Artificial Intelligence is changing how people discover businesses.
Instead of searching Google and clicking through ten websites, many users now ask AI assistants like ChatGPT, Claude, Gemini, and Perplexity questions such as:
- “What’s the best digital marketing agency?”
- “Recommend the best web design company.”
- “Which SEO agency should I hire?”
- “Best restaurant near me.”
This raises an important question:
How do AI assistants decide which businesses to recommend?
Over the past several days, we conducted a structured investigation using official documentation, public resources, and direct conversations with multiple AI systems to separate facts from assumptions.
This article summarizes what we discovered.
Why We Started This Research
There are hundreds of articles claiming that AI recommends businesses based on:
- Backlinks
- Domain Authority
- Schema Markup
- Google Rankings
- Brand Mentions
- Reddit Discussions
- Reviews
However, very few of these articles cite official documentation from AI companies.
Rather than repeating existing opinions, we wanted to answer a simpler question:
What do AI companies officially disclose about business recommendations?
Our Research Method
To avoid speculation, we followed a structured process.
We investigated:
- Official OpenAI documentation
- Anthropic (Claude) documentation
- Google Gemini documentation
- Google Business documentation
- AI responses using identical prompts
- Independent comparisons across multiple AI models
Every claim was classified into one of four evidence levels:
- Official Documentation
- AI Self-Description
- Industry Analysis
- Hypothesis
This helped us distinguish verified information from educated guesses.
Key Finding #1: There Is No Public Ranking Formula
One of the clearest findings from our investigation is that none of the major AI companies publicly publish an exact business recommendation algorithm.
We found no official documentation stating that recommendations are based on a fixed formula such as:
- 30% Reviews
- 20% Backlinks
- 15% Domain Authority
- 10% Schema Markup
No provider publishes a weighting system like this.
Key Finding #2: AI Companies Publish Principles, Not Algorithms
Although exact ranking formulas remain undisclosed, AI companies consistently publish high-level principles.
Across the documentation we reviewed, common themes include:
- Understanding the user’s request
- Considering context and preferences
- Using available information when appropriate
- Providing helpful responses
- Separating recommendations from advertising where documented
These explain the goals of the system, not the internal ranking logic.
Key Finding #3: User Intent Matters More Than Generic Rankings
One of the strongest patterns across all AI models was personalization.
For example, the recommendation can change depending on:
- Budget
- Location
- Industry
- Experience level
- Personal preferences
- Follow-up questions
Instead of producing a universal “Top 10” list, AI assistants attempt to recommend businesses that best match the user’s specific needs.
Key Finding #4: Official Documentation and AI Explanations Are Different
During our investigation, we asked AI systems to separate official information from their own reasoning.
This revealed an important distinction.
Official Documentation
Published by the AI company.
Highest confidence.
AI Self-Description
The AI explaining how it believes it approaches recommendations.
Useful, but not independently verified.
Hypothesis
Reasonable explanations based on observed behavior.
Interesting, but not officially confirmed.
Understanding this difference is essential when researching AI optimization.
Key Finding #5: Many Popular GEO Claims Are Not Officially Confirmed
Many online articles confidently state that AI recommendations depend directly on:
- Backlinks
- Google Rankings
- Domain Authority
- Schema Markup
- Reddit Mentions
- Brand Mentions
Based on our research, we found no official documentation from OpenAI, Anthropic, or Google confirming these as direct ranking factors for business recommendations.
This does not mean these signals have no value.
It simply means there is currently no public evidence proving they are official AI ranking factors.
What We Know With Confidence
Based on official documentation and our investigation:
✅ AI recommendations are influenced by the user’s request.
✅ Context and personalization matter.
✅ Some AI systems can retrieve recent information when appropriate.
✅ Recommendations are intended to be helpful rather than advertiser-driven where officially documented.
What We Still Don’t Know
Despite extensive research, several important questions remain unanswered.
No major AI company publicly explains:
- The exact business recommendation algorithm
- Ranking weights
- Internal scoring systems
- Whether backlinks directly affect recommendations
- Whether domain authority directly influences recommendations
- Whether schema markup directly changes recommendations
These remain areas for future research.
Our Conclusion
The internet is full of advice about “ranking in AI.”
However, much of that advice is based on assumptions rather than official documentation.
Our investigation found that AI companies are transparent about their principles but do not publicly disclose the exact mechanics behind business recommendations.
Instead of chasing undocumented ranking formulas, businesses should focus on building genuine trust by:
- Publishing accurate information
- Maintaining consistent business profiles
- Creating useful, original content
- Earning authentic customer trust
- Being cited by reliable sources
As AI continues to evolve, long-term digital credibility is likely to matter more than attempting to optimize for undocumented algorithms.
Final Thoughts
This article is the first phase of an ongoing research project by Clone Gallery Research.
As new documentation, experiments, and evidence become available, we will continue updating our findings to help businesses understand how AI-powered discovery is evolving.
Research Note: This article distinguishes between official documentation, AI self-descriptions, industry analysis, and hypotheses. Where no official documentation exists, we intentionally avoid presenting speculation as fact.