How LLMs “Discover” Brands and Companies

LLMs can learn about a brand in two different ways

The first happens during training.

Large language models are trained on enormous collections of text. During that process, the model learns relationships between words, concepts, products, people and companies.

Imagine that a fictional company called Flowdesk appears across the internet in sentences such as:

“Flowdesk is a project management platform for remote teams.”

“Flowdesk helps companies manage projects and tasks.”

“Alternatives to Flowdesk include Asana and Monday.com.”

The model does not save these sentences in a traditional company database. Instead, it gradually learns relationships. Flowdesk becomes connected with ideas such as project management, remote teams, task management and perhaps competitors like Asana. This is one way a model develops what we might call an understanding of a brand. The more clearly those relationships appear across useful content, the easier it becomes for the model to associate the company with a particular market or problem. But training is only part of the story.

AI assistants can also discover companies while answering

Modern AI assistants are increasingly connected to search engines, indexes and other information sources. This means they do not always need to depend entirely on what the underlying model learned during training.

Suppose someone asks:

“What are some good payroll platforms for small companies in Portugal?”

An AI system with web access may search for relevant information before answering. It can retrieve company websites, articles, comparison pages or other sources and use that information to build its response. This creates an important distinction. An LLM may already know about a company from training, or an AI assistant may find that company through retrieval when the question is asked. For established brands, both may happen. For a newer company, retrieval can be particularly important. The business might not have existed when a model was trained, but it can still become part of an AI-generated answer if current information about it is available and relevant.

The difficult part is not simply being online

Most companies already have websites. That does not necessarily mean AI systems understand them well. Consider a startup whose homepage says:

“We are redefining the future of business.”

It sounds impressive, but it contains very little information. What does the company actually sell? Who uses it? Which market does it operate in? What problem does it solve?

Now compare that with:

“We provide workforce scheduling software for restaurants and retail businesses.”

The second description gives an AI system much more to work with. It connects the company to a product category, a business problem and a specific type of customer. This is why clear positioning matters beyond traditional marketing. A website is not only communicating with potential customers anymore. Search engines, AI systems and automated agents are also trying to understand what the company represents. If the positioning is vague, their understanding can be vague too.

A brand is understood through context

A company also does not exist online only through its own website. Imagine an AI system encountering a brand across several sources. Its website describes it as customer-support software. G2 lists it in the help desk category. A technology publication compares it with Zendesk. A customer writes about using it to automate support tickets. A YouTube review demonstrates its chatbot functionality. A Reddit discussion recommends it to small ecommerce companies. Together, these sources create much richer context than the company's homepage alone. The brand becomes associated with customer support, help desks, ecommerce, automation and specific competitors. This is particularly important because people rarely ask AI assistants only for company names.

They ask questions such as:

“What is a good Zendesk alternative for a small business?”

“Which customer support platform has good AI automation?”

“What tools can a Shopify store use to reduce support tickets?”

For a company to appear in answers like these, the AI needs enough context to connect the company with those specific situations. Simply repeating the company name is not enough.

What other people say about the company matters

There is another important difference between a company's website and the wider web. Your website tells people how you describe your company. Other websites show how the market describes your company. Reviews, comparison articles, industry publications, customer stories, directories, videos and community discussions can all provide additional context. Suppose a company calls itself “the leading AI sales platform.” which is a marketing statement. But if customers, reviewers and industry publications independently describe the product as a sales automation platform, compare it with similar products and discuss where it works well, AI systems have much more information available to understand its place in the market. This does not mean companies should try to create hundreds of artificial mentions. The goal is not repetition for its own sake. The goal is to build a genuine presence around the topics, products and problems the company wants to be associated with.

AI visibility is not the same as ranking on Google

This is where thinking about LLMs like traditional search engines can become misleading. Google normally gives users a ranked list of pages. An LLM generates an answer. That answer can change depending on the question.

Ask:

“What are the best CRM platforms?”

Then ask:

“What is a simple CRM for a five-person company?”

Then:

“What CRM has strong WhatsApp integration?”

The answers may contain completely different brands. A company may be highly relevant to one question and irrelevant to another. There is therefore no permanent “number one position” inside an LLM. AI visibility is much more contextual. What matters is whether the model can confidently connect a company with the specific problem, category, customer or requirement mentioned in the question.

What should companies actually do?

The good news is that companies do not need to rebuild their entire marketing strategy for LLMs. Most of the fundamentals are familiar. Make sure someone visiting the website can quickly understand what the company does, who the product is for and which problems it solves. The website, social profiles, directories, product listings and other important sources should tell roughly the same story, create useful content around real customer questions, explain use cases, compare approaches, document integrations, answer common buying questions, publish customer stories, make product information easy to find and keep important pages current.

And look beyond the company website.

Reviews, partnerships, industry coverage, customer discussions and other credible third-party mentions help build a broader picture of the business. The objective is not to “trick ChatGPT” into recommending a company. It is to make the company easier to understand.

The web is becoming the context behind the answer

For years, companies optimized websites mainly around one question:

Can people find us on Google?

AI assistants introduce another question:

When someone asks an AI about our market, does it understand who we are?

The difference is subtle, but important. LLMs do not discover brands through a single ranking factor or a hidden company directory. They build relationships from the information available to them, either through what they learned during training or what they can retrieve when generating an answer. That means a company's AI visibility ultimately depends on something fairly simple. Is there enough clear, consistent and credible information available for an AI system to understand what the company does and when it is relevant? Companies that make that answer obvious will be much easier for both people and AI systems to discover.