When someone asks ChatGPT, “What’s the best company for this?” or “Which service should I choose?”, getting mentioned is valuable. But being the first company ChatGPT recommends can be even more powerful.
That naturally leads to an important question:
Can a company actually make ChatGPT recommend it first?
The honest answer is no—not in the traditional sense of buying a top position or following a secret optimization trick.
ChatGPT does not offer a normal “position #1” advertising slot for recommendations. Its shopping and recommendation systems consider the user’s intent, context, available information, product or company details, reviews, third-party sources, price, availability, quality, and other signals. OpenAI says product results are selected independently and are not ads.
So the better goal is not simply:
“How do I manipulate ChatGPT into putting my company first?”
It is:
How do I become the company that has the strongest evidence and relevance for the exact question my potential customer is asking?”
That change in thinking is the foundation of modern AI search visibility.
What Does First Company Recommended Actually Mean?
There are several different outcomes that businesses sometimes confuse.
A company might:
- appear somewhere in a ChatGPT answer
- be cited as a source
- be included in a shortlist
- be recommended as one of the better options
- be recommended near the top
- be the first company mentioned
- be the final recommendation for a particular buyer’s needs
These are not the same thing.
For example, imagine someone asks:
“What are the best AI SEO agencies for a U.S. SaaS company?”
ChatGPT could potentially respond with several companies and explain the strengths of each.
Your objective might therefore be to move from:
Not mentioned → Mentioned → Shortlisted → Recommended → Frequently first-mentioned
That is a much more realistic way to measure progress than assuming there is one permanent “#1 ranking.”
AI answers can also change from one run to another. They can vary with the wording of the question, the user’s requirements, the information available on the web, location, freshness, and model changes.
Even LinkinGrow’s own measurement approach recognizes this variability by sampling buyer questions repeatedly rather than treating one screenshot as proof of visibility.
Why ChatGPT Doesn’t Simply Rank Companies Like Google
Traditional search engines generally return a ranked list of pages.
Generative AI systems work differently.
When a person asks a recommendation question, the system can interpret the request, retrieve information, compare alternatives, and generate an answer.
OpenAI’s current shopping documentation says product selection considers the user’s query and context along with information such as structured product metadata, product descriptions, reviews, price, and other third-party information.
ChatGPT’s newer shopping research experience can also perform multi-step product discovery, using merchant product information, publicly available product information, and other retail sources to compare options.
That means the company with the strongest traditional SEO ranking isn’t automatically guaranteed to become the first recommendation.
Instead, you need to build a credible evidence footprint around the questions your customers ask.
1. Start With the Buyer Question, Not Your Company Name
One of the biggest mistakes businesses make is optimizing for their brand name.
But customers rarely ask:
“Tell me about Company X.”
They ask:
- “What’s the best accounting software for a small business?”
- “Which cybersecurity company is best for healthcare?”
- “What’s the best marketing agency for SaaS startups?”
- “Which CRM is easiest for a 10-person sales team?”
- “What are the best alternatives to Salesforce?”
- “Which AI SEO agency can help my company appear in ChatGPT?”
- “What company should I hire for local SEO?”
Those questions define the competitive environment.
If you want to become the first company recommended for a specific question, start by documenting the exact questions buyers ask.
Build a buyer-question database
Create categories such as:
Category questions
“What are the best [service] companies?”
Comparison questions
“Company A vs Company B?”
Alternative questions
“What are the best alternatives to [competitor]?”
Use-case questions
“What is the best [service] for a small business?”
Budget questions
“What is the best [service] under $X?”
Location questions
“What are the best [service providers] in New York?”
Problem-based questions
“How can I solve [specific problem]?”
This creates a much more useful AI visibility strategy than simply publishing hundreds of pages targeting generic keywords.
2. Make Your Website Easy for AI Systems to Understand
Before worrying about authority, make sure your own website clearly explains what your company actually does.
A surprisingly large number of businesses make this difficult.
Your homepage should quickly communicate:
- what you sell
- who you serve
- what problem you solve
- where you operate
- what makes your solution different
- which use cases you support
- how customers can evaluate you
Your product and service pages should go deeper.
For example, instead of writing:
“We provide innovative solutions for modern businesses.”
Say:
“We help U.S. SaaS companies improve their visibility in AI-generated answers across ChatGPT, Google AI experiences, Claude, and Perplexity.”
The second statement gives both humans and machines considerably more useful context.
Technical SEO Still Matters
AI visibility does not replace technical SEO.
Google’s current guidance for AI features specifically says existing SEO fundamentals remain important. Pages need to be crawlable and indexable, important content should be available in text, internal linking matters, and structured data should match the visible content. Google also says there is no special “AI schema” required to appear in its AI features.
OpenAI likewise says publishers can help their content be discovered and surfaced in ChatGPT by ensuring they do not block OAI-SearchBot.
So your basic checklist should include:
- crawlable pages
- sensible robots.txt rules
- indexable important pages
- strong internal linking
- descriptive page titles
- useful headings
- readable text
- accurate structured data
- fast, usable pages
- consistent company information
- current product and service details
You don’t need a mysterious “ChatGPT ranking code.”
You need a website that is accessible, understandable, and useful.
3. Build Evidence Outside Your Own Website
This may be one of the most important differences between traditional SEO and AI recommendation visibility.
Your company saying:
“We are the best.”
is weak evidence.
Other credible sources independently describing your company, expertise, products, results, or category relevance can be much more useful.
Think about the information ecosystem around your brand.
It can include:
- industry publications
- independent reviews
- expert interviews
- podcasts
- YouTube videos
- customer case studies
- reputable directories
- community discussions
- comparison articles
- partner websites
- professional organizations
- research
- press coverage
- product databases
- relevant forums and communities
Recent GEO research has also pointed toward the importance of third-party and earned media in AI search visibility. One 2025 study found substantial differences between AI search and traditional Google search in the sources used, with earned media playing a particularly important role in its experiments.
The practical lesson is simple:
Don’t build your entire AI visibility strategy on your own website.
Build a reputation that exists across the web.
4. Become the Best Answer for a Narrow Category First
Trying to become “the best company” for an enormous category is usually unrealistic.
Suppose your company sells marketing software.
You probably won’t immediately become the first recommendation for:
“What’s the best marketing software?”
That’s too broad.
Instead, identify a narrower question where your company has a genuine advantage.
For example:
“What’s the best AI visibility platform for B2B SaaS companies?”
or:
“What are the best tools for monitoring brand mentions in AI search?”
A narrower category gives you an opportunity to build stronger relevance.
Then expand.
Think of it as:
Narrow authority → category authority → broader recommendation visibility
5. Create Content That Helps Someone Make a Decision
Generic blog posts aren’t enough.
If your goal is recommendation visibility, your content should help someone evaluate choices.
Instead of publishing:
“What Is AI Marketing?”
create content such as:
“AI Visibility Platforms: What Should a B2B Company Compare?”
Or:
“How to Measure Whether Your Brand Appears in AI Recommendations”
Or:
“AI Search Visibility vs Traditional SEO: What Businesses Should Track”
Good decision-oriented content should explain:
- who a solution is for
- who it isn’t for
- important features
- limitations
- pricing considerations
- implementation requirements
- alternatives
- trade-offs
- realistic results
- questions buyers should ask
This gives AI systems more useful material to work with while simultaneously creating better content for humans.
Bing’s current AI Performance guidance similarly recommends aligning content with user intent, improving depth and clarity, supporting claims with evidence, keeping content fresh, and maintaining consistency across formats.
6. Don’t Manufacture Reviews
This deserves special attention.
If someone tells you:
We’ll create hundreds of positive reviews so ChatGPT recommends your company.”
Be extremely careful.
Fake reviews aren’t a legitimate AI optimization strategy.
In the United States, the FTC’s Consumer Reviews and Testimonials Rule addresses fake or false consumer reviews and testimonials, including reviews that misrepresent someone as having experience they did not actually have. The rule also addresses buying reviews and certain incentives tied to review sentiment.
There is also a broader strategic problem.
If your entire AI visibility strategy depends on fabricated evidence, you’re building something fragile.
A better approach is to earn genuine evidence:
- real customer experiences
- legitimate reviews
- transparent case studies
- original research
- expert commentary
- independent coverage
- authentic community participation
The goal isn’t to create the appearance of authority.
The goal is to actually become easier to trust.
7. Make Your Brand Consistent Everywhere
Imagine your website says:
LinkinGrow is an AI Answer Engine Optimization platform.
But a directory calls you:
LinkinGrow SEO Agency
A podcast describes you as:
An AI marketing consultant
And another website says:
LinkinGrow is a traditional SEO company.
That creates unnecessary ambiguity.
Your:
- company name
- category
- products
- services
- descriptions
- founders
- locations
- URLs
- social profiles
- product names
should be consistent wherever practical.
Consistency helps humans understand your company—and gives search and AI systems clearer signals about the entity they’re encountering.
8. Give AI Systems Specific Facts to Work With
AI models cannot reliably recommend what they cannot understand.
Suppose you sell software.
Don’t just say:
“Powerful AI platform.”
Explain:
- what the product does
- who it is designed for
- supported industries
- integrations
- pricing model
- key features
- limitations
- geographic availability
- implementation process
- customer profile
- alternatives
- measurable outcomes
For physical products, OpenAI says product discovery can use information such as structured metadata, descriptions, reviews, price, and availability.
For service businesses, the same principle applies conceptually:
Give the information ecosystem enough accurate detail to understand why your company belongs in a particular recommendation set.
9. Build Original Evidence Instead of Rewriting Everyone Else
One of the strongest ways to become more useful to AI systems is to publish information other websites don’t already have.
For example:
Publish original research
Survey 500 customers.
Publish the methodology and findings.
Publish benchmarks
Measure real-world performance across a meaningful dataset.
Publish transparent case studies
Explain:
- starting point
- strategy
- implementation
- timeframe
- results
- limitations
Publish expert analysis
Don’t just repeat industry news.
Explain what it means for buyers.
Publish comparison frameworks
Teach people how to evaluate competing solutions.
Original information gives other publishers something worth citing.
And citations create additional evidence around your brand.
10. Optimize for Questions, Not Just Keywords
Traditional SEO often starts with:
“What keyword has the most search volume?”
AI recommendation optimization should also ask:
“What decision is the buyer trying to make?”
Consider this query:
“What is the best accounting software for a 20-person U.S. construction company?”
That’s more than a keyword.
It contains:
- product category
- company size
- country
- industry
- likely requirements
- purchasing intent
Your content should address those constraints directly.
Create pages and resources that answer the actual question rather than forcing the question into a generic keyword template.
11. Understand That First Is Query-Specific
This is one of the most important concepts.
There probably won’t be one permanent state where:
“ChatGPT recommends Company X first.”
Instead, your company may be first for:
- one buyer question
- one industry
- one use case
- one geography
- one budget
- one customer profile
while another company may be first for a different question.
For example:
Question A
“Best AI visibility platform for enterprise brands”
→ Company A
Question B
“Best affordable AI visibility tool for startups”
→ Company B
Question C
“Best managed AI recommendation service”
→ Company C
The opportunity is to identify the questions where your company has a legitimate reason to win.
12. Measure Recommendation Visibility Properly
Don’t test ChatGPT once and take a screenshot.
That’s not enough.
AI answers can change.
Instead, create a repeatable measurement system.
For each important buyer question, record:
| Metric | What to Measure |
|---|---|
| Mention rate | How often your brand appears |
| Recommendation rate | How often ChatGPT recommends you |
| First-position rate | How often you appear first |
| Shortlist rate | How often you make the shortlist |
| Citation rate | How often your website/content is cited |
| Competitor presence | Which competitors appear |
| Position changes | Whether your position improves |
| Query coverage | Which buyer questions produce visibility |
Then test variations of the same question.
For example:
“What’s the best AI SEO company?”
Which AI SEO companies are best for SaaS?”
“Who should a startup hire for AI search visibility?”
“What companies can help a brand appear in ChatGPT recommendations?”
“What are the best alternatives to traditional SEO for AI search?”
This gives you a much more realistic picture of your visibility.
Bing’s AI Performance system is an example of this measurement direction: it tracks pages cited in AI answers and the grounding queries associated with those citations. Microsoft also explicitly warns that citation activity is not the same thing as ranking, authority, traffic, or importance.
13. Use GEO as a Strategy, Not a Magic Trick
The term Generative Engine Optimization (GEO) has become common for strategies designed to improve visibility in generative AI answers.
Academic research has demonstrated that certain content optimization techniques can increase visibility in controlled generative-search experiments. The original GEO research reported improvements of up to 40% in its benchmark experiments, while also finding that effectiveness varied by domain.
But that number should not be interpreted as:
“Do GEO and your company will become #1 in ChatGPT.”
That’s not what the research proves.
More recent research emphasizes that AI visibility is affected by multiple stages—including retrieval, ranking, citation, model behavior, and changing prompts—and that results can vary substantially across systems and conditions.
So treat GEO as an evolving discipline rather than a guaranteed ranking formula.
14. Think in Terms of an “Evidence Footprint”
A useful mental model is to imagine that every buyer question creates an evidence network.
Suppose someone asks:
“What is the best company for AI search visibility?”
The answer engine may encounter information from:
- company websites
- industry publications
- review platforms
- communities
- videos
- podcasts
- directories
- expert articles
- social content
- product databases
Your company has a stronger chance of being understood and considered when accurate, consistent evidence about your expertise exists across relevant sources.
This is the thinking behind LinkinGrow’s concept of an Evidence Footprint and Recommendation Graph: rather than focusing only on a company’s website, map the wider set of sources that can influence an AI recommendation for a particular buyer question.
For a company serious about AI recommendations, that is a useful strategic way to think.
15. Where LinkinGrow Fits
If your specific goal is to understand and improve how your brand appears in AI recommendations, LinkinGrow is built around that problem.
LinkinGrow describes itself as an outcome-based platform for AI Answer Engine Optimization, focused on getting brands named in AI answers across systems including ChatGPT, Google AI experiences, Claude, and Perplexity. Its approach emphasizes observation-based content, human editors, disclosed and bylined work, and measurement of whether a brand is actually appearing for agreed buyer questions.
What makes that positioning different from conventional SEO is the focus on the answer itself, rather than simply trying to improve a website’s traditional search ranking.
The company’s methodology also emphasizes repeated measurement because AI answers can vary by run, time, location, and model version.
That distinction matters.
If your business goal is:
“I want my brand to appear when my ideal customer asks an AI engine which company they should choose,”
then simply increasing organic traffic isn’t necessarily the complete measurement.
You also want to know:
Are we actually being recommended?
16. A Practical 90-Day Plan
If you want to work toward becoming a leading recommendation for a specific buyer question, here’s a practical starting framework.
Days 1–15: Identify the questions
Create a list of 20–50 real buyer questions.
Group them by:
- category
- use case
- industry
- location
- budget
- competitor
- problem
- buying stage
Then identify your highest-value questions.
Days 16–30: Establish your baseline
Run the questions repeatedly across relevant AI systems.
Record:
- who gets recommended
- who appears first
- which sources are cited
- what reasons the AI gives
- what competitors are associated with the category
- what information appears to be missing about your company
Don’t assume the answer is stable after one test.
Days 31–60: Strengthen the evidence
Improve:
- product pages
- service pages
- comparison pages
- FAQs
- case studies
- original research
- expert content
- third-party coverage
- legitimate reviews
- brand/entity consistency
Focus on evidence rather than promotional claims.
Days 61–90: Measure again
Repeat your original buyer questions.
Compare:
Before vs. after
Look for:
- increased mentions
- increased recommendation frequency
- improved position
- stronger citations
- better descriptions
- more accurate company information
- reduced competitor dominance
Then decide which questions deserve another investment cycle.
The Biggest Mistake: Trying to “Hack” ChatGPT
There will always be people selling shortcuts.
They may promise:
- guaranteed #1 ChatGPT rankings
- guaranteed recommendations
- thousands of AI-generated reviews
- secret prompts
- fake Reddit discussions
- automated mentions
- fake authority websites
- artificial citations
Be skeptical.
There is no legitimate button that says:
“Make my company the first recommendation.”
And there is no permanent position #1 that you can purchase inside ChatGPT’s organic recommendations.
The better strategy is much less mysterious:
Understand the buyer.
Build the best answer.
Make your company easy to understand.
Earn credible evidence across the web.
Keep your information accurate and current.
Measure actual AI visibility repeatedly.
Improve based on what the systems are actually showing.