What Data Do AI Models Use to Recommend Businesses

The Question Every Business Owner Asks

Once a business owner discovers that AI is recommending (or not recommending) their business, the first question is always the same: “What information is it using?”

It is a fair question. If you knew what data AI relies on, you could understand why you appear or don't appear. You could make sense of the results.

The answer is both simpler and more complicated than most people expect.

Training Data, Explained Simply

AI models like ChatGPT, Gemini, and Claude are not databases. They do not store a list of businesses and look them up when you ask. Instead, they are trained on massive collections of text from across the internet.

During training, the model reads billions of words - from websites, review platforms, news articles, forums, directories, and public documents. It does not memorize this text. Instead, it learns patterns: which businesses are associated with which locations, industries, and qualities.

When you ask “who is the best accountant in Phoenix,” the model is not searching a database. It is generating text based on the patterns it learned. If a particular firm was mentioned frequently and positively across the training data, the model is more likely to generate that name in its response.

The Data Sources That Matter

While no AI company publishes the exact contents of their training data, the types of sources that influence AI recommendations follow clear patterns:

• Review platforms. Google Reviews, Yelp, industry-specific review sites. Not the star ratings themselves, but the text content of reviews that mentions businesses by name.
• Business directories. BBB, Angi, Healthgrades, Avvo, Clutch, and industry-specific directories. These create consistent associations between business names, locations, and services.
• Local news and media. Articles mentioning businesses in context of their community, industry, or achievements.
• Business websites. The content on your own site, especially if it clearly describes who you are, where you are, and what you do.
• Professional associations. Chamber of commerce listings, trade organization directories, professional certification bodies.
• Forums and community sites. Reddit, Quora, and niche forums where real people discuss and recommend businesses.

The common thread: businesses that are mentioned frequently, consistently, and across multiple independent sources are more likely to appear in AI recommendations.

What AI Cannot See

Just as important as what AI uses is what it cannot access:

• Anything behind a login. Private customer portals, internal dashboards, password-protected content.
• Real-time data. Today's Google reviews, this week's social media posts, your latest website update. AI models are trained on historical snapshots, not live feeds.
• Paid advertising. Your Google Ads, Facebook campaigns, and sponsored listings have zero influence on AI recommendations.
• Google Business Profile. Your GBP is a Google product. AI models do not have direct access to it.
• Your internal metrics. Customer satisfaction scores, retention rates, revenue - none of this is visible to AI.
• Engagement metrics. Follower counts, like counts, pageviews - AI does not factor these in.

This creates a gap. Your business might be excellent by every measure that matters. But if that excellence is not reflected in the publicly available text AI was trained on, the model has no way to know.

The Knowledge Cutoff Problem

Every AI model has a knowledge cutoff date - the point at which its training data stops. Anything that happened after that date does not exist in the model's world.

This means:

• A business that opened six months ago may be invisible to a model trained a year ago
• A rebranded business might still appear under its old name
• A negative review that was addressed and resolved may still influence the model's perception
• Awards, certifications, or media coverage from after the cutoff date have no effect

Different models have different cutoff dates. This is one reason why ChatGPT, Gemini, and Claude can give such different answers to the same question.

Each Model Sees Different Data

ChatGPT, Gemini, and Claude are not built on the same training data. Each company collects, filters, and processes internet data differently.

This means each model has a different picture of your business. One model might have strong data about you from review sites. Another might have picked up a news article. A third might have missed you entirely.

In our Denver Dentists study, the three models disagreed on which practices to recommend 100% of the time. Not once did all three models give the same answer. This is a direct result of each model being trained on different data.

What This Means for Your Business

Understanding what data AI uses leads to a few practical conclusions:

• Your website alone is not enough. AI looks at the entire internet, not just your domain. A broader web presence across directories, review sites, and publications creates more data points for AI to learn from.
• Consistency matters. If your business name, address, and service descriptions are inconsistent across platforms, AI may not connect all that information to one business.
• There is no shortcut. You cannot game AI training data. The path to AI visibility is having a genuine, well-established presence across the sources AI learns from.
• Monitoring is essential. Since you cannot control what AI sees, the most important thing you can do is monitor what it says about you. That starts with checking your visibility across all major models.

Read next: What Happens When AI Gets Your Business Wrong →