How Large Language Models Actually Learn About Your Brand (Without Ever Visiting Your Website Directly)

The New Reality: Why AI Tools Talk About Some Brands and Ignore Others

Over the last few years, the game has quietly shifted from "Can people find us on Google?" to "Will AI assistants actually name our brand when someone asks for help?"

When a customer types something like "Who are the best payroll providers for small businesses?" into an AI assistant, large language models are doing the heavy lifting behind the scenes. They pull together an answer in seconds—and some brands get mentioned over and over, while others never show up at all.

Here at My Brand Mentioned, we talk to a lot of business owners who assume these AI tools "visit their website" in real time, the same way a human would. Or they imagine AI works exactly like Google's crawlers. In reality, that's not how large language models operate.

Common frustrations we hear:

• You see competitors mentioned in AI answers, but your brand is invisible.
• You've invested years into SEO and content, but AI tools still don't seem to "know" who you are.
• You're worried that just ranking in Google isn't enough anymore.

In this article, we're going to walk through in plain language:

• What large language models actually are and how they learn.
• Where they really get information about your brand.
• Why third-party content and brand mentions now matter as much as your own website.
• Simple checks and practical steps to improve your odds of being included in AI answers.

Example: Imagine a local plumbing company that shows up on page one of Google when someone searches "emergency plumber in Denver." They're doing "ok" in traditional search. But when a homeowner asks an AI assistant, "Who are the most reliable plumbers in Denver?" only two big competitors are recommended—and our local company isn't mentioned at all. That's the gap we're talking about.

At My Brand Mentioned, our whole focus is helping brands close that gap—so they show up more often and more accurately in AI-generated answers, not just in traditional search results.

What Large Language Models Actually Are (In Plain English)

From Search Engines to Large Language Models—What Changed?

Traditional search engines and large language models solve different problems.

• Search engines scan billions of pages, match keywords, and then rank those pages so you can click through and read them yourself.
• Large language models (LLMs) like the ones behind ChatGPT, Gemini, Claude, and others are trained to predict the next word in a sentence based on patterns in huge amounts of text.

That means when you ask a question, an LLM isn't going out to browse the entire web from scratch. It's mostly drawing on what it has already "read" during training, plus whatever updates its creators feed it over time.

A simple way to think about it: a search engine is like a giant, constantly updated index of the web. A large language model is more like a person who has read an enormous amount of material and built up a "reading memory" that it uses to answer questions in its own words.

Example: If you Google "best HR software for small businesses," you'll see a list of web pages you can click. If you ask an AI assistant "Who are some reputable HR software platforms for small businesses?" it will often respond with a curated list of brand names and a short description of each. That curated list is the model using what it has learned about those brands from its training data.

How Large Language Models Learn: Training Data in Human Terms

To learn, large language models are trained on massive collections of text called "training data." Think of it like feeding a super-hungry reader:

• Articles, blog posts, and guides
• Product and service pages
• FAQs and help docs
• Directories and review sites
• Forums, Q&A sites, and more

As they "read" all this content, large language models learn patterns:

• Which brands are associated with which services ("Brand X is a CRM for real estate teams").
• Which locations they're tied to ("Brand Y serves clients across the Northeast U.S.").
• What problems they help solve and for whom.

There are a few important myths to clear up:

• They are not secretly reading your private CRM, call notes, or internal dashboards.
• They mostly learn from public web content and licensed or partner data, plus any extra information their creators choose to feed them.

Your own website is just one data source in a sea of content. If almost no one else is talking about you online, the model has very little to work with when it tries to answer questions in your space.

If you want a more technical but still approachable explanation for your team, we like this overview of how large language models are trained from AWS.

Where Large Language Models Actually Get Information About Your Brand

Public Web Pages and Structured Data

When it comes to your brand, large language models primarily learn from what's publicly available on the web. That usually includes:

• Your own website: About page, service pages, FAQs, blog posts, case studies, and so on.
• Third-party sites: Business directories, industry associations, local chamber listings, media articles, review platforms, and niche blogs.

Well-structured information makes a difference. Clear "About" content that spells out who you are, what you do, who you serve, and where you operate helps machines connect the dots.

Technical elements like schema markup (a way of adding structured data to your site) can also make it easier for search engines and AI systems to understand your business details, but even without that, plain-language clarity goes a long way.

If your brand is only lightly mentioned online—or only on your own site—large language models won't have much to learn from. That's often why some businesses feel "invisible" in AI answers.

Brand Mentions Beyond Your Own Site (The Big Blind Spot)

Here's the big shift: LLMs don't just care what you say about yourself. They care what the wider web says about you.

When lots of credible websites mention "Brand X" in connection with a specific service, audience, or location, the model starts to build a strong association. For example:

• "Brand X" + "managed IT services" + "Dallas law firms."
• "Brand Y" + "outsourced accounting" + "nonprofits."

Traditional SEO focuses a lot on backlinks—who links to your website and how that affects rankings. That still matters. But for large language models, the bigger story is about brand mentions and context:

• Where your brand name appears.
• What words surround it.
• What problems, audiences, and locations are mentioned with it.

If you only ever talk about yourself on your own site, it's like whispering your story in a crowded stadium. The model "hears" you, but it may not be enough to confidently bring you into the conversation when users ask questions.

Third-Party Content, Reviews, and "Expert" Sites

Large language models pay special attention when other sites do a good job of explaining what you do. For example:

• An industry blog writes a "Top 10 Providers" list and clearly describes your niche and ideal customer.
• A directory or marketplace includes a detailed profile with services, pricing tiers, and customer types.
• Customers leave thoughtful public reviews that mention specific outcomes and use cases, not just "Great service!"
• Niche publications or community sites feature your company in a case study or Q&A.

Models don't "trust" just one random mention. They look for patterns and consistency across many places. The more aligned, clear, and repeated that story is, the easier it is for the model to understand when you're a good fit for a user's question.

Example: We worked with a regional B2B manufacturer that had almost no presence outside its own website. After being featured in several industry roundups, guest posts, and comparison articles that clearly described its specialization and ideal customers, we started to see that brand show up more often in AI answers about suppliers in its niche. The content didn't "hack" the algorithm—it simply gave the models more evidence to work with.

If you want to understand how search and AI-powered experiences use signals like entities, reviews, and high-quality content, Google's own documentation on AI features and how they use your website's content is a helpful reference for your team.

At My Brand Mentioned, we specialize in creating and placing this kind of third-party content—articles, listicles, and features that clearly describe who you are, what you offer, and who you serve—so AI tools have far better "evidence" to learn from.

Why "Being Talked About Elsewhere" Now Matters as Much as Your Own Website

Entity-Level Understanding: How Machines See Your Brand

Behind the scenes, AI systems don't just see your brand as a logo and a URL. They try to build what's called an "entity" for your business—a structured profile made up of facts and relationships, such as:

• Your brand name.
• What you do (services, products, specializations).
• Who you serve (industries, company sizes, locations).
• Where you operate (regions, cities, countries).
• What you're known for (niches, reputation, key strengths).

When that information is consistent across many third-party sites, it strengthens your brand's profile in the eyes of both search engines and large language models. When it's fragmented or contradictory, it weakens that profile and makes it less likely you'll be recommended confidently.

How Mentions Influence Which Brands Show Up in AI Recommendations

In our experience, models tend to prioritize brands that:

• Show up frequently on credible, relevant websites.
• Are described with similar language and positioning across those sites.
• Clearly match the intent behind a question (industry + problem + audience + location).

When that happens, you're more likely to be named when someone asks questions like:

• "Who are the best [service] providers for [audience]?"
• "Which companies can help with [problem] in [region]?"

Example: Picture a boutique marketing agency that is consistently described online as "a PPC agency specializing in B2B SaaS startups." That exact idea shows up on their website, on directory listings, in podcast show notes, and in guest articles. Over time, large language models start to connect that agency strongly with queries about PPC for B2B SaaS—but not, for example, with local restaurant marketing. The pattern of mentions teaches the model where they fit.

Why Traditional SEO Alone Isn't Enough Anymore

Traditional SEO still matters. Ranking highly in Google when people search for what you do is critical for many businesses, and we don't see that changing.

But SEO and AI visibility are solving two slightly different problems:

• SEO helps individual pages rank so people can click through and learn about you.
• AI visibility helps your brand be named and described inside AI-generated answers, even when the user never visits your website directly.

From what we've seen across many clients, the safest, most future-proof strategy is to treat AI visibility as a layer on top of your SEO—not a replacement, but an essential complement. Here at My Brand Mentioned, we typically work alongside existing SEO efforts to fill that off-site, AI visibility gap.

Simple Ways to Check If Large Language Models Already Understand Your Brand

Quick Self-Check Using AI Assistants

You don't need a data science team to do a quick check on how well large language models understand your brand today. You can do a basic audit in under an hour.

Open one or more AI tools (ChatGPT, Perplexity, Claude, Gemini, etc.) and try prompts like:

• "Which [your service] providers would you recommend in [your city/region]?"
• "What can you tell me about [Your Brand Name] in [your city/region]?"
• "Who are some reputable companies that help [your audience] with [your main problem]?"

Here's a simple process you can follow:

1. Create a quick spreadsheet with columns: Question, Tool, Mentioned Y/N, How Described.
2. Test 3–5 questions in each AI tool you care about.
3. For each answer, note:
   • Did the tool mention your brand at all?
   • If yes, was the description accurate, outdated, or misleading?
4. Look for patterns:
   • Are you never mentioned?
   • Only mentioned for certain types of questions or locations?
   • Mentioned but described in a way that no longer fits your business?

This gives you a baseline. From there, you can track whether your visibility improves as you invest in better off-site content and brand mentions.

Quick Web Visibility and Consistency Check

Next, do a 10–15 minute scan of how your brand looks across the web. Use your preferred search engine and try:

• "[Your Brand Name] [your city or region]"
• "[Your Brand Name] reviews"
• "[Your Brand Name] [your primary service]"

As you click through, ask yourself:

• Are third-party sites describing us clearly and accurately?
• Is the same basic story showing up everywhere, or is it fragmented?
• Are there old descriptions that no longer match who we serve today?

Turning Insights Into Action: Getting Your Brand "AI-Ready"

Building a Strong, Consistent Brand Story Across the Web

Your goal is to make it extremely easy for both humans and machines to see the same story about your brand over and over again. That means:

• Same brand name.
• Same core services and positioning.
• Same general target audience and location(s).

Start with the places you control most:

• Your website's About page and key service pages.
• Your Google Business Profile and major directory listings.
• Profiles on industry associations, marketplaces, or software directories (if applicable).
• Guest posts, interviews, and feature articles on relevant sites.

Here's a simple sequence you can follow:

1. Write a clear, one-sentence positioning statement: "We are a [type of business] that helps [audience] with [problem] in [location, if relevant]."
2. Update your main website pages to reflect that statement and supporting details.
3. List the top 3–5 third-party sites that already mention your brand (directories, reviews, associations) and update those descriptions to match your new positioning.
4. Make a short checklist you can reuse whenever you create a new profile or bio, so you don't drift away from that positioning over time.

Over time, this repeated, consistent story becomes the "backbone" of how large language models understand your brand entity.

Creating the Right Kind of Off-Site Content for AI Visibility

Not all mentions are created equal. A generic directory listing that simply says "Great service!" is less helpful than a detailed article that describes exactly who you help and how.

We've found that the best content for AI visibility tends to be:

• Context-rich: It explains who you serve, what problems you solve, and what makes you different.
• Specific: It uses real examples, industries, and use cases—not just buzzwords.
• Hosted off-site: It lives on reputable, relevant websites besides your own.

Some strong formats include:

• Expert roundups where you contribute a quote and your brand is listed with a focused description.
• "Best of" or "Top X Providers" listicles in your niche where your brand appears in context.
• Educational guest posts that teach something useful and naturally mention your brand in the right context.

Quality and relevance are far more important than blasting your name onto hundreds of low-quality sites. Spammy, off-topic placements can actually confuse both users and machines.

Example: A consulting firm we've seen do this well focused on a handful of niche industry Q&As, conference blogs, and partner sites. Each piece clearly explained their methodology, ideal client profile, and the outcomes they deliver. As those mentions stacked up, they started showing up more often when AI tools were asked for "consulting firms that specialize in [their niche]."

When It Makes Sense to Bring in a Partner

The reality is that most teams are already stretched. Planning, writing, and placing high-quality off-site content takes time, relationships, and a clear strategy. It's very easy to waste budget on placements that look good on paper but don't move the needle for AI visibility.

A good partner should help you with:

• Clarifying your positioning and the story you want AI tools to learn about your brand.
• Creating content that's friendly to both humans and machines—clear, specific, and context-rich.
• Finding and securing placements on reputable, relevant websites.
• Tracking where and how your brand is being mentioned over time, so you can see progress.

Here at My Brand Mentioned, we built our service specifically to take this off your plate. We design an AI visibility plan around your brand, write content that clearly explains who you are and who you help, and get that content published on the kinds of sites large language models pay attention to. That way, when someone asks an AI assistant for help in your space, there's a much better chance your name is part of the answer.