schema markup, internal site search, APIs, AI-driven applications, and more — increasing consistency wherever answers are generated. This creates a foundation for accurate visibility for both humans and machines.
What MCP could mean for the future of AI discovery
Model Context Protocol (MCP) points to a possible future where LLMs pull brand-verified data directly, instead of crawling the web. Brands with structured, connected data today will be best prepared for that potential reality.
Why watch
If you're responsible for brand accuracy, digital strategy, or AI readiness, this session breaks down how answer engines work – and what they expect from your data. You'll gain a clearer understanding of how to influence visibility today, and how to prepare for the future.
Why it matters
AI search doesn't just change where answers appear, it changes who controls them. In this zero-click world, brands can't afford to have unstructured or scattered data. To be visible, trusted, and cited, your data needs to be ready for machines.
This episode shows why a knowledge graph isn't a backend tool — it's your brand's visibility infrastructure.
