How AI Models Choose Which Brands to Recommend

When ChatGPT recommends a "best CRM for startups" or Perplexity compares project management tools, what determines which brands appear? Understanding these factors is crucial for improving your AI visibility.

How AI Models Form Opinions

AI language models don't have opinions in the human sense. Instead, they generate responses based on patterns learned from their training data and, for some models, real-time information retrieval.

Training Data Sources

Large language models like GPT-4 and Claude are trained on vast datasets including:

• Web content — Websites, blogs, documentation
• Publications — News articles, industry publications
• Reviews — Customer reviews, expert analyses
• Discussions — Forums, social media, community sites
• Books and papers — Published works in various fields

The relative weight of your brand across these sources influences how prominently you appear in AI responses.

Real-Time Retrieval

Some AI systems (like Perplexity and Google AI Overviews) retrieve current information from the web when generating responses. For these systems:

• Your current search rankings matter
• Recently published content can influence responses
• Links from authoritative sites increase your chances of citation

Factors That Influence Brand Mentions

1. Authority and Reputation

AI models tend to recommend brands that appear authoritative across multiple sources:

• Industry recognition — Awards, analyst coverage, press mentions
• Customer reviews — G2, Capterra, TrustRadius ratings
• Expert endorsements — Influencer recommendations, case studies
• Market position — Market share, longevity, customer base

What to do: Build genuine authority through customer success, industry participation, and thought leadership.

2. Content Quality and Coverage

Brands with comprehensive, helpful content are more likely to be recognized:

• Answer customer questions — Create content addressing common queries
• Comparison pages — Honest comparisons with competitors
• Documentation — Thorough product documentation
• Educational content — Industry insights and how-to guides

What to do: Create content that directly answers the questions your customers ask AI assistants.

3. Recency and Accuracy

AI models may prefer recent, accurate information:

• Updated content — Fresh content signals active presence
• Accurate information — Outdated info can lead to inaccurate recommendations
• Current features — AI should know about your latest capabilities

What to do: Regularly update your content, especially product pages and comparison content.

4. Contextual Relevance

AI models match recommendations to user intent:

• Use case alignment — Are you positioned for the specific use case asked about?
• Segment fit — Enterprise vs SMB, specific industries, team sizes
• Geographic relevance — Some queries are location-specific

What to do: Create content that explicitly addresses different use cases and customer segments.

5. Competitive Landscape

Your visibility is relative to competitors:

• Share of voice — How often you're mentioned vs alternatives
• Positioning — How you're described relative to competitors
• Feature comparison — How your capabilities stack up

What to do: Understand how AI positions you against competitors and address gaps.

Platform-Specific Differences

Different AI platforms may emphasize different factors:

ChatGPT

• Relies primarily on training data
• May have outdated information about recent products
• Influenced by prevalence in training corpus

Perplexity

• Uses real-time web retrieval
• Cites sources explicitly
• Current content and SEO matter more

Claude

• Trained on different data mix
• May have different knowledge cutoffs
• Emphasizes safety and accuracy

Google AI Overviews

• Integrated with Google Search
• Favors authoritative, well-structured content
• SEO signals heavily influence visibility

Gemini

• Google's conversational AI
• Connected to Google's knowledge graph
• May favor Google ecosystem content

Optimizing for AI Visibility

Based on these factors, here's how to improve your chances of being recommended:

1. Create Question-Answering Content

Structure content to directly answer the questions users ask:

• "What is the best X for Y?"
• "Compare X vs Z"
• "How to solve [problem]"

2. Build Authority Signals

Invest in genuine authority building:

• Customer reviews on major platforms
• Industry analyst coverage
• Press mentions and awards
• Thought leadership content

3. Optimize for Retrieval

For platforms with real-time retrieval:

• Maintain strong SEO fundamentals
• Create authoritative comparison content
• Build quality backlinks

4. Stay Current

Keep information accurate and up-to-date:

• Update product pages regularly
• Refresh comparison content
• Add new features and capabilities

5. Monitor and Iterate

Track what AI actually says about you:

• Use PromptFern to monitor AI responses
• Identify gaps and opportunities
• Adjust strategy based on data

The Bottom Line

AI models recommend brands based on aggregate signals of authority, relevance, and reputation. Unlike traditional SEO, you can't simply optimize for specific keywords—you need to build genuine authority across the sources AI models trust.

The good news: brands that genuinely serve customers well and communicate effectively tend to do well in AI recommendations. The key is understanding how your brand currently appears and systematically improving your presence.

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Want to see how AI models currently position your brand? Start monitoring with PromptFern to understand your AI visibility.