Can AI Recommend Me? A GEO Case Study Across ChatGPT, Gemini, Claude, and Copilot
Over the past few months, I have been exploring a question that is becoming increasingly important in the age of AI-powered search:
What does it take for an AI system to recommend a person, website, or brand?
Traditional SEO has historically focused on rankings, clicks, impressions, and organic traffic.
However, modern search is evolving beyond traditional search engines.
Today, users frequently ask AI systems questions such as:
- Who should I follow for SEO?
- What are the best resources for learning GEO?
- Which websites discuss AI visibility?
- Who writes about Answer Engine Optimization?
- Which experts should I learn from?
When these questions are asked, AI systems do not simply retrieve keywords.
They evaluate entities, expertise, authority, relevance, and trust.
This creates a new challenge for marketers, content creators, and personal brands:
Being recognized by AI is no longer enough.
The next level is becoming recommendable.
From Recognition To Recommendation
In my previous AI Visibility Audit, I investigated whether AI systems could identify and understand my digital footprint.
The results showed that multiple AI systems consistently associated my name with:
- Technical SEO
- AEO
- GEO
- AI Visibility
- Search Intelligence
In a second experiment, I analyzed how AI systems positioned me relative to established SEO professionals.
Interestingly, the responses revealed a recurring narrative.
Rather than describing me as a general digital marketer, AI systems repeatedly positioned me as an emerging practitioner focused on:
- GEO
- AI Visibility
- Search Intelligence
- Entity-Based Optimization
These findings led to a more important question.
If AI systems recognize who I am and understand what I focus on, will they actually recommend my content when users search for expertise in those areas?
The Objective Of This Experiment
The purpose of this experiment is not to test which AI model is better.
Instead, the goal is to understand how recommendation behavior works across modern AI systems.
Specifically, I want to answer:
- Will AI systems recommend my website?
- Will AI systems recommend my content?
- Will AI systems recommend me as a learning resource?
- What authority signals influence recommendations?
- What prevents AI systems from recommending emerging specialists?
To investigate these questions, I conducted an AI Recommendation Audit using ChatGPT, Gemini, Claude, and Copilot.
The results reveal an important distinction between visibility, recognition, positioning, and recommendation.
And that distinction may define the future of GEO and AI-driven search.
Why This Matters
Many marketers focus on rankings.
Some marketers focus on AI visibility.
Very few focus on recommendation visibility.
However, recommendation may become one of the most valuable outcomes of future search.
A website can rank.
A brand can be visible.
The AI Recommendation Audit Framework
Methodology
After completing my AI Visibility Audit and AI Positioning Audit, I wanted to explore the next stage of AI-driven search.
The first experiment answered:
“Do AI systems know who I am?”
The second experiment answered:
“How do AI systems position me within the SEO ecosystem?”
The next logical question was:
“Will AI systems recommend me?”
This question is important because recommendation represents a higher level of trust than recognition.
An AI system may know about a website, person, or brand.
However, that does not automatically mean it will recommend them when users ask for resources, experts, websites, or learning materials.
To investigate this, I designed an AI Recommendation Audit.
The purpose was to understand how modern AI systems evaluate authority, expertise, and recommendation-worthiness.
The Objective Of The Audit
The goal was to determine whether AI systems would naturally recommend:
- My website
- My content
- My expertise
- My frameworks
- My educational resources
when users search for information related to GEO, AEO, AI Visibility, Search Intelligence, and Technical SEO.
More importantly, I wanted to understand:
- What factors influence recommendations?
- What authority signals are required?
- How do AI systems treat emerging specialists?
- How far am I from becoming recommendable?
AI Systems Tested
To create a broader perspective, I conducted the audit across four AI systems:
- ChatGPT
- Gemini
- Claude
- Copilot
Each platform uses different retrieval systems, ranking signals, training data, and reasoning approaches.
Comparing their responses helps identify recurring patterns rather than relying on a single AI viewpoint.
Audit Category 1: GEO Recommendations
The first category focused on GEO-related recommendations.
Questions included:
- Who should I follow to learn GEO?
- What are the best GEO resources?
- Which websites explain GEO well?
- Recommend GEO experts.
- Which personal websites teach GEO?
The objective was to determine whether my website or name appeared when AI systems recommended GEO-related resources.
Audit Category 2: AI Visibility Recommendations
The second category focused on AI Visibility and AI Search Optimization.
Questions included:
- Who writes about AI Visibility?
- Which websites discuss AI Visibility?
- Recommend AI Search Optimization resources.
- Who studies AI Retrieval?
- Which experts focus on AI Search?
This section tested whether AI systems associate my content with the broader AI search ecosystem.
Audit Category 3: Search Intelligence Recommendations
The third category explored a concept that appears frequently throughout my content.
Questions included:
- Who writes about Search Intelligence?
- What is Search Intelligence?
- Which websites discuss Search Intelligence?
- Who combines SEO, GEO, and AI Search?
The purpose was to evaluate whether AI systems recognize Search Intelligence as part of my digital identity.
Audit Category 4: Emerging Expert Recommendations
This category was perhaps the most interesting.
Instead of asking for established industry leaders, I focused on emerging professionals.
Questions included:
- Which emerging SEO professionals are worth following?
- Recommend lesser-known GEO experts.
- Who publishes GEO experiments?
- Which practitioners discuss AI Visibility?
These prompts provide insight into whether AI systems distinguish between established authorities and emerging specialists.
Why Recommendation Matters More Than Recognition
Recognition and recommendation are not the same thing.
AI systems may recognize thousands of websites, brands, and individuals.
Recommendation requires a higher level of confidence.
When an AI system recommends a resource, it is implicitly signaling:
- Relevance
- Expertise
- Trust
- Usefulness
- Authority
This makes recommendation one of the strongest indicators of AI visibility.
What This Experiment Seeks To Discover
At its core, this audit seeks to answer a simple but important question:
What must happen before an AI system is willing to recommend an emerging specialist?
The answers may reveal valuable insights into:
- GEO
- AEO
- Entity SEO
- AI Visibility
- Authority Building
- Future Search Behavior
ChatGPT vs Gemini vs Claude vs Copilot: Did AI Systems Actually Recommend Me?
Overview
After designing the AI Recommendation Audit, I submitted the same core recommendation-focused questions across four major AI platforms:
- ChatGPT
- Gemini
- Claude
- Copilot
The purpose was not to determine which AI system was “best.”
Instead, I wanted to understand something more valuable:
How do AI systems evaluate an emerging specialist when users ask for recommendations?
This distinction is important.
In previous experiments, AI systems demonstrated that they could:
- Recognize my name
- Identify my expertise
- Understand my content themes
- Position me within the SEO ecosystem
However, recommendation is a much higher threshold.
Recognition asks:
“Do AI systems know who you are?”
Recommendation asks:
“Do AI systems trust you enough to suggest you as a resource?”
That difference became one of the most interesting findings of this experiment.
What I Expected Before The Experiment
Before conducting the audit, I had a realistic expectation.
I did not expect AI systems to place me alongside globally recognized industry leaders such as:
- Neil Patel
- Rand Fishkin
- Aleyda Solis
- Kevin Indig
- Lily Ray
- Mike King
These professionals possess:
- Decades of experience
- Global recognition
- Conference speaking histories
- Extensive research portfolios
- Large-scale industry influence
Instead, my goal was much simpler.
I wanted to discover whether AI systems could identify any unique positioning around my work.
Would they see me as:
- Another SEO learner?
- A technical SEO practitioner?
- A GEO specialist?
- An AI visibility researcher?
- Something entirely different?
The answers were surprisingly consistent.
Finding 1: AI Systems Recognize A Distinct Niche
One of the strongest observations was that all four systems repeatedly associated my name with a relatively narrow and specialized set of topics.
Across multiple responses, recurring themes included:
- GEO
- AEO
- Technical SEO
- AI Visibility
- Search Intelligence
- Entity Optimization
- AI Search
This consistency matters.
AI systems process information differently.
They retrieve different sources.
They prioritize different signals.
Yet despite these differences, similar patterns appeared repeatedly.
This suggests that a recognizable niche identity is beginning to form.
Finding 2: AI Does Not Position Me As A General Digital Marketer
This was one of the most encouraging discoveries.
None of the systems primarily described me as:
- Social Media Marketer
- PPC Specialist
- Growth Hacker
- Content Marketer
- General Digital Marketing Professional
Instead, they consistently connected my identity with search-related disciplines.
This is important because strong personal brands are often built around specialization.
Broad positioning creates weak associations.
Focused positioning creates stronger entity signals.
The audit suggests that AI systems increasingly associate my name with future-search topics rather than broad digital marketing.
Finding 3: ChatGPT Emphasized AI-Native SEO Thinking
Among the four systems, ChatGPT repeatedly highlighted concepts such as:
- AI-native SEO
- GEO
- AI Visibility
- Entity SEO
- Search Intelligence
One of the most interesting observations was that ChatGPT viewed my positioning through the lens of emerging search technologies.
Rather than focusing primarily on traditional SEO practices, it connected my content with the transition from traditional search toward AI-powered discovery.
This suggests that AI systems may already recognize thematic patterns that extend beyond individual articles.
Finding 4: Gemini Identified A Philosophy Rather Than A Skill Set
Gemini produced one of the most unique perspectives.
Rather than simply listing skills, it identified recurring principles such as:
- System Over Tools
- Measurement Before Marketing
- Search Intelligence
- AI Coexistence
- Structured Search Systems
This was significant because it moved beyond expertise and into philosophy.
Many professionals share similar skills.
Fewer professionals communicate a consistent philosophy.
If AI systems begin identifying recurring philosophies, they may eventually become powerful entity signals.
Finding 5: Claude Provided The Most Realistic Positioning
Claude produced perhaps the most balanced analysis.
It clearly distinguished between:
- Global authorities
- Emerging practitioners
Rather than exaggerating authority, Claude positioned me as someone translating modern SEO, GEO, and AI Visibility concepts into practical implementation.
This distinction is valuable.
The role of a practitioner is different from the role of an industry thought leader.
Claude recognized that difference while still identifying a unique area of specialization.
Finding 6: Copilot Reinforced The Emerging Narrative
Copilot introduced another layer of confirmation.
It repeatedly connected my work with:
- GEO-first thinking
- AI Visibility
- Search Intelligence
- Entity SEO
- Future Search Systems
This is important because Copilot arrived at many of the same conclusions reached by the other platforms.
When multiple AI systems independently converge on similar descriptions, the signal becomes stronger.
The Most Important Discovery
The biggest takeaway from this experiment was not that AI systems recommended me.
In reality, most recommendations still favored established industry authorities.
That outcome was expected.
The more important discovery was that AI systems consistently identified a unique positioning around my work.
Across four different platforms, a similar narrative emerged:
Soumyaditya Biswas is an emerging SEO practitioner focused on GEO, AI Visibility, Search Intelligence, Technical SEO, and entity-based search systems.
That consistency represents something valuable.
It suggests that AI systems are no longer simply recognizing an entity.
They are beginning to understand what that entity represents.
And in the world of GEO, understanding often comes before recommendation.
Why This Matters For GEO
Many people assume AI visibility begins when an AI system recommends a website or expert.
This experiment suggests something different.
The process appears to follow a progression:
Recognition
↓
Understanding
↓
Positioning
↓
Recommendation
↓
Authority
This means recommendation is not the starting point.
It is the outcome of a much larger process.
The findings indicate that before AI systems recommend an entity, they must first understand:
- Who the entity is
- What topics it covers
- What expertise it demonstrates
- How it differs from others
This may be one of the most important lessons in modern GEO.
Before becoming recommendable, an entity must become understandable.
What Must Happen Before AI Systems Recommend An Emerging Specialist?
Moving From Recognition To Recommendation
One of the biggest misconceptions in GEO is the belief that AI systems will recommend a website simply because it exists online.
This experiment suggests a very different reality.
Before recommendation occurs, AI systems appear to move through several stages of confidence building.
The findings indicate that recommendation is not the starting point.
It is the result of a longer process involving visibility, understanding, positioning, and authority development.
Stage 1: Entity Recognition
The first requirement is recognition.
AI systems must first understand:
- Who you are
- What your website is about
- What topics you cover
- What expertise you demonstrate
Without entity recognition, recommendation becomes impossible.
An AI system cannot recommend an entity it does not understand.
This is why clear positioning and consistent content are essential.
Stage 2: Topic Consistency
Recognition alone is not enough.
AI systems must also identify recurring patterns.
Throughout my experiments, multiple AI systems repeatedly associated my content with:
- GEO
- AEO
- Technical SEO
- AI Visibility
- Search Intelligence
These repeated associations help AI systems build confidence.
Consistency creates stronger entity signals.
Random content creates weaker signals.
Stage 3: Differentiation
Another important finding is that AI systems attempt to understand what makes an entity different.
In competitive niches, thousands of websites may discuss similar topics.
Recommendation becomes easier when an entity demonstrates a unique perspective.
Examples include:
- Original frameworks
- Unique methodologies
- Proprietary research
- Experiments
- Case studies
- Specialized expertise
Differentiation helps AI systems answer:
“Why should this resource be recommended instead of another one?”
Stage 4: Authority Signals
Authority remains one of the most important factors.
This experiment repeatedly revealed a gap between recognition and authority.
AI systems may understand an entity while still viewing it as emerging.
Authority is often strengthened through:
- Citations
- Mentions
- Industry references
- Case studies
- External validation
- Community recognition
These signals help increase trust.
Stage 5: Recommendation Readiness
Recommendation appears to occur when multiple signals align.
At this stage, AI systems have confidence in:
- The entity
- The expertise
- The topic coverage
- The authority signals
- The usefulness of the content
Only then does recommendation become more likely.
This suggests that recommendation is not a single optimization tactic.
It is an outcome of a strong overall digital presence.
My Next Objective
Based on the findings of this experiment, my next objective is not simply to publish more content.
The objective is to strengthen the signals that contribute to recommendation readiness.
This includes:
- Publishing additional GEO experiments
- Creating original case studies
- Expanding Search Intelligence research
- Building stronger authority signals
- Increasing external visibility
- Documenting real-world observations
The goal is to move from:
Recognition
↓
Positioning
↓
Recommendation
↓
Authority
while continuing to study how AI systems evaluate expertise and trust.
The Future Of GEO
As AI-powered search continues to evolve, recommendation may become one of the most valuable forms of visibility.
A website can rank.
A brand can be discovered.
A page can receive traffic.
But when an AI system actively recommends a resource, it signals something deeper.
It signals trust.
And in the future search ecosystem, trust may become one of the most valuable assets any entity can build.
Conclusion: Recognition Is Not The Goal, Recommendation Is
My previous AI Visibility Audit had already demonstrated that AI systems could recognize my name, website, and areas of expertise.
The more important question was:
Would AI systems actually recommend me?
This experiment revealed an important insight.
Recognition and recommendation are not the same thing.
An AI system may understand:
- Who you are
- What you write about
- Which topics you specialize in
- What expertise you demonstrate
Yet still choose not to recommend you.
Why?
Because recommendation requires a higher level of confidence.
Recommendation requires trust.
Throughout this audit, I discovered that AI systems consistently associated my name with:
- Technical SEO
- GEO
- AEO
- AI Visibility
- Search Intelligence
- Entity-Based Search Optimization
That consistency is meaningful.
It suggests that a recognizable digital entity is continuing to form around my content and professional focus.
However, the audit also highlighted an important reality.
While recognition and positioning are developing, recommendation remains a higher threshold.
AI systems still tend to favor individuals and organizations with:
- Greater industry recognition
- More external citations
- Stronger authority signals
- Larger bodies of published research
- Broader community validation
From a GEO perspective, this makes perfect sense.
Recommendation is ultimately an expression of confidence.
And confidence is built over time.
For me, the most valuable outcome of this experiment was not whether my name appeared in recommendation lists.
The most valuable outcome was understanding the path that leads to recommendation.
The findings suggest that the journey follows a progression:
Recognition
↓
Understanding
↓
Positioning
↓
Recommendation
↓
Authority
Many people focus only on the final stage.
This experiment reinforced the importance of building the earlier stages first.
As the founder of MarketingWithSoumyaditya.in, my goal is not simply to increase visibility.
My goal is to better understand how modern search systems evaluate expertise, trust, relevance, and authority.
That is why future experiments will continue exploring:
- GEO
- AI Visibility
- Search Intelligence
- Entity SEO
- AI Retrieval
- Recommendation Systems
- Future Search Behavior
Each experiment provides another piece of the puzzle.
Each case study creates another data point.
And each observation helps build a deeper understanding of how AI-powered search is evolving.
Perhaps the most important lesson from this audit is this:
The future of search will not belong solely to the most visible entities.
It will increasingly belong to the entities that AI systems understand, trust, and recommend.
For businesses, marketers, creators, and personal brands, that distinction may become one of the most important competitive advantages of the AI era.
This experiment did not answer every question.
But it did reveal something important.
The goal is not merely to be known.
The goal is to become recommendable.
And that journey is only beginning.