AI Visibility and AI Interface - Two Layers of SEO for the Era of Answers

Search has changed. Previously, a user would open Google or Yandex, enter a query, click on links, and explore websites on their own.

Today, the scenario increasingly looks different: a person asks a question to ChatGPT, Claude, or Perplexity and gets a ready-made answer compiled from multiple sources at once.

This changes the approach to SEO. Now, it is important not only to get into search engine results but also to make information about your company, product, or service understandable for AI systems-and at the same time, think about what a person will do with your site if they open it alongside AI.

I would divide this task into two parts: AI Visibility and AI Interface.

AI Visibility: Making Your Site Understandable for Neural Networks

The first task is to help AI systems correctly understand what is on your site.

Here, basic technical and content structure remains crucial:

• Clear site architecture;
• High-quality and standalone pages (each must make sense outside the context of the rest of the site);
• Headings and structured content;
• FAQ;
• Documentation;
• Schema.org and other structured data;
• sitemap.xml;
• robots.txt.

An additional tool can be llms.txt - a text file in the root of the site, where you briefly and in a structured way describe the site: what the company does, what products it offers, and where the most important information is located.

Example:

# Company Name

> Brief description of the company and product.

## Main Sections

- [Product](https://site.com/product)
- [Services](https://site.com/services)
- [Documentation](https://site.com/docs)

## Useful Materials

- [How it works](https://site.com/how-it-works)
- [FAQ](https://site.com/faq)

Essentially, this is an attempt to create a clear map of the site's most important content for LLMs.

But it is important not to overestimate the role of llms.txt. The standard is not officially supported by any major AI company, and the mere fact of having the file does not guarantee that ChatGPT, Claude, or another neural network will read it and start recommending your product. Moreover, most conversational AI systems today do not crawl websites in advance at all-they access the page only at the moment of a user's request (through search or an explicit link).

So llms.txt is an additional, inexpensive layer of preparing a site for the LLM era, not a guarantee of getting into neural network answers. You shouldn't ignore it, but you shouldn't overestimate its effect either.

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AI Interface: Letting the User Interact with Your Site via AI

We are used to thinking that the neural network should find our site on its own. But we can go the other way: don't wait for the user to find information about the product through ChatGPT, but right on the site offer them to continue exploring through a familiar AI interface.

For example, place buttons:

• Ask ChatGPT
• Ask Claude
• Explore in Perplexity

Behind each button is not just a link, but a pre-prepared prompt that provides the user with the necessary context, without forcing them to formulate a question from scratch.

The user is on the product page. Instead of the usual "Learn more" button, you can offer:

Ask AI if this product is right for me.

And pass a ready-made query to the neural network:

Examine the page [URL] and explain in simple terms what problem this product solves. What companies is it suitable for and in what cases should it be considered?

The button becomes a ready-made AI prompt that helps the user figure out the product faster, rather than just a link to another article.

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Every Page Can Have Its Own AI Scenario

You don't have to use one universal question for the entire site. Each page can have its own AI interaction scenario:

That is, we begin to design not only the pages of the site, but also scenarios for user interaction with AI based on these pages-essentially a separate UX layer on top of the usual content.

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How It Works Technically - And What the Catch Is

The idea of the button is simple: the user clicks it and opens the familiar neural network with a ready-made question and a link to the desired page. Technically, this is done via the q parameter in the URL, for example:

https://chatgpt.com/?q={text}
https://claude.ai/new?q={text}
https://www.perplexity.ai/search/new?q={text}

But this method has some practical problems that you should be aware of before you put such buttons in production.

This is an unofficial and unstable mechanism. Neither OpenAI, Anthropic, nor Perplexity document or guarantee support for the q parameter-this is behavior that has developed de facto and can change without warning. For example, Claude's parameter stopped working for some time at one point; you shouldn't rely on it as a permanent API.

It doesn't work the same for all services. ChatGPT and Perplexity generally automatically send the request via the link; Claude's q parameter substitutes the text into the input field but doesn't always guarantee sending it automatically-the user might need to press Enter themselves.

Because of this, it is more practical to build a fallback option into such buttons: if automatic substitution fails, the user must still see the prompt text and be able to copy it manually (for example, a "Copy Prompt" button next to the transition button). It's slightly less impressive, but essential in the era of the lazy internet.

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The Site Is No Longer the Only Interface

Previously, the site was the main place where a user interacted with company information: read texts, studied pricing, compared products, and drew conclusions independently.

Now, increasingly, the user just wants to ask: "I have a small company, will this service work for me?"-and get an answer. It's important for them to quickly understand if the solution fits them.

In this sense, the site is gradually becoming not an interface but a source of structured information that can be interacted with via different AI systems.

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What to Check Before Implementation

Before putting AI buttons all over the site, it makes sense to honestly answer a few questions:

• Is the page self-sufficient at all? If AI opens only it, will there be enough context for a clear answer, or does the page rely on the menu, adjacent blocks, and other context "invisible" to the model?
• Are you ready for the fact that the model might make a mistake or miss something? A button is delegating part of the product explanation to a third party that you do not control.
• Does the button duplicate a simpler path? Sometimes a clear FAQ on the page will answer the user faster than transitioning to an external chat.
• How will you measure the effect? Button clicks are not enough-it's useful to see if such users get further down the funnel than those who explored the site the usual way.

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What Changes in the SEO Approach

LLM SEO is not just an attempt to make your site appear in the response of ChatGPT or Perplexity. It is a broader task.

On the one hand, you need to make the site understandable for AI:

• Structure information;
• Create high-quality, standalone content;
• Use llms.txt as an additional, but not decisive layer.

On the other hand, you need to think about how the user can continue exploring your product via AI, and build a reliable (not just impressive) technical mechanism for this.

Even now, we must begin designing sites not only around pages and user journeys but also around AI scenarios.