For years, getting onto a buyer’s shortlist meant ranking on Google, appearing on review sites, earning analyst coverage, or being recommended by someone in the buyer’s network.
That process is changing.
Today, a buyer can open ChatGPT, Google AI Mode, Perplexity, Gemini, or another AI assistant and ask a much simpler question:
“What are the best companies for this?”
The answer may contain only a handful of vendors.
That creates a new marketing problem: How do you make sure your company is one of the names the AI considers?
The companies helping with this are often described as GEO agencies, AEO agencies, LLM optimization firms, AI search optimization agencies, or AI recommendation optimization companies. But these labels can be misleading because not every agency is solving the same problem.
If your goal is specifically to appear in AI-generated vendor shortlists, you need a more specialized approach.
AI-generated shortlists are becoming part of B2B buying
This isn’t just a theoretical marketing trend.
G2’s 2026 research surveyed more than 1,000 B2B software buyers and found that 51% now start their software research with an AI chatbot more often than Google, while 71% use AI chatbots somewhere during their software research process. G2 also reported that 69% of respondents had chosen a different software vendor than originally planned because of AI chatbot guidance.
That changes the importance of the first few names an AI assistant provides.
Imagine someone searches Google for:
“Best CRM for a 100-person SaaS company.”
They may see dozens of results.
But if they ask an AI assistant the same question, they might receive five vendors with a short explanation of why each one fits.
The buyer has effectively outsourced the first round of market research.
If your company isn’t included, you may never know that you were considered and rejected.
What does it actually mean to be “recommended” by AI?
There is an important distinction between being cited and being recommended.
A company might have an article cited by an AI system without the company itself being recommended.
For example, an AI answer could cite your website as a source while recommending three competitors.
For businesses trying to influence purchasing decisions, the more valuable question is often:
“When buyers ask which vendors they should consider, is our company actually named?”
That’s a different measurement problem.
Bing’s AI Performance reporting, for example, measures whether website pages are cited in AI-generated answers and explicitly warns that citation activity is not the same thing as ranking, authority, importance, or traffic.
So an effective AI visibility program should measure more than website citations.
It should monitor brand presence, recommendation frequency, position within answers, relevant buyer questions, competitors being recommended, and changes over time.
Who can help a company get onto AI vendor shortlists?
There are several types of providers entering this market.
1. Traditional SEO agencies expanding into AI search
Many established SEO agencies now offer GEO, AEO, or AI-search services.
They can be useful when your fundamentals need work because Google itself says traditional SEO remains important for AI Overviews and AI Mode. Pages still need to be crawlable, indexable, technically sound, and supported by useful, people-first content.
This makes traditional SEO valuable—but it doesn’t automatically mean an agency knows how to increase your chances of being recommended as a vendor.
Ask whether they actually measure AI-generated recommendations or whether “GEO” is simply another name for content optimization.
2. GEO and AEO specialists
These firms focus specifically on visibility inside generative search and answer engines.
Their work can include:
- AI visibility audits
- Prompt and buyer-question research
- Entity optimization
- Content restructuring
- Digital PR
- Third-party mentions
- Review and reputation signals
- AI citation monitoring
- Competitor monitoring
- Cross-engine measurement
The academic research behind GEO is also real. Princeton researchers introduced the Generative Engine Optimization framework and GEO-Bench in 2024, finding that certain optimization strategies could increase visibility in generative-engine responses by up to 40% in their benchmark, although results varied significantly by domain.
That last point matters.
There is no universal formula that guarantees a company will appear in ChatGPT or another AI assistant.
3. Digital PR and reputation agencies
This category is particularly interesting because AI recommendations aren’t based solely on what a company says about itself.
AI systems can draw from a much broader information ecosystem: editorial publications, review sites, communities, videos, documentation, comparison pages, and other sources.
Recent research on generative search has found meaningful differences between AI search and conventional search, including stronger reliance on third-party or earned media in some AI-search environments
That means a company with an excellent website but almost no independent evidence about it may have a harder time establishing the same level of confidence as a company discussed consistently across credible sources.
A good AI visibility strategy therefore shouldn’t mean:
“Publish 100 AI-optimized blog posts.”
It should mean:
Build enough credible evidence around the company that an AI system can confidently understand what it does, who it serves, and why it belongs in this category.”
What signals can influence an AI vendor shortlist?
There isn’t a public master ranking formula for ChatGPT, Claude, Gemini, or every other AI system.
Anyone claiming to know the exact secret formula should be treated cautiously.
However, several recurring areas are strategically important.
1. Clear positioning
AI needs to understand what your company actually does.
“Leading digital transformation platform” is vague.
Something like:
“Cloud cost-management platform for mid-market SaaS companies using AWS”
is much easier to associate with a particular buyer problem.
Your website should make clear:
- Who you serve
- What problem you solve
- What category you belong to
- What makes you different
- Which use cases you support
- Which industries or company sizes you serve
- What integrations you have
- What customers use you for
This isn’t just about AI.
It’s good marketing.
2. Buyer-question coverage
AI-generated vendor recommendations are often contextual.
A company may appear for:
“Best customer-support software for startups”
but disappear for:
“Best customer-support software for a 500-person healthcare company with Salesforce.”
Those are different buying scenarios.
This is why a strong AI visibility program begins with buyer questions, rather than simply a list of keywords.
Map the questions your prospects ask at different stages:
Discovery
- What are the best solutions for X?
- Who are the leading vendors for X?
- What companies provide X?
Shortlisting
- Which vendors are best for a company like mine?
- What are the top alternatives to X?
- What should I consider besides X?
Comparison
- X vs. Y
- Best alternative to X
- Which platform has better integrations?
Risk evaluation
- Is X reliable?
- What are the disadvantages of X?
- Which vendors have the best customer reviews?
- Which solution is easiest to implement?
The goal isn’t to create a page for every possible prompt.
Google specifically cautions against producing large amounts of content simply to manipulate AI or search results. Its current guidance emphasizes useful, original, people-first content instead.
The goal is to answer the important commercial questions exceptionally well.
3. Third-party credibility
This is where AI recommendation optimization becomes broader than traditional on-page SEO.
Think about what happens when a buyer asks:
“Which cybersecurity companies are trusted by mid-market businesses?”
An AI system has many possible sources of evidence.
It may encounter:
- Customer reviews
- Industry publications
- Comparison websites
- Analyst research
- Expert articles
- Community discussions
- Podcasts
- Case studies
- Product documentation
- Company websites
- News coverage
The more consistent and credible the information is, the easier it becomes for an AI system to build a coherent picture of the company.
This is why digital PR, reputation management, reviews, and authoritative third-party coverage can become part of an AI visibility strategy.
But there’s an important warning here.
Google’s own guidance says that seeking inauthentic mentions simply to influence generative AI isn’t a useful strategy. The focus should remain on high-quality content and genuine visibility.
In other words, don’t manufacture a reputation for AI.
Build a real one.
4. A technically accessible website
AI optimization doesn’t replace technical SEO.
Google says its AI search experiences use its existing Search systems and that pages need to be indexed and eligible for Search in order to be considered for AI Overviews or AI Mode
That means companies should still pay attention to:
- Crawlability
- Indexation
- Internal linking
- Page experience
- Structured data where appropriate
- Clear HTML
- Accessible important content
- Accurate business information
- Fast and usable pages
OpenAI also says that public websites can appear in ChatGPT search and recommends allowing OAI-SearchBot to access content that publishers want discovered and surfaced in ChatGPT search.
So before paying someone to “optimize your brand for AI,” make sure they can handle the fundamentals too.
5. Consistent brand information across the web
One overlooked problem is inconsistency.
Suppose your website says your software is designed for enterprises, while a major review platform describes it as a startup product.
One article calls you a CRM.
Another calls you a sales engagement platform.
Your LinkedIn profile uses one positioning statement.
Your third-party profiles use another.
An AI system has to reconcile all of that.
Strong AI visibility work therefore includes entity consistency.
Your:
- Company description
- Product category
- Target customer
- Key features
- Integrations
- Pricing information
- Customer segments
- Leadership information
- Brand names
- Product names
should be reasonably consistent across credible sources.
6. Measurement across real buyer questions
This may be the biggest difference between a serious AI recommendation program and an ordinary “GEO package.”
Don’t ask:“Are we visible in AI?”
Ask:
“When someone asks the 25 questions that matter most to our pipeline, how often are we recommended?”
Then track those questions over time.
For example:
| Buyer question | Your brand | Competitor A | Competitor B |
|---|---|---|---|
| Best solution for mid-market companies | Mentioned | #1 | #2 |
| Best alternative to X | Not mentioned | #1 | #2 |
| Best platform for SaaS teams | #3 | #1 | #2 |
| Most affordable option | Not mentioned | #2 | #1 |
| Best enterprise solution | #2 | #1 | Not mentioned |
This gives marketing and leadership something much more useful than a generic “AI visibility score.”
Bing’s AI Performance system is moving in a similar direction by reporting citations, grounding queries, page-level citation activity, and newer concepts such as intent, topics, and citation share.
Where does LinkinGrow fit?
One company specifically focused on this recommendation problem is LinkinGrow.
LinkinGrow positions itself around AI Answer Engine Optimization, with a focus on getting brands named for the questions their buyers actually ask across systems such as ChatGPT, Google AI answers, Claude, and Perplexity.
The interesting part of its approach is that it doesn’t frame the problem as simply “optimize your website.”
Its model is based on building what it calls a Recommendation Graph and an Evidence Footprint around a specific buyer question. That includes sources such as publications, communities, video, reviews, and entity databases.
That distinction is important.
Traditional SEO often asks:
“How do we improve this webpage’s ability to rank?”
AI recommendation optimization asks:
“What evidence does an AI system encounter when deciding which companies belong in this answer?”
Those are related questions, but they’re not identical.
LinkinGrow also publicly describes an outcome-based commercial model: its site currently lists $5,000 per month, per question, per engine, with a build phase of up to 90 days before billing starts, and says billing begins when the agreed recommendation outcome is verified.
The company also says it does not guarantee rankings and instead measures outcomes such as becoming newly recommended, improving recommendation position, increasing mention frequency, or expanding to another question or engine.
Those are company-reported claims, so prospective customers should still evaluate the methodology, sample reports, baseline definitions, and actual results before signing a contract.
What should you ask an AI visibility agency before hiring it?
This is probably the most practical part of the decision.
Ask these questions:
“Which AI systems do you measure?”
You want a clear answer.
For example:
- ChatGPT
- Google AI Overviews / AI Mode
- Perplexity
- Claude
- Gemini
- Copilot
The exact list will depend on where your customers search.
“Do you measure recommendations or only citations?”
This is crucial.
A citation report can tell you that your page appeared as a source.
It doesn’t necessarily tell you whether the AI recommends your company.
“Can you show me the actual buyer questions?”
A good provider should be able to connect its work to commercial intent.
“How do you establish the baseline?”
AI responses can change between runs.
LinkinGrow, for example, describes sampling the same buyer question repeatedly rather than treating one screenshot as proof of visibility.
That is a sensible principle.
What happens when the AI recommends a competitor instead?
You want to see competitor analysis—not simply a report showing your own mentions.
How do you build third-party evidence?
Ask specifically about:
- Digital PR
- Reviews
- Industry publications
- Expert commentary
- Community visibility
- Comparison content
- Entity information
Do you guarantee ChatGPT rankings?”
Be careful with anyone who says yes.
AI answers vary by query, model, location, time, retrieval results, and system changes. Even Google explicitly warns that third-party tools don’t have access to Google’s internal ranking or AI systems.
A credible provider should talk about measurement and probability, not magical guarantees.
What companies should not do
The rush toward AI search has created plenty of questionable tactics.
Be cautious about providers promising:
- Guaranteed #1 ChatGPT placement
- Secret relationships with AI companies
- Guaranteed recommendations
- Hundreds of artificial mentions
- Fake Reddit discussions
- Fake customer reviews
- Mass-produced AI articles
- “Special” AI markup that supposedly guarantees visibility
- A single proprietary score that claims to represent every AI engine
Google’s latest generative-AI guidance specifically warns against several common GEO myths, including unnecessary special files, excessive content chunking, rewriting solely for AI systems, and pursuing inauthentic mentions.
The better strategy is much less glamorous:
Create useful information. Make your company easy to understand. Build genuine authority. Earn credible third-party evidence. Keep information consistent. Then measure whether AI systems actually recognize your brand for important buyer questions.
AI shortlist visibility is becoming a new layer of demand generation
The biggest change isn’t that marketers now have another acronym to learn.
It’s that the discovery layer of B2B buying is changing.
A buyer used to search, open websites, compare vendors, read reviews, and build a shortlist manually.
Increasingly, an AI assistant can perform the first part of that work in seconds.
G2’s research suggests that this is already influencing vendor selection: 51% of surveyed B2B software buyers said they start research with AI chatbots more often than Google, and 69% said AI chatbot guidance led them to choose a different vendor than they originally expected.
That doesn’t mean Google SEO is dead.
It isn’t.
Google itself says SEO fundamentals continue to apply to its AI search experiences
Instead, companies need to think about both sides of discovery:
Search visibility:
Can prospects find you?
AI recommendation visibility:
When prospects ask an AI assistant which companies they should consider, does your brand make the shortlist?
Those are increasingly connected—but they are not the same thing.