Feeds vs. Structure: How LLMs Actually See Your Business

Most people think “AI SEO” is just about posting more content. It’s not.

To an LLM, your visibility depends on how well it can see, understand, and trust what you publish.

Let’s break it down.

1. Feeds – The Surface Layer

Feeds help surface-level crawlers see what you’re producing. They’re like machine-readable windows into your website: structured, accessible, and easy to parse. When you expose your data through a feed, you’re essentially saying:

“Here’s what I want AI systems to know about my business, products, or content.”

Feeds power discoverability, they help LLMs find you.

2. Schema – The Structure Layer

Schema is not just data, it’s structure. It defines how your information connects to the world’s shared knowledge graph. For example, you can write your brand name anywhere on your site and hope the model connects the dots.

Or you can use structured schema like schema.org/brand, which tells the model exactly what that text represents.

So instead of guessing, the model knows:

“This is a brand, not just a string of text.”

That’s the power of structure, you’re not leaving meaning up to inference. You’re defining it.

3. Trust – The Confidence Layer

Trust layers help LLMs verify whether your content is reliable. This isn’t a public “score,” but it’s real. Models evaluate the confidence of your information based on multiple signals:

• How consistent your data is across the web (how others see you, what place you hold in your industry)
• Whether your site uses authoritative references or metadata (references are better)
• How your brand has been cited, linked, or reviewed elsewhere (social media platforms, third party reviews)
• Do you have a physical location? If so, how can it verify that location belongs to you?

The higher your trust signal, the more likely your content is treated as authoritative rather than noise.

4. Context – The Meaning Layer

Everything you produce is content, but not everything you produce has context. Context is why your content exists.

If you write a blog post about “how to make bead bracelets,” the content is the tutorial, but the context is teaching beginners how to start a new hobby. If the model’s user is an advanced jewelry designer, your content is technically correct but contextually irrelevant.

LLMs reason by context, not keyword. They elevate information that best fits user intent, not just information that exists.

That’s why understanding context alignment is critical to LLM visibility.

Bringing It Together

• Feeds make you discoverable.
• Schema makes you understandable.
• Trust layers make you credible.
• Context makes you relevant.

This isn’t theoretical.

It’s the foundation of how large models perceive, prioritize, and recall information.

And while there’s no official textbook on “LLM Visibility,” the signals are already there.

We’re just learning how to read them and for the first time, to influence them.