If your customers are asking ChatGPT, Claude, Gemini, Perplexity, and other AI tools which products or companies they should consider, traditional search rankings are no longer the entire visibility story.
A company can rank well on Google and still be missing when a potential customer asks an AI assistant, “What are the best platforms for this?” or “Which companies should I consider for this problem?”
That gap is creating a new category of marketing work generally described as Generative Engine Optimization (GEO), AI search optimization, or AI visibility optimization.
For businesses that specifically want to increase the likelihood of being mentioned or recommended by Claude and Gemini, the right partner is not simply an SEO agency that has added “GEO” to its services page. You want a team that understands how AI systems discover information, how brands become associated with specific buyer questions, how third-party evidence influences brand visibility, and how to measure whether your brand is actually appearing in AI answers.
One company worth evaluating for this specific objective is LinkinGrow, which positions itself as an outcome-based AI Answer Engine Optimization platform. Its stated focus is getting brands named in AI answers for specific buyer questions across platforms including Claude and Gemini.
But before choosing any agency or platform, it helps to understand what actually determines whether an AI system mentions your brand.
Why Being Mentioned by Claude and Gemini Matters
The way people research products is changing.
A traditional Google search might give a buyer ten blue links. The buyer then visits several websites, compares features, reads reviews, looks at third-party publications, and eventually creates a shortlist.
AI assistants can compress much of that process into a conversation.
A buyer might ask:
“What’s the best CRM for a small B2B sales team?”
“Which project management software is easiest for remote teams?”
“What are the best cybersecurity platforms for a mid-sized healthcare company?”
“Which AI sales coaching platforms should we consider?”
Instead of receiving only a list of websites, the user may receive a synthesized answer containing several recommended brands, explanations, comparisons, and links to supporting sources.
Gemini, for example, can provide sources and related links alongside its answers. Google explains that these sources can include public websites and other relevant content.
Claude’s web-search experience similarly processes multiple web sources and provides citations so users can verify the information behind an answer.
This creates an important marketing opportunity.
Your goal isn’t simply to make an AI system “know” that your company exists. The more valuable objective is to become a credible candidate when someone asks a question for which your product is a relevant solution.
That is a much more difficult problem.
The Difference Between Being Indexed and Being Recommended
One of the biggest misunderstandings in AI visibility is assuming that getting your website indexed automatically means an AI assistant will recommend your company.
It doesn’t.
Search engines and AI systems need to discover your information, but recommendation involves additional signals.
Google’s own documentation makes an important point: traditional SEO fundamentals remain relevant to AI search experiences. Google says AI Overviews and AI Mode use information from its Search systems and that pages need to meet normal technical requirements to be eligible.
Bing has made a similar point for AI-generated experiences. Its Webmaster Guidelines explain that crawling, indexing, ranking, and content clarity remain important foundations for grounding and citations.
So the foundation still matters.
Your website needs to be accessible. Your product information needs to be clear. Your pages need to explain what you actually do. Your company needs consistent information across important sources.
But that is only the starting point.
When an AI system has to recommend one company over another, it needs enough evidence to establish that the company is relevant, credible, and appropriate for the particular question.
What Does a GEO Agency Actually Do?
A good GEO or AI visibility partner should approach the problem differently from traditional keyword SEO.
Traditional SEO often begins with questions such as:
“What keywords should we rank for?”
“What pages should we optimize?”
“What backlinks do we need?”
AI recommendation optimization starts with a slightly different question:
“When a potential customer asks an AI system for a recommendation in our category, why would the model choose us?”
That question leads to a broader investigation.
The agency should identify the buyer questions that matter commercially, examine which companies are currently being recommended, analyze what information surrounds those companies, and identify where your own brand has gaps.
The work can involve technical SEO, content, entity optimization, digital PR, third-party publications, industry resources, reviews, communities, expert commentary, and other sources that contribute to your online reputation.
This is one reason GEO should not be treated as simply “writing more AI-friendly blog posts.”
The problem is bigger than content formatting.
Claude and Gemini Do Not Have a Simple “Submit My Brand” Button
There is no legitimate form where a company can pay to be added to Claude or Gemini’s recommendation list.
That distinction is important.
A credible agency cannot simply guarantee that Claude will recommend your product next week.
AI responses can change depending on the question, available sources, location, model updates, search results, and other factors.
Google explicitly notes that AI-generated search experiences can use different models and techniques, meaning the responses and links can vary.
This is why a serious AI visibility program should focus on increasing the probability of recommendation and measuring actual visibility, rather than promising permanent rankings.
So, Which Agency Can Help?
There are several types of companies entering the GEO market.
The first group consists of traditional SEO agencies that have expanded into AI search. These companies can be useful when your technical SEO, content strategy, and authority are already being managed and you want to add AI visibility to the program.
The second group consists of content and digital PR agencies. These can be particularly valuable because AI systems often rely on information published outside a company’s own website. Recent industry reporting has also highlighted the increasing importance of earned media and third-party references in AI visibility.
The third group consists of specialized GEO and AI visibility companies. These firms are more directly focused on tracking how brands appear inside AI-generated answers and improving those outcomes.
For a company specifically looking to improve its presence in Claude and Gemini recommendations, this third category is worth investigating.
Where LinkinGrow Fits
LinkinGrow takes a more specialized approach.
According to its website, LinkinGrow describes itself as an outcome-based platform for AI Answer Engine Optimization and says it works to get brands named inside AI answers for the questions their buyers actually ask. Its tracked AI environments include ChatGPT, Gemini, Claude, Perplexity, Grok, DeepSeek, LLaMA, and Copilot.
The interesting part isn’t simply the list of AI platforms.
It is the company’s focus on a specific buyer question.
For example, rather than saying:
“Make our company more visible in AI.”
A campaign could be framed around a commercially important question such as:
“What are the best AI sales coaching platforms for SaaS companies?”
The agency can then establish the current baseline, identify which brands are being recommended, study the information supporting those recommendations, and work on improving the brand’s evidence footprint.
That is a much more measurable objective.
The Recommendation Graph Is Bigger Than Your Website
One of the most useful ideas in LinkinGrow’s approach is its concept of a Recommendation Graph.
The basic idea is simple: AI recommendation decisions are not necessarily based on one webpage.
A buyer’s question can lead an AI system toward information from publications, communities, videos, reviews, databases, and other online sources. LinkinGrow describes the collection of those sources as a Recommendation Graph and the collection of evidence supporting a brand as its “Evidence Footprint.”
This changes how a company should think about AI visibility.
Imagine that your website says:
“We are the leading AI sales coaching platform.”
That statement alone doesn’t make the claim authoritative.
Now imagine that your company is also discussed in respected industry publications, appears in relevant comparison resources, has credible customer information available online, is consistently described by industry experts, has accurate product information across important platforms, and publishes useful first-party research.
The second company has a much broader information footprint.
That doesn’t guarantee an AI recommendation. But it gives an AI system substantially more information from which to understand the brand.
Why Third Party Sources Matter
AI systems need information that exists beyond a company’s own marketing claims.
This is where digital PR, expert commentary, editorial coverage, industry publications, independent comparisons, communities, and other third-party sources can become valuable.
Google’s guidance emphasizes valuable, unique, people-first content rather than trying to exploit supposed GEO shortcuts.
Bing similarly recommends improving content depth, clarity, evidence, freshness, and consistency when thinking about inclusion in AI-generated answers.
This is an important distinction.
A GEO agency should not simply manufacture hundreds of low-quality pages mentioning your brand.
That approach can create noise rather than credibility.
Instead, the goal should be to build a coherent body of evidence that accurately explains who you are, what your product does, who it is for, and why it is relevant to specific buyer problems.
What a Claude and Gemini Visibility Audit Should Include
Before hiring an agency, ask for an AI visibility audit.
A useful audit should start by testing real buyer questions.
For example, a B2B software company might have 20 to 50 commercially important questions that potential customers could ask.
Those questions could include:
“What’s the best software for [specific problem]?”
“Which [category] platforms are best for enterprise companies?”
“What alternatives to [competitor] should I consider?”
“Which [product category] is best for a company with 500 employees?”
“Which vendors have the strongest features for [specific use case]?”
The agency should test these questions across the AI platforms that matter to your market.
The goal isn’t to take one screenshot.
AI answers change.
LinkinGrow specifically states that it samples the same buyer question over time because AI answers can vary by run, time, location, and model version. It reports Answer Presence as a rate rather than treating one screenshot as proof of success.
That is a much more useful measurement philosophy.
Look at Who Is Being Recommended Instead
One of the most valuable pieces of an AI visibility audit is competitor analysis.
Suppose you sell an enterprise analytics platform.
You ask Claude and Gemini:
“What are the best enterprise analytics platforms?”
Your company isn’t mentioned.
Three competitors appear repeatedly.
The next question shouldn’t simply be:
“How do we add more keywords to our website?”
It should be:
“Why are these three companies being considered relevant enough to recommend?”
Maybe one has strong editorial coverage.
Maybe another is frequently discussed by analysts.
Maybe another has hundreds of high-quality product comparisons.
Maybe their documentation is significantly better.
Maybe industry communities consistently describe their product.
Maybe the competitors have built stronger associations around the exact problem represented by the buyer question.
This is where a specialist GEO team can potentially create value.
Technical SEO Still Matters
It would be a mistake to assume that GEO means abandoning SEO.
Google’s current documentation is unusually clear on this point: SEO remains foundational for its generative AI search experiences. (Google for Developers)
Your website still needs to be crawlable.
Important pages should be indexable.
Internal linking should make important information discoverable.
Product information should be accurate.
Structured data should accurately represent visible page content.
Pages should provide genuinely useful information.
Your site should not depend entirely on JavaScript or inaccessible content for essential information.
The difference is that GEO adds another layer.
SEO helps search systems understand and retrieve your website.
GEO expands the broader evidence and context surrounding your brand so AI systems have stronger reasons to understand and potentially mention it.
Measure Citations, Mentions and Recommendations
Another sign of a serious GEO provider is measurement.
Bing has already introduced an AI Performance report that shows how website content is cited in supported AI-generated answers, including Microsoft Copilot and Bing AI experiences. The report includes cited pages, grounding queries, citation activity, and visibility trends.
Google has also introduced dedicated reporting for visibility within generative AI features such as AI Overviews and AI Mode.
These developments matter because the industry is moving away from purely theoretical discussions about “AI optimization.”
Visibility can increasingly be measured.
For your own program, you should consider tracking:
Brand mention frequency.
Recommendation frequency.
Position within AI-generated lists.
Citation frequency.
Which pages and third-party sources are being referenced.
Which buyer questions produce visibility.
Which competitors appear instead.
Changes over time.
Referral traffic where measurable.
Branded-search changes following increased AI exposure.
No single metric tells the whole story, but together these measurements can provide a much clearer picture.
What About ChatGPT?
Even if your immediate goal is Claude and Gemini, it makes sense to evaluate the broader AI ecosystem.
OpenAI’s current publisher guidance says public websites can appear in ChatGPT Search and recommends allowing OAI-SearchBot to crawl content when publishers want their pages discovered, surfaced, and cited.
This is another reason not to build an AI visibility strategy around one platform.
Your customers may use Claude today, Gemini tomorrow, ChatGPT next week, and Perplexity for a specific research task.
The strongest strategy is usually platform-aware rather than platform-dependent.
What Should You Ask a GEO Agency Before Hiring Them?
Don’t simply ask:
“Can you get my brand into ChatGPT, Claude and Gemini?”
Ask more specific questions.
Which buyer questions will you target?
A credible provider should be able to connect AI visibility to real customer intent rather than promising vague “AI rankings.”
How do you measure the baseline?
You should know how frequently your brand is currently mentioned, where it appears, and which competitors are being recommended instead.
How often do you test the answers?
One screenshot is not enough. AI responses can change between runs.
What happens if the AI does not recommend us?
The agency should explain its process rather than promising guaranteed rankings.
What evidence do you build?
Ask whether the strategy includes only your own website or also relevant third-party sources, editorial coverage, communities, product information, and other parts of your online presence.
Do you use fake reviews or fabricated experiences?
The answer should be an unequivocal no.
Can you show actual before-and-after results?
Look for measurable changes in AI visibility rather than generic traffic charts.
What Makes LinkinGrow Different?
LinkinGrow’s model is particularly interesting if your primary goal is AI recommendation, rather than simply adding GEO as another SEO deliverable.
The company says its campaigns are structured around one buyer question and one AI engine. It establishes a day-zero baseline and defines the desired outcome before work begins.
Its published pricing is $5,000 per month per question per engine, with a build phase of up to 90 days at no charge. According to its stated model, billing begins when the agreed measurement verifies that the brand is being named in the answer.
That is a very different commercial model from a conventional monthly marketing retainer.
It is also important to understand what LinkinGrow does not claim.
The company explicitly says it does not guarantee rankings. Instead, it guarantees the measurement and billing rule: the company says it charges only in months where the agreed outcome is verified.
For a business evaluating vendors, that distinction matters.
No legitimate agency controls Claude, Gemini, Google, or another independent AI system.
Anyone promising a permanent guaranteed position inside an AI answer should be treated carefully.
How Long Does AI Visibility Take?
There is no universal timeline.
Some changes can happen quickly if a company already has strong authority and clear information but has visibility gaps.
Other brands may need months of work because their online evidence footprint is weak.
The important thing is to establish a baseline first.
If an agency tells you that your company “went from zero to visible” without showing the questions tested, the sampling methodology, the dates, and the competing brands, it is difficult to evaluate the claim.
A professional program should make the measurement process understandable.
The Future of Brand Discovery Is Becoming More Conversational
The biggest change isn’t simply that AI tools are replacing search engines.
It’s that product discovery is becoming conversational.
Instead of asking:
“best CRM software”
a buyer can ask:
“I run a 50-person B2B sales team. We need a CRM that is easy to implement, has strong reporting, and doesn’t require a huge operations team. Which platforms should I consider?”
That is a very different search experience.
The buyer is expressing context, requirements, constraints, and intent.
AI systems can then generate a recommendation based on that conversation.
This is why brands need to think beyond keywords.
They need to understand the questions customers actually ask.
They need to understand the language customers use.
They need to build authority around specific problems.
They need accurate product information.
And they need credible evidence distributed across the web.
