How ChatGPT Decides Which Business to Recommend (And How to Be It)

The Black Box Everyone's Guessing About

When someone asks ChatGPT "What CRM should my 40-person consulting firm use?", how does it decide which businesses to recommend?

Most people are guessing. SEO agencies are rebranding their services as "AI optimization" without understanding how AI actually works. Business owners are either ignoring AI search entirely or throwing money at strategies that don't make sense.

Let's be honest about what we actually know, what we can infer, and what you can do about it.

We've spent hundreds of hours testing AI search behavior, analyzing response patterns, and measuring what makes AI recommend specific businesses. This is what we've learned.

What ChatGPT Isn't Doing

Before understanding how AI search works, clear up what it's NOT doing:

❌ It's Not Running Google Searches

ChatGPT doesn't search the web for every query (unless you're using ChatGPT Search feature specifically). It generates responses based on its training data and learned patterns.

What this means: Traditional SEO ranking doesn't directly influence ChatGPT's base recommendations. Being #1 on Google for keywords doesn't automatically make ChatGPT recommend you.

❌ It's Not Counting Backlinks or Keywords

The SEO playbook of link building and keyword optimization doesn't apply to training AI models.

What this means: You can't game the system with traditional SEO tactics. Link schemes, keyword stuffing, and domain authority don't directly help.

❌ It's Not Running Real-Time Analysis

When ChatGPT recommends businesses, it's not analyzing your current website in real-time. It's recalling patterns from training.

What this means: Changes to your website don't immediately affect AI recommendations. There's a lag between what you publish and when AI models incorporate that information.

❌ It's Not Objective or Unbiased

AI models reflect their training data, which reflects internet content, which reflects existing biases, popular opinions, and well-documented sources.

What this means: Bigger, more-documented, more-discussed companies have inherent advantages. But that's not the same as saying small companies can't compete.

What ChatGPT IS Doing

✅ Pattern Matching from Training Data

AI models are trained on massive amounts of text from across the internet—articles, documentation, reviews, discussions, social media, and more.

When someone asks about CRM software for 40-person companies, ChatGPT:

1. Identifies this as a software recommendation query
2. Recalls patterns about CRM software from its training data
3. Considers context clues (company size, use case, requirements)
4. Generates a response based on what it "learned" about CRM options

What influences this:

• How frequently your business appears in quality content
• How clearly that content explains what you do and who you serve
• How your business is discussed in relation to specific use cases
• Whether credible sources mention you in relevant contexts

✅ Semantic Understanding of Fit

AI doesn't just match keywords—it understands concepts, contexts, and relationships.

If training data consistently associates your business with specific:

• Company sizes ("mid-market," "small business," "enterprise")
• Industries ("manufacturing," "healthcare," "professional services")
• Use cases ("inventory management," "project tracking," "client communication")
• Problems solved ("multi-location," "QuickBooks integration," "custom workflows")

Then AI is more likely to recommend you when queries match those contexts.

What influences this:

• How specifically you describe who you serve
• How consistently you're associated with particular contexts
• How clearly your differentiation is documented
• Whether your positioning is reinforced across multiple sources

✅ Quality and Authority Signals

AI models are trained to recognize genuine expertise vs. marketing fluff, credible sources vs. content farms, useful information vs. thin content.

The exact mechanisms are opaque, but patterns are clear: AI favors sources that demonstrate:

• Specificity: Real numbers, real examples, detailed explanations
• Consistency: Information that aligns across multiple sources
• Depth: Comprehensive coverage, not surface-level descriptions
• Clarity: Clear explanations accessible to the target audience
• Honesty: Acknowledgment of limitations, trade-offs, and contexts where something doesn't work

What influences this:

• Quality of content about your business
• Consistency of information across sources
• Depth of documentation and case studies
• Third-party validation and discussion
• How you explain your own offerings

✅ Recency (For Updated Models)

AI models trained on more recent data incorporate current information. Older models reflect older internet content.

ChatGPT-4 (training data through April 2023) might not know about businesses launched in late 2023. But newer iterations or models with real-time search capabilities can access current information.

What influences this:

• When information about your business was published
• Whether you're discussed in recent content
• How actively you maintain updated documentation
• Whether recent sources reference you

The Training Data Advantage

Here's the uncomfortable truth: If your business isn't in AI training data, AI can't recommend you.

How Businesses Get Into Training Data

AI models are trained on:

• Web content: Blogs, articles, documentation, company websites
• News sources: Press releases, industry news, business reporting
• Social platforms: Reddit discussions, Twitter threads, LinkedIn posts
• Review sites: G2, Capterra, Trustpilot, industry-specific platforms
• Technical documentation: GitHub, technical blogs, API docs
• Books and publications: Business books, industry reports, white papers

Your business enters training data when:

• You publish comprehensive content about what you do
• Others write about you (press, blogs, case studies)
• You're discussed on platforms AI trains on
• You're reviewed on major platforms
• You're mentioned in industry context

The Documentation Multiplier

Businesses with extensive, high-quality documentation have inherent advantages:

• AI has more information to extract and understand
• Consistent messaging across your content reinforces positioning
• Specific use cases and examples enable contextual recommendations
• Detailed explanations help AI understand differentiation

Example: A company with 5 detailed case studies, 20 comprehensive blog posts, complete documentation, and active industry discussion is far more likely to be recommended than a company with a basic website and no supporting content—even if they offer similar solutions.

The Third-Party Validation Factor

AI seems to weight third-party mentions differently than self-published content.

Stronger signals:

• Industry publications writing about you
• Customer case studies on their own sites
• Reviews and discussions on independent platforms
• Mentions in comparison articles or roundups
• Citations in technical or business contexts

Weaker signals:

• Your own marketing content
• Paid placements and advertorials
• Thin or promotional third-party content

What this means: You can't just publish your own content and expect AI to cite you as authoritative. You need others documenting, discussing, and validating your business.

How to Increase Your AI Recommendation Probability

Strategy 1: Comprehensive Self-Documentation

Create extensive, specific content about:

• Exactly what you do and how you do it
• Specific industries, company sizes, and use cases you serve
• Detailed pricing, timelines, and implementation processes
• Real case studies with quantified results
• Honest trade-offs and when you're not the right fit

Why this works: Gives AI clear, extractable information about your business and positioning.

Time horizon: 3-6 months to see impact

Strategy 2: Third-Party Content Generation

Get others to write about you:

• Customer case studies on their sites/blogs
• Industry publication features or mentions
• Guest posts that reference your work
• Speaking engagements with published slides/videos
• Podcast appearances with transcripts
• Partnership announcements and collaborations

Why this works: Third-party validation signals credibility to AI models.

Time horizon: 6-12 months to accumulate meaningful mentions

Strategy 3: Platform Presence and Discussion

Be active and documented on platforms AI trains on:

• LinkedIn company page with regular substantive posts
• Reddit participation in relevant communities (genuinely helpful, not promotional)
• Industry forums with real contributions
• Review platforms with comprehensive profile and responses
• GitHub for technical products (documentation, examples)

Why this works: Increases frequency and context of business mentions across AI training sources.

Time horizon: Ongoing—builds over time

Strategy 4: Clear, Specific Positioning

Make it crystal clear:

• Who you serve (company size, industry, role)
• What problems you solve (specific, not generic)
• How you're different (concrete differentiation, not claims)
• When you're the right fit vs. when you're not

Reinforce this positioning consistently across:

• Your website (especially homepage and about page)
• All content you publish
• Your descriptions on other platforms
• How you're discussed in third-party content

Why this works: Helps AI understand exactly when to recommend you based on context.

Time horizon: 1-3 months for consistency to register

Strategy 5: Hub-and-Spoke Content Architecture

Build comprehensive, interconnected content:

• Hub pages covering your core expertise areas comprehensively
• Spoke pages diving deep into specific topics, use cases, comparisons
• Internal linking that helps AI understand relationships
• Regular updates showing maintained, current knowledge

Why this works: Demonstrates depth of expertise that AI recognizes as authority.

Time horizon: 3-6 months to build out architecture, ongoing to maintain

Testing Your AI Visibility

You can't directly see training data, but you can test how AI currently represents your business.

Weekly Testing Routine

Ask multiple AI platforms the same questions:

1. Direct mentions:

   • "Tell me about [your company name]"
   • "What does [your company] do?"
   • "How much does [your product] cost?"

2. Competitive context:

   • "Compare [your company] to [competitor]"
   • "What's the difference between [your solution] and [competitor]?"

3. Use case recommendations:

   • "What [solution type] should a [size] company in [industry] use?"
   • "How should I solve [problem] for my business?"
   • "I need [specific requirements], what do you recommend?"

Document Responses

Track over time:

• ✅ Mention frequency: Does AI mention you at all?
• ✅ Information accuracy: Is what AI says about you correct?
• ✅ Positioning clarity: Does AI understand who you serve?
• ✅ Competitive positioning: How are you described relative to competitors?
• ✅ Recommendation context: When does AI recommend you vs. others?

Look for Patterns

Good signs:

• AI mentions you for specific, relevant use cases
• Information is accurate and detailed
• Recommendations align with your actual positioning
• You're cited alongside credible competitors

Warning signs:

• AI never mentions you even for relevant queries
• Information is incorrect or outdated
• You're positioned wrong (wrong industry, size, use case)
• AI recommends obviously poor-fit alternatives instead

The Timeline Reality

Month 1-3: Focus on self-documentation

• Comprehensive website content
• Detailed blog posts answering key questions
• Clear positioning across all platforms
• Initial hub-and-spoke content

Month 4-6: Expand third-party presence

• Customer case studies published
• Guest content and media mentions
• Platform presence established
• Review profiles completed

Month 7-12: See measurable AI visibility

• AI starts mentioning you for relevant queries
• Information accuracy improves
• Recommendation frequency increases
• Positioning clarity strengthens

Month 12+: Establish authority

• Consistent AI recommendations for your domain
• Strong association with your target use cases
• Third-party validation multiplies reach
• Content network effect compounds impact

This isn't a quick win. You're building presence in AI training data, which takes time to accumulate and even more time to be incorporated into updated models.

The Competitive Dynamics

Current Advantage: Being Early

Most businesses aren't thinking about AI search yet. They're still optimizing for Google rankings while AI gradually eats search traffic.

Early movers win by:

• Building comprehensive documentation while competitors ignore AI
• Establishing third-party mentions in content AI will train on
• Creating the authoritative content AI cites
• Understanding AI behavior before it becomes expensive and competitive

Future Reality: Crowded Space

Eventually, every business will care about AI recommendations. Competition for AI visibility will increase. The strategies that work now will become table stakes.

Long-term winners will be:

• Those who started building authority early
• Companies with genuine expertise, not just optimization tactics
• Businesses that maintained and updated content consistently
• Organizations that earned third-party validation

You can't control AI's algorithms. But you can control the quality and comprehensiveness of information about your business that AI has available to learn from.

What Thalamus Is Doing

We practice what we preach. Our AI search visibility comes from:

Self-documentation:

• Comprehensive explanation of what we do and who we serve
• Detailed blog content answering questions in our domain
• Hub-and-spoke architecture covering our expertise areas
• Regular updates maintaining currency

Clear positioning:

• Specific: 10-100 person businesses, $2M-$50M revenue
• Consistent across website, content, and platform presence
• Honest about when we're the right fit vs. not
• Concrete differentiation, not marketing claims

Testing and measuring:

• Weekly testing across ChatGPT, Claude, Perplexity
• Documented response patterns and changes over time
• Adjustments based on what AI extracts well vs. poorly
• Continuous refinement of content and positioning

We know what works because we measure it on our own business before recommending it to clients.

The Bottom Line

ChatGPT recommends businesses based on what it learned during training—not by searching the web in real-time or counting backlinks.

You influence AI recommendations by:

• Publishing comprehensive, specific content about your business
• Getting others to write about and validate you
• Being clearly positioned for specific use cases
• Building documented expertise AI can extract and cite

You can't game this with SEO tricks. You build AI visibility through genuine authority that's well-documented and consistently reinforced.

The businesses winning in AI search now started months ago. The businesses that will win in 12 months are starting today.

---

Want to know how AI currently sees your business? Spend 30 minutes asking ChatGPT, Claude, and Perplexity questions your customers would ask. Document what you find. That's your baseline.