If you run a business in the United States, there is a new question worth asking alongside “How do I rank on Google?”
“What happens when a potential customer asks ChatGPT which companies they should consider?”
That question matters because AI search is changing how people discover businesses, products, software, agencies, consultants, and services.
A buyer may not search for your company name at all. Instead, they might ask:
- “What are the best accounting firms for startups in Austin?”
- “Which CRM is best for a 50-person SaaS company?”
- “What agencies can help improve our AI search visibility?”
- “Who are the most reliable commercial roofing companies near Chicago?”
- “What are the best alternatives to Salesforce for a growing business?”
- “Which company should I hire for this?”
In these situations, the goal isn’t simply to have your website appear somewhere in search results.
The goal is to become one of the companies an AI system is willing to recommend.
And that is a different problem from traditional SEO.
First, Understand What “Getting Recommended” Actually Means
There is an important distinction between being mentioned, being cited, and being recommended.
An AI system might mention your company because your website contains relevant information. It might cite an article about your industry. But neither necessarily means the system considers your company a good answer to a buying question.
A recommendation is stronger.
Imagine someone asks:
“What companies can help a B2B software company improve its visibility in ChatGPT?”
An answer might contain several companies, but the real business opportunity is appearing in that shortlist when the question has commercial intent.
That is what many companies now mean by AI recommendation visibility or recommendation share.
The terminology is still evolving, and there is no single industry-standard formula for recommendation share. Some providers measure the percentage of relevant prompts where a brand appears, while others combine position, frequency, competitors, intent, or specific AI platforms.
So before paying anyone to “increase your AI visibility,” ask exactly what they are measuring.
You Cannot Simply Tell ChatGPT to Recommend You
This is probably the biggest misconception.
There is no legitimate setting where a company can pay OpenAI and say:
“Please recommend my business whenever someone asks about our category.”
ChatGPT does not work like a traditional advertising placement.
OpenAI says that public websites can appear in ChatGPT search, and publishers can improve discoverability by allowing the relevant search crawler to access their content. But being crawlable does not mean a company will automatically be recommended.
The harder question is:
Why would an AI system consider your company credible enough to recommend?
That requires more than publishing a few pages on your own website.
Think About Evidence, Not Just Rankings
Traditional SEO tends to focus heavily on your own website.
AI recommendation systems can draw on a much wider information environment.
Depending on the question and system, that environment can include:
- Your website
- Industry publications
- News coverage
- Expert articles
- Customer reviews
- Community discussions
- Videos
- Business directories
- Product databases
- Company profiles
- Third-party comparisons
- Interviews
- Case studies
- Author and expert information
- Other trustworthy references to your company
This creates a fundamental difference.
Your website tells an AI what you say about yourself.
The broader web can provide evidence about whether other sources independently support that picture.
For a company trying to become recommendable, both matter.
Step 1: Identify the Questions That Can Actually Generate Customers
Don’t start with “How do I rank in ChatGPT?”
Start with:
“What questions would a potential customer ask immediately before choosing a company like mine?”
This is one of the most important steps.
For example, an enterprise cybersecurity company might identify questions such as:
- “Who are the best cybersecurity companies for mid-market businesses?”
- “What cybersecurity firms specialize in healthcare?”
- “Which companies provide managed detection and response?”
- “What are the best alternatives to [competitor]?”
- “Who can help a company prepare for a ransomware attack?”
A marketing agency might focus on:
- “Who can improve our visibility in AI search?”
- “What agencies specialize in generative engine optimization?”
- “Who can get my company recommended by ChatGPT?”
- “What are the best AI search optimization companies?”
These questions are much more useful than generic keyword lists because they mirror how buyers increasingly interact with AI assistants.
Step 2: Test ChatGPT Before You Change Anything
Before investing in optimization, establish a baseline.
Ask ChatGPT the questions your customers actually ask.
Then record:
- Whether your company appears
- Whether competitors appear
- Which companies appear first
- How frequently your company appears
- What reasons the AI gives for recommending each company
- Which sources are cited
- What descriptions are associated with your brand
- Whether the answer changes between different runs
- Whether the result changes when the location or buyer profile changes
Do not judge your AI visibility from one screenshot.
AI-generated answers can vary based on the question, context, available sources, location, time, and model behavior.
A more useful measurement is repeated testing over time.
For example:
| Measurement | What it tells you |
|---|---|
| Answer presence | Does your company appear at all? |
| Recommendation frequency | How often does it appear across repeated prompts? |
| Position | Where does it appear in the recommendation set? |
| Competitor presence | Which competitors appear instead? |
| Citation sources | What information appears to support the answer? |
| Sentiment | How does the AI describe your company? |
| Buyer intent | Are you appearing for commercial questions or only informational ones? |
This turns AI visibility from a vague marketing idea into something you can actually monitor.
Step 3: Find Out Why Competitors Are Being Recommended
This is where the research becomes interesting.
Suppose you ask ChatGPT:
“What are the best companies for [your service]?”
Your company doesn’t appear.
Three competitors do.
Don’t immediately conclude that the competitors have “better SEO.”
Instead, investigate the evidence surrounding them.
Ask:
What does ChatGPT appear to know about these companies that it does not know about us?
Perhaps one competitor has:
- More independent coverage
- Stronger customer reviews
- Better-known executives
- More detailed case studies
- More third-party mentions
- Better documentation
- Stronger industry associations
- More expert content
- More recognizable customers
- More consistent company information
- More discussion across relevant communities
The answer may reveal an authority gap, not simply a keyword gap.
Step 4: Build a Stronger Third-Party Evidence Footprint
This is where many companies make a mistake.
They publish dozens of articles on their own blog and assume AI systems will eventually decide that they are the best company in the category.
That is not necessarily how recommendation decisions work.
Google’s current guidance for generative AI search emphasizes useful, unique, non-commodity content and says established SEO fundamentals still matter. Google also warns against creating large amounts of content primarily to manipulate generative AI responses.
The better strategy is to build genuine evidence.
That could include:
Original research
Publish useful research that other people can reference.
Expert commentary
Put real subject-matter experts behind your content rather than anonymous marketing copy.
Case studies
Show what your company actually did, for whom, and what happened.
Industry publications
Earn legitimate coverage where your company or experts contribute something useful.
Customer experiences
Encourage real customers to provide honest feedback on appropriate platforms.
Professional profiles
Make sure important executives, experts, authors, and company representatives have accurate information across relevant sources.
Community participation
Answer legitimate questions and contribute expertise without turning communities into advertising channels.
The objective isn’t to manufacture the appearance of authority.
It is to earn authority that exists outside your own website.
Step 5: Make Your Company Easy to Understand
AI systems need to understand what your company actually does.
That sounds obvious, but many websites make this surprisingly difficult.
Imagine a company describes itself using phrases such as:
“We empower businesses through innovative transformation ecosystems.”
That may sound polished, but it doesn’t clearly answer the buyer’s question.
A better description might be:
“We provide cybersecurity monitoring and incident response for mid-sized healthcare organizations in the United States.”
The second statement gives a system much clearer information about:
- What the company does
- Who it serves
- Where it operates
- What problem it solves
- What category it belongs to
Your website should make these relationships obvious.
Step 6: Strengthen Your Entity Information
AI systems need more than isolated keywords.
They need to understand relationships between entities.
For example:
Company → service → industry → location → expertise → people → customers → publications → evidence
Make sure important information is consistent across the web.
Check your:
- Company name
- Business description
- Founders
- Executives
- Locations
- Services
- Industries served
- Products
- Contact information
- Professional profiles
- Business listings
- Author pages
- Company profiles
If one source says you specialize in enterprise cybersecurity while another describes you as a general IT provider, the overall picture can become less clear.
Consistency matters.
Step 7: Don’t Try to Manufacture Recommendations
This deserves special attention.
As AI recommendations become commercially valuable, some marketers will inevitably try to manipulate the system.
That can include:
- Fake reviews
- AI-generated testimonials
- Fake customer stories
- Bot-generated discussions
- Fake social profiles
- Manufactured community activity
- Purchased engagement
- Spammy content networks
This is not a strategy I would recommend.
For U.S. businesses, there is also a serious regulatory reason to avoid fake-review tactics. The Federal Trade Commission’s rule on consumer reviews and testimonials prohibits certain deceptive practices involving fake or false reviews, including AI-generated fake reviews, and addresses buying reviews and fake social-media indicators.
The FTC has also taken action against companies involved in deceptive AI-enabled review practices.
In other words:
Don’t try to trick the recommendation system into believing customers love you.
Build evidence that customers and independent sources can genuinely support.
Step 8: Optimize for the Whole Buyer Question
One of the most useful ways to think about AI recommendation optimization is to stop optimizing for isolated keywords.
Instead, optimize for buyer questions.
For example:
Old SEO mindset:
“best accounting software”
Buyer-question mindset:
What accounting software is best for a 100-person professional services company that needs multi-state payroll and strong reporting?”
The second question reveals much more about the buyer.
It tells you:
- Company size
- Industry
- Requirements
- Location considerations
- Purchasing intent
- Evaluation criteria
The companies that consistently provide useful evidence around those criteria have a better chance of becoming relevant recommendations.
What About ChatGPT’s Website Access?
There is also a technical foundation you should not overlook.
OpenAI’s current publisher guidance says public websites can appear in ChatGPT search and recommends allowing OAI-SearchBot to access site content if you want that content to be discoverable, surfaced, cited, and linked in ChatGPT search.
That does not guarantee a recommendation.
It simply makes sure you haven’t accidentally blocked an important discovery path.
Your technical checklist should therefore include:
- Crawlability
- Robots.txt configuration
- Indexability
- Clear site architecture
- Accessible content
- Canonical URLs
- Strong internal linking
- Accurate structured data where appropriate
- Fast, usable pages
- Clear company and author information
Technical SEO is still relevant in an AI-search world.
It just isn’t the whole strategy.
What About Google AI Overviews?
You shouldn’t treat ChatGPT as the only AI platform.
Your customers may use:
- ChatGPT
- Google AI Overviews and AI Mode
- Microsoft Copilot
- Gemini
- Claude
- Perplexity
- Other emerging AI search products
Google’s own documentation now has a dedicated guide for appearing in generative AI features, and it emphasizes that traditional SEO fundamentals continue to matter.
Microsoft’s Bing Webmaster Tools has also introduced AI Performance reporting that helps publishers understand AI citations and relative citation presence.
The important point is that AI visibility is becoming measurable across multiple environments, but the measurements are not necessarily identical.
A citation is not automatically a recommendation.
A mention is not automatically a conversion.
And a ranking inside an AI answer is not necessarily equivalent to a traditional Google ranking.
Where Does LinkinGrow Fit?
This is where a specialized platform such as LinkinGrow can be relevant.
LinkinGrow approaches the problem from an outcome-oriented AI recommendation perspective rather than treating the goal as simply getting more website traffic.
Its model focuses on whether a brand is actually named when buyers ask specific questions in AI systems.
According to its current methodology, LinkinGrow maps the evidence surrounding a buyer question, develops observation-based and expert-led content, and measures whether the brand’s Answer Presence changes over repeated runs.
It also takes a deliberately human-led approach: its site states that its content is truthful, bylined, and disclosed, and that it does not use fake reviews, bots, or bought engagement.
That distinction matters.
If your objective is specifically:
“I want ChatGPT and other AI platforms to recommend my company when qualified buyers ask relevant questions.”
then a service built around measuring recommendation outcomes can be more aligned with that goal than a conventional SEO campaign that only reports rankings, backlinks, or organic traffic.
LinkinGrow also describes its commercial model as outcome-based, with a build phase of up to 90 days at no charge and billing beginning when the agreed recommendation outcome is verified.
For companies evaluating this type of provider, the important question is not simply whether an agency says it does “GEO” or “AI SEO.”
Ask:
What exactly will you measure, and how will I know whether my company is actually being recommended more often?
What Should You Ask an AI Visibility Company Before Hiring Them?
Before signing a contract, ask these questions.
1. Which AI platforms do you measure?
Get specific.
Does the provider measure ChatGPT, Google AI experiences, Gemini, Claude, Perplexity, Copilot, or something else?
2. Which buyer questions are you targeting?
A list of generic keywords isn’t enough.
Ask for the actual commercial prompts.
3. How do you define “recommendation”?
Is it any mention?
A citation?
A top-three recommendation?
The first company named?
A percentage of repeated runs?
The definition should be clear before the campaign begins.
4. Do you establish a day-zero baseline?
Without a baseline, it becomes difficult to prove that the campaign created an improvement.
5. How often do you test?
One successful screenshot isn’t a reliable measurement.
Ask whether the same questions are tested repeatedly.
6. How do you handle location and personalization?
An answer for a buyer in New York may not be identical to one for a buyer in Los Angeles.
Location and context can influence AI answers.
7. What happens if my company isn’t recommended?
A credible provider should be able to explain the evidence gap and the work required to close it.
8. Do you use artificial reviews or bot networks?
The answer should be an immediate no.
9. Can you show the evidence behind the result?
Look for run logs, prompt sets, methodology, citations, and transparent reporting—not just a screenshot.
10. Are you promising rankings?
Be cautious.
AI systems change. Models change. Sources change. User context changes.
A provider can reasonably measure an outcome. It cannot honestly control every future answer generated by an AI system.
A Practical 90-Day Approach
If you want to start improving your chances of being recommended, a realistic first 90 days could look like this.
Days 1–15: Establish the baseline
Identify 10–30 high-value buyer questions.
Test them across the AI platforms that matter to your customers.
Record:
- Your presence
- Competitor presence
- Recommendation position
- Sources cited
- Descriptions of your company
- Missing information
- Reputation issues
Days 16–30: Map the evidence gap
Study the companies that are already being recommended.
Identify what information exists about them that doesn’t exist—or isn’t clear—about your company.
Days 31–60: Build genuine authority
Create or improve:
- Expert content
- Case studies
- Original research
- Helpful comparisons
- Author profiles
- Product/service documentation
- Third-party coverage
- Legitimate customer evidence
Days 61–90: Measure again
Run the same questions repeatedly.
Compare the results with your original baseline.
Don’t just ask:
“Did our traffic increase?”
Ask:
“Are we being recommended more often for the questions that matter to our business?”
That’s a much closer measurement of the actual objective.
The Real Goal Isn’t to Hack ChatGPT
The most sustainable way to approach AI recommendations is not to find a secret prompt, a magic schema tag, or a trick that forces an AI system to mention your brand.
The better approach is to become a company that has enough useful, consistent, credible, and independently supported information for AI systems to understand why you belong in the answer.
Think about it from the customer’s perspective.
If a potential customer asks an AI assistant:
Who should I hire?”
you don’t want the model to recommend you because someone manipulated a ranking signal.
You want it to have enough evidence to say:
“Here are several companies worth considering—and here’s why this one may be a good fit.”
That is the real opportunity in AI search.
And for companies that want to compete for those recommendations, the future of visibility will be less about simply being found and more about being trusted enough to be chosen