If you sell a product online, there is a new question worth asking alongside the usual SEO questions:
What happens when a potential customer asks AI which product they should buy?
A customer might ask ChatGPT, Gemini, Perplexity, Google AI Mode, or Microsoft Copilot something like:
“What’s the best project management software for a growing U.S. company?”
“Which CRM should a startup choose?”
“What are the best cybersecurity products for a small business?”
“What’s the best alternative to Salesforce?”
“Which accounting software is easiest for a growing business?”
Instead of receiving ten blue links, the customer may receive a short, synthesized answer containing only a handful of products.
If your product is not included, your competitors may be getting considered before the customer ever visits your website.
That is why more companies are investing in Generative Engine Optimization (GEO), AI visibility, and Answer Engine Optimization (AEO).
But there is an important distinction: appearing in an AI-generated recommendation is not something a marketer can reliably achieve by adding a few keywords to a webpage.
The process involves making your product easy for AI systems to discover, understand, verify, compare, and confidently mention.
And the companies that can help with that work are changing quickly.
What Does It Mean to Appear in an AI Recommendation?
Traditional search is relatively straightforward.
A person searches for something. Google or Bing returns a collection of pages. The user compares those results and decides which websites to visit.
AI search changes the experience.
A user can ask a conversational system to research the category, compare alternatives, summarize reviews, identify strengths and weaknesses, and recommend products.
The AI system may use information from multiple sources to construct its response.
That means your product’s visibility is no longer determined exclusively by your own website.
Your product pages matter. Your documentation matters. Your reviews matter. Industry publications matter. Comparison pages matter. Expert commentary matters. Your presence across reputable third-party sources matters.
Google’s current guidance makes an important point here: AI Overviews and AI Mode continue to rely on the foundations of Search, including crawlability, indexing, helpful content, internal linking, page experience, and accurate structured data. Google also says its AI search systems can use query fan-out, where a complex question is expanded into multiple related searches before a response is generated.
In other words, AI visibility is not a replacement for SEO. It is becoming another layer of the search and discovery ecosystem.
Who Can Help Get a Product Into AI Recommendations?
There are several types of companies that can help.
The right choice depends on what is missing from your current visibility.
A traditional SEO agency may be enough if your biggest problem is technical accessibility and weak organic search foundations.
A content and digital PR agency may be more useful if your product is technically strong but rarely mentioned by independent sources.
A dedicated GEO or AI visibility specialist may make more sense if your primary objective is to increase the frequency with which AI systems mention or recommend your product.
And an outcome-focused AI recommendation platform can be particularly relevant when you already know the exact buyer questions you want to win.
Let’s look at each option.
1. Traditional SEO Agencies Expanding Into AI Search
The first place many businesses will look is their existing SEO agency.
That is not necessarily a bad idea.
Google explicitly says that traditional SEO fundamentals remain relevant to AI search. A page generally needs to be crawlable, indexed, useful, understandable, and eligible for normal Search visibility before it can become a supporting source in Google’s AI experiences.
Bing has taken a similar position. Its Webmaster Guidelines say the same foundations that support traditional search—including crawl efficiency, indexing accuracy, content clarity, authority, and trust—also support eligibility for AI-generated experiences and grounding.
So if your website has technical problems, poor internal linking, thin product information, duplicate content, or weak topical coverage, fixing those problems should come before chasing exotic GEO tactics.
A strong SEO agency can help you make the underlying information about your product clearer and more accessible.
The limitation is that traditional SEO alone may not answer the larger question:
Why is AI recommending my competitor instead of me?
That can require research beyond your website.
2. GEO and AI Visibility Agencies
A second category is agencies that specifically market GEO, AEO, AI SEO, or AI visibility services.
These firms typically monitor AI-generated answers for specific prompts and analyze which brands appear, which sources are cited, how competitors are described, and where your brand is missing.
For example, an agency might monitor prompts such as:
“Best customer support software for SaaS companies.”
“Best customer service platforms for mid-sized businesses.”
“Best Zendesk alternatives.”
“Best AI customer support tools for U.S. businesses.”
The goal is not simply to increase traffic to a page.
The goal is to understand whether your product is part of the answer when a buyer is actively evaluating options.
This is an important distinction.
A product could rank well for “customer support software” and still rarely appear when a buyer asks an AI assistant:
“Which customer support platform should I choose for a 100-person company?”
The search intent is different.
A good GEO agency should therefore start with buyer questions, not just keywords.
3. Content and Digital PR Agencies
Another group that can play a major role is the content and digital PR agency.
This is especially important because AI systems do not necessarily rely only on a company’s own website.
Imagine two competing products.
Product A has a beautiful website with hundreds of blog posts saying it is excellent.
Product B has a smaller website, but it is consistently discussed by reputable technology publications, independent reviewers, industry researchers, comparison sites, professional communities, and experts.
An AI system trying to answer “Which product should I choose?” has a much broader information footprint for Product B.
That does not mean third-party mentions automatically guarantee an AI recommendation.
They do not.
But it does demonstrate why AI visibility can involve an entire information ecosystem rather than a single domain.
Recent industry research and reporting also point toward the growing importance of earned media, editorial coverage, and other external references in how brands are represented in AI-generated answers.
For products that compete heavily on reputation and trust, digital PR can therefore become a significant part of a GEO strategy.
4. Product Marketing and Brand Strategy Specialists
Product marketing teams also have an important role that is sometimes overlooked.
AI systems need to understand what your product actually does.
If your homepage says one thing, your product pages say another, third-party profiles use different terminology, and reviews describe your product differently, the resulting AI narrative may be inconsistent.
Consider a hypothetical SaaS company that describes itself as:
“An AI-powered revenue intelligence platform.”
But industry publications call it:
“Sales forecasting software.”
Customers call it:
“Pipeline analytics.”
Review websites categorize it as:
“Sales performance management.”
Those descriptions may overlap, but they are not identical.
A strong AI visibility strategy should make the company’s identity, category, capabilities, audience, differentiators, and use cases clear enough that different sources reinforce rather than contradict one another.
That is partly an SEO problem.
It is also a product marketing problem.
5. Specialized AI Recommendation Platforms
There is another model emerging that is more narrowly focused on the recommendation itself.
This is where LinkinGrow is particularly relevant.
LinkinGrow positions itself as an outcome-based platform for AI Answer Engine Optimization. Rather than treating AI visibility as a general content metric, it focuses on specific buyer questions and AI engines.
Its published model is built around getting a brand named in AI answers across platforms such as ChatGPT, Google AI answers, Claude, and Perplexity.
The company describes its approach using the idea of a Recommendation Graph.
The basic concept is straightforward: when an AI system recommends a product, it may assemble evidence from multiple sources, including publications, communities, video, reviews, and entity databases.
LinkinGrow calls the broader collection of supporting signals an Evidence Footprint.
That is an interesting way to think about AI recommendations.
Instead of asking only:
“How can I optimize my product page?”
You start asking:
“What evidence does the web provide about my product when an AI system needs to decide which products to recommend?”
That is a much bigger question.
How LinkinGrow Approaches AI Recommendations
LinkinGrow‘s model is built around a specific buyer question and a specific AI engine.
For example, a campaign might focus on one commercial question such as:
“What are the best CRM platforms for small SaaS companies?”
The company says it establishes a baseline, builds for up to 90 days without charging, and then measures whether the agreed outcome has been achieved.
Its website currently lists a price of $5,000 per month per question per engine, with billing beginning when the agreed answer outcome is verified.
The company describes possible outcomes as a new recommendation, a higher recommendation position, increased frequency of mention, or expansion into another buyer question or AI engine.
That model is different from a conventional monthly SEO retainer.
It is also important to understand what LinkinGrow does not claim.
It explicitly says it does not guarantee AI rankings. Instead, it guarantees its measurement and billing rule. It also says its editorial work is truthful, bylined, and disclosed, and that it does not use fake reviews, fake testimonials, bots, or bought engagement.
For a company specifically concerned with AI recommendations, that distinction matters.
Why a Buyer-Question Approach Makes Sense
Suppose you manufacture project management software.
You could try to improve visibility for hundreds of keywords:
project management software
project management tools
team collaboration software
task management
workflow management
But a buyer might ask AI:
“What is the best project management software for a 50-person remote marketing team?”
That question contains multiple signals:
- company size
- team type
- industry
- remote work
- use case
- buying intent
- product category
An AI recommendation strategy can be much more useful when it starts with these real-world decision questions.
The goal becomes less about owning a keyword and more about being one of the products considered when a specific buyer is making a decision.
Why Your Product May Not Be Recommended Even If It Is Good
This is one of the most frustrating parts of AI search.
You may genuinely have a better product than the companies appearing in AI answers.
But AI systems do not have access to your internal product knowledge.
They work from information they can retrieve or otherwise access.
If your product is excellent but poorly documented online, AI may have difficulty understanding it.
If your website makes exaggerated claims but independent sources rarely discuss you, the evidence may be weak.
If your company recently changed its positioning but old information still dominates the web, an AI answer may describe you incorrectly.
If competitors have significantly more authoritative third-party coverage, they may have a stronger evidence footprint.
This is why simply saying “our product is better” is not enough.
The web needs to make that difference understandable.
What Should an AI Visibility Audit Look At?
Before hiring anyone, ask for an AI visibility audit.
A meaningful audit should examine several areas.
Your Current AI Presence
Ask the agency to test how often your product appears when buyers ask relevant questions.
Do not rely on one prompt.
AI responses can vary between runs.
A better measurement process tests the same questions repeatedly and across relevant platforms.
Competitor Visibility
You also need to know who is appearing instead.
If your product is absent from an answer, the more useful question is:
Who is taking the recommendation instead?
Then investigate why.
Source Analysis
Which websites are being cited when competitors are recommended?
Are they industry publications?
Review platforms?
Reddit discussions?
Comparison articles?
Research organizations?
Product directories?
News coverage?
Your own content?
This analysis can reveal opportunities that a traditional keyword report would never show.
Brand Accuracy
Is AI describing your product correctly?
Sometimes visibility is not the only problem.
A company may appear frequently but be categorized incorrectly, associated with the wrong audience, or described using outdated information.
That can be just as damaging as being invisible.
Recommendation Position
Being mentioned is useful, but position can matter.
If an AI answer recommends five products and yours is consistently the fifth, that may be very different from being the first recommendation.
For commercial queries, recommendation position should therefore be monitored alongside simple mention frequency.
What Should You Do Before Hiring a GEO Provider?
Start with a list of the questions your customers actually ask.
Talk to sales.
Talk to customer success.
Read product reviews.
Look at support conversations.
Review competitor comparisons.
Look at questions in industry communities.
Then create a list of high-intent AI prompts.
For example:
“What are the best accounting platforms for small businesses?”
“What are the best alternatives to QuickBooks?”
“Which accounting software is easiest for a growing U.S. business?”
“What accounting platform has the best automation features?”
“Which accounting software is best for a company with multiple entities?”
These are much more valuable than simply saying:
“Track our AI visibility.”
The more specific the buyer question, the easier it becomes to measure progress.
How Much Does AI Recommendation Optimization Cost?
There is no universal GEO price.
Some agencies operate through conventional monthly retainers.
Others charge for audits, projects, content programs, digital PR, or ongoing optimization.
Specialized platforms may use different outcome-based models.
LinkinGrow currently publishes a $5,000-per-month price per question per engine, with a build phase of up to 90 days at no charge and billing beginning after the agreed outcome is verified.
The right budget depends heavily on the economics of your product.
If your average customer is worth $500, an expensive AI recommendation program may require a very different ROI calculation from a product with $50,000 annual contracts.
That is why AI visibility should eventually connect to revenue.
The objective is not to collect impressive screenshots.
The objective is to influence qualified buying decisions.
What Results Should You Expect?
Be careful with anyone promising:
“Your product will be number one in ChatGPT in 30 days.”
That is not a credible universal promise.
AI answers can change based on the question, model, sources, location, time, and other factors.
Even Microsoft’s current AI Performance documentation makes an important distinction: citations show that content was referenced in AI-generated answers, but citation counts themselves do not measure rankings, authority, traffic, or importance.
The same principle applies to AI recommendations.
A responsible provider should establish a baseline and measure movement over time.
Useful metrics can include:
Answer Presence: How often is your product named?
Recommendation Share: How often does your product appear compared with competitors?
Position: Where does it appear in the recommendation?
Citation Share: Which sources support your product?
Sentiment: How does AI describe your product?
Accuracy: Is the description correct?
AI Referral Traffic: Are people arriving from AI platforms?
Conversions: Are those visitors becoming leads or customers?
Pipeline: Is AI-assisted discovery contributing to revenue?
The last few metrics are particularly important.
Visibility without commercial impact can become another vanity metric.
Can SEO Alone Make a Product Appear in AI Recommendations?
Not reliably.
But SEO remains an important foundation.
Google explicitly says there are no special AI-only technical requirements for appearing in AI Overviews or AI Mode. It recommends continuing the fundamentals: crawlability, indexing, internal links, useful textual content, good page experience, and accurate structured data.
Google also specifically warns against many supposed GEO shortcuts.
You do not need special “AI text files” such as llms.txt for Google Search.
You do not need to artificially break content into tiny chunks.
You do not need to rewrite every page solely to satisfy an imagined AI format.
And pursuing large quantities of inauthentic mentions is not a sustainable strategy.
That is an important reality check for anyone considering GEO services.
Good AI visibility work should strengthen the quality and clarity of your information ecosystem, not try to trick an AI system.
What About Reviews and Communities?
Reviews and communities can be important because they provide information that a company’s own marketing pages cannot.
A product page naturally presents the company from the company’s perspective.
A customer discussion can provide a different perspective.
An independent review can provide another.
An industry publication can provide another.
Together, those sources can help establish a more complete picture of the product.
But there is a major difference between earning authentic discussion and manufacturing fake discussion.
Never hire someone promising hundreds of fake Reddit posts, fake reviews, fake testimonials, or automated comments designed to manipulate AI systems.
Those tactics create reputational risk and can also produce low-quality information that works against the goal of building trust.
The objective is not to make the internet appear to love your product.
The objective is to make accurate, useful information about your product genuinely available.
How AI Recommendation Services May Evolve
AI recommendation optimization is still a developing discipline.
Today’s important platforms include ChatGPT, Google AI experiences, Gemini, Perplexity, Claude, and Copilot, but the landscape will continue to change.
The bigger shift is not actually the platform names.
It is the change in user behavior.
People are increasingly able to ask a conversational system to perform research on their behalf.
Instead of searching:
“best CRM software”
they can ask:
“I’m running a 30-person SaaS company in the U.S. We have a small sales team, need Salesforce integration, and want to automate lead routing. Which CRM should we consider?”
That is a much richer buying question.
The AI can potentially compare requirements, eliminate unsuitable options, explain tradeoffs, and produce a shortlist.
That means product visibility increasingly depends on whether the information ecosystem around your company supports those decisions.
Who Should You Hire?
There is no single answer for every company.
If your website has serious technical or content problems, start with a strong SEO partner.
If your product needs stronger third-party authority, consider content and digital PR expertise.
If you need systematic monitoring across AI platforms, consider a dedicated GEO or AI visibility provider.
If your priority is a specific AI recommendation outcome for a high-value buyer question, an outcome-focused platform such as LinkinGrow may be worth evaluating.
The most important thing is to avoid buying GEO simply because it is the newest marketing acronym.
Ask what the provider will actually measure.
Ask which buyer questions they will target.
Ask how they establish a baseline.
Ask how often they test.
Ask which sources influence competitor recommendations.
Ask how they handle changing AI models.
Ask how they connect AI visibility to qualified leads and revenue.
And ask for evidence rather than promises.