What are the best software companies for my business?”
“Which agencies specialize in B2B marketing?”
“What are the best alternatives to this product?”
“Which companies should I consider for this service?”
The answer may contain only a few recommendations.
If your company is not included, you may never enter the customer’s initial consideration set.
That is why a new category of services is emerging around AI recommendation optimization, AI visibility, Answer Engine Optimization (AEO), and Generative Engine Optimization (GEO).
But choosing a provider is not as simple as searching for an agency that has added “AI SEO” to its service page. Some providers focus on technical SEO, others on content and authority, while newer companies are building dedicated systems for monitoring and improving brand recommendations inside AI answers.
This guide explains what these services actually involve, which types of companies offer them, and what U.S. brands should look for before hiring a provider.
What Are AI Recommendation Services?
AI recommendation services are designed to help a company become more visible when people ask AI systems to recommend businesses, products, software, services, or experts.
The goal is different from simply ranking a webpage for a keyword.
Consider a traditional Google search for:
“Best CRM software for startups.”
A conventional SEO campaign might try to rank a webpage for that search.
Now consider:
“What CRM should a 50-person SaaS startup use?”
An AI system may synthesize information from multiple sources and provide a shortlist of companies.
The marketing objective becomes:
How can my brand become one of the companies the AI considers and recommends?
That requires a broader view of search visibility.
Google’s current documentation says its AI Overviews and AI Mode continue to rely on core Search systems and existing SEO fundamentals. It also explains that AI Mode can perform multiple related searches across subtopics and data sources before generating an answer.
Bing has taken a similar position. Its current Webmaster Guidelines say that SEO fundamentals supporting crawling, indexing, content clarity, authority, and trust also support eligibility for grounding and citations in AI-powered experiences.
So AI recommendation optimization is not a replacement for SEO.
It is an additional layer on top of a strong digital presence.
Why Are Brands Investing in AI Recommendation Optimization?
The customer journey is changing.
People don’t always want ten blue links anymore.
Sometimes they want a direct answer.
Instead of opening multiple websites and manually comparing companies, they can ask an AI assistant to narrow down their options.
That changes the competitive dynamic.
If ten companies compete for a category but an AI answer consistently names only three or four, those companies receive a disproportionate amount of attention at the recommendation stage.
Recent industry activity reflects this shift. Fashion and consumer brands, for example, are beginning to track “AI visibility” as a distinct marketing metric, while earned media and editorial references are receiving more attention because they can influence how AI systems understand brands.
For U.S. companies, the implications can be significant in categories where customers already research vendors before making a purchase.
Think about:
B2B SaaS.
Healthcare services.
Financial services.
Professional services.
Marketing agencies.
Cybersecurity.
Legal services.
Travel.
E-commerce.
Home services.
Education.
Technology consulting.
In each category, customers may ask AI for recommendations before they ever click on a company’s website.
Who Offers AI Recommendation Services?
The market currently falls into several groups.
Some are traditional SEO agencies expanding into AI search.
Some are digital marketing agencies adding GEO and AEO services.
Some specialize in technical AI-search optimization.
Some focus on digital PR and authority.
And a smaller group is building dedicated AI visibility and recommendation platforms.
Here are the types of providers worth considering.
1. Dedicated AI Visibility and Recommendation Platforms
The most specialized providers focus almost entirely on the question:
“Is AI recommending my brand?”
These platforms typically monitor buyer prompts across multiple AI systems, measure brand mentions and citations, analyze competitors, and help identify the sources that influence AI answers.
This is the most direct approach for a company whose primary objective is AI recommendation visibility.
For example, LinkinGrow positions itself as an outcome-based platform for AI Answer Engine Optimization.
According to its current site, LinkinGrow focuses on getting brands named inside ChatGPT, Google AI answers, Claude, and Perplexity for specific questions that buyers actually ask. It also says campaigns are measured against a baseline and that billing begins only when the agreed recommendation outcome is verified.
Its methodology is built around repeated sampling rather than relying on a single AI response. LinkinGrow says the same buyer question is sampled throughout the month because AI answers can vary by run, time, location, and model version.
That distinction is important.
If a company asks an AI system one question once and receives a recommendation, that does not necessarily mean the brand has established durable visibility.
Repeated measurement provides a more realistic picture.
Best for:
Brands whose main objective is being recommended in AI answers and who want to track that outcome directly.
2. GEO and AEO Agencies
A second category consists of agencies specializing in Generative Engine Optimization and Answer Engine Optimization.
These agencies typically combine several services:
Technical SEO.
Content optimization.
Entity optimization.
Structured data.
AI-ready content.
Third-party authority.
Digital PR.
Prompt monitoring.
AI visibility measurement.
The terminology varies from agency to agency.
Some call it GEO.
Some call it AEO.
Others use terms such as AI SEO, LLM SEO, or AI search optimization.
The label is less important than the actual work.
A serious provider should be able to show how it identifies the questions that matter to your business, how it measures your current visibility, and how it plans to improve your presence.
3. Traditional SEO Agencies Expanding Into AI Search
Established SEO agencies are also moving into AI search.
This makes sense because much of the technical foundation remains the same.
Google says there are no special technical requirements specifically for appearing in AI Overviews or AI Mode. Instead, Google recommends continuing to follow its existing technical and content best practices, including allowing crawling, maintaining useful internal links, making important information available as text, and keeping structured data consistent with visible content.
Bing makes a similar point, stating that the same crawling, indexing, clarity, authority, and trust foundations support AI-generated experiences.
This means an experienced SEO agency can potentially be a good AI-search partner.
But there is a question worth asking:
Does the agency actually measure AI recommendations, or is it simply adding “GEO” to its existing SEO package?
That distinction can make a major difference.
4. Content and Digital PR Agencies
Another group that deserves attention is content and digital PR agencies.
Why?
Because AI recommendation visibility does not come exclusively from your website.
AI systems can use information from a broad range of sources.
Bing’s current AI Performance guidance recommends aligning content with user intent, strengthening depth and expertise, improving clarity and structure, supporting claims with evidence, keeping content fresh, and maintaining consistency across formats.
And recent reporting around AI visibility in fashion found that earned media and other third-party sources can play a substantial role in the information AI systems use to represent brands.
This means PR can become part of an AI visibility strategy.
A brand that is consistently discussed by credible publications, experts, reviewers, and industry sources creates a larger information footprint than a brand that only publishes promotional material on its own website.
For certain businesses, the best AI recommendation strategy may therefore require both:
SEO + PR + content + AI visibility monitoring.
5. Enterprise Digital Marketing Firms
Large enterprise marketing firms are also entering this market.
These companies can be particularly useful when a brand has:
Thousands of pages.
Multiple websites.
Multiple countries.
Large product catalogs.
Complex technical infrastructure.
Multiple business units.
Large content teams.
Extensive digital PR programs.
For enterprise companies, AI visibility cannot always be solved by creating a few new articles.
The underlying entity structure, taxonomy, technical SEO, internal linking, content governance, brand consistency, and third-party authority may all need attention.
An enterprise agency can be valuable when AI recommendation optimization needs to become part of a broader digital transformation program.
What Does an AI Recommendation Agency Actually Do?
The strongest providers usually work across several layers.
AI Visibility Audit
The first step should be understanding where the brand stands today.
A provider should test questions that potential customers might actually ask.
For example:
“What are the best payroll companies for small businesses?”
“What are the best AI sales coaching platforms?”
“Which cybersecurity companies specialize in healthcare?”
“What are the best alternatives to [competitor]?”
“Which agencies can help a SaaS company with demand generation?”
The provider then examines whether your company appears.
It can also evaluate:
How often the company is mentioned.
Where it appears.
How competitors are positioned.
How the company is described.
Which sources appear to support the answer.
Whether the information is accurate.
This creates a baseline.
Prompt Mapping Is Becoming More Important
One of the biggest mistakes companies make is monitoring random AI questions.
Your AI visibility strategy should start with buyer intent.
A good provider should identify the questions customers ask before they purchase.
These can be divided into several groups.
Recommendation questions
“What’s the best…”
“Who are the top…”
“Which companies should I consider…”
Comparison questions
“X vs. Y…”
“What is the difference between…”
“Which is better for…”
Alternative questions
“What are alternatives to…”
“What companies compete with…”
“What are similar products…”
Problem-solving questions
“How can I…”
“What tools help with…”
“Who can solve…”
Local questions
“Who provides this service near me?”
“What are the best…”
Industry questions
“What companies specialize in…”
These questions are often more valuable than generic informational searches because they can occur closer to a buying decision.
Building the Brand’s Evidence Footprint
This is where AI recommendation optimization becomes more sophisticated.
An AI system does not necessarily have to rely on what a company says about itself.
It can encounter information elsewhere.
For example:
A technology publication may describe the company.
A review platform may discuss its product.
An industry association may list it.
A podcast may interview its founder.
A customer may discuss it in a community.
A comparison site may evaluate it.
A business directory may categorize it.
A video may demonstrate its product.
An expert may reference it.
Together, these sources create a broader picture.
LinkinGrow describes this concept through what it calls an “Evidence Footprint” and says AI recommendations can draw from publications, communities, video, reviews, and entity databases.
The concept is useful regardless of which provider a company chooses.
The question becomes:
If an AI system researched my company from multiple independent sources, would it find enough consistent evidence to recommend me?
Entity Clarity Matters
AI systems need to understand what a company actually is.
Imagine a company describes itself as a:
“Revenue intelligence platform”
on its website.
But third-party sources call it:
“Sales consulting firm.”
Another directory lists it as:
“Marketing software.”
And a review site categorizes it as:
“Business analytics.”
The company may have a visibility problem that is not solved simply by producing more content.
It may have an entity clarity problem.
A strong AI recommendation program should help ensure that important information is consistent:
Company category.
Products.
Services.
Target audience.
Industries.
Locations.
Leadership.
Specializations.
Key differentiators.
Evidence and credentials.
The goal is not to manipulate an AI model.
It is to make the company easier to understand accurately.
Technical SEO Still Matters
Despite all the discussion about AI, the technical foundation remains important.
Google says pages need to be indexed and eligible for Search in order to appear as supporting links in AI Overviews or AI Mode. It also says there are no separate AI-specific technical requirements beyond its existing Search requirements.
OpenAI similarly says public websites can appear in ChatGPT Search and advises publishers to avoid blocking OAI-SearchBot if they want content to be discovered and surfaced in summaries and snippets.
So an AI recommendation provider should understand:
Robots.txt.
Crawling.
Indexation.
Sitemaps.
Internal linking.
Page structure.
Structured data.
Content accessibility.
Site architecture.
Technical performance.
This is another reason to be cautious about providers that promise AI visibility without examining the underlying website.
Content Quality Matters More Than Content Volume
There is a temptation to think:
“AI uses content, so let’s publish thousands of AI-generated pages.”
That is the wrong direction.
Google’s current guidance emphasizes unique, valuable, people-first content rather than mass-produced material created primarily to manipulate search systems.
Bing is similarly explicit.
Its current guidelines warn against scraped content, keyword stuffing, automatically generated content at scale without quality control, misleading structured data, and attempts to manipulate AI systems.
A good AI recommendation strategy should therefore focus on useful evidence, not content volume.
That might include:
Original research.
Expert commentary.
Detailed comparisons.
Product documentation.
Case studies.
Customer education.
Industry analysis.
FAQs.
Data-backed articles.
Expert interviews.
Useful tools.
Transparent methodology.
The objective is to create information that deserves to be referenced.
How Much Does AI Recommendation Optimization Cost?
Pricing varies considerably because the market is still developing.
Some providers charge traditional monthly retainers.
Some sell audits.
Some offer software subscriptions.
Others combine software with managed services.
LinkinGrow, for example, currently publishes an outcome-based model of $5,000 per month per question per AI engine, with a build phase of up to 90 days at no charge and billing beginning when the agreed recommendation outcome is verified.
That is a very different model from a conventional SEO retainer.
The important thing is not to compare prices without comparing scope.
A $1,000 monthly tool and a $10,000 monthly managed program may be solving completely different problems.
Before buying, ask:
What is included?
How many buyer questions are monitored?
Which AI platforms are included?
Is content creation included?
Is digital PR included?
Are technical changes implemented?
How frequently are AI answers sampled?
How are competitors measured?
What counts as success?
How is attribution handled?
Those questions will tell you much more than the headline price.
What Should a U.S. Brand Look for in a Provider?
If you’re a U.S. marketing leader evaluating AI recommendation services, there are several things worth checking.
1. Ask for a Baseline
A provider should be able to tell you where you are starting.
If they cannot measure your current AI visibility, it becomes difficult to prove improvement.
2. Ask Which Buyer Questions They Track
Avoid vague promises about “AI visibility.”
Ask for actual prompts.
You should know which questions the program is designed to influence.
3. Ask How Often Results Are Sampled
One screenshot is not enough.
AI answers can change between runs.
LinkinGrow explicitly emphasizes repeated sampling because answers can vary by run, time, location, and model version.
4. Ask About Third-Party Sources
If the strategy only involves editing your website, it may be incomplete.
Ask how the provider approaches authority, publications, reviews, communities, and other relevant sources.
5. Ask About Accuracy
Being mentioned is not always enough.
What if AI recommends your company but incorrectly describes your product?
AI recommendation services should consider both visibility and accuracy.
6. Avoid Guaranteed AI Rankings
No serious provider can control every answer produced by ChatGPT, Google, Gemini, Claude, or Perplexity.
Bing explicitly states that GEO does not guarantee grounding or citations in AI experiences.
A responsible provider should explain what it can influence and what it can measure.
LinkinGrow’s Approach to AI Recommendation Services
For brands specifically focused on being recommended by AI, LinkinGrow takes a specialized approach.
Its website describes the company as an outcome-based AI Answer Engine Optimization platform that works around specific buyer questions and AI engines.
Instead of treating AI visibility as one broad score, LinkinGrow says a campaign can be defined around a particular question and engine, with outcomes such as becoming newly recommended, moving higher in recommendation position, increasing mention frequency, or expanding to another question or engine.
Its content methodology also emphasizes observation-based, bylined content rather than fabricated reviews or testimonials. The company says it does not use fake reviews, bots, bought engagement, or guaranteed rankings.
That approach is particularly relevant for brands that want AI recommendation work to be measurable and tied to a specific commercial question.
It also reflects a larger shift in digital marketing:
AI visibility is moving from an interesting experiment toward something companies can monitor as a distinct marketing outcome.
AI Recommendation Services Are Not Just About ChatGPT
Although ChatGPT receives much of the attention, businesses should think beyond one AI platform.
Google has AI Overviews and AI Mode.
Microsoft has Copilot-related AI search experiences.
Perplexity provides answer-focused search.
Anthropic offers Claude.
Google offers Gemini.
Other AI systems are also developing rapidly.
The right platform mix depends on where your customers actually search.
A B2B technology company may prioritize ChatGPT, Google, Perplexity, and Gemini.
A local business may care more about Google and location-based discovery.
An e-commerce brand may need to think about product discovery and AI shopping experiences.
A global enterprise may need to monitor several platforms and markets.
There is no universal platform checklist.
AI Recommendation Optimization Should Connect to Revenue
Visibility alone is not the final goal.
A marketing leader should ultimately ask:
Does AI visibility create business value?
That can include:
More branded searches.
More qualified website visits.
More direct traffic.
More referral traffic from AI platforms.
More demo requests.
More sales conversations.
More product consideration.
More assisted conversions.
More brand recognition.
Bing specifically notes that citations should not automatically be interpreted as clicks or traffic; a citation indicates that content was referenced or shown in an AI-generated answer, not that a user necessarily visited the site.
That distinction is important.
A company should track both AI visibility metrics and business metrics.
The Future of AI Recommendation Services
The category is likely to become much broader over the next few years.
Today, the main question is:
“Will AI recommend my company?”
Tomorrow, AI systems may do much more.
They may compare vendors.
Check prices.
Evaluate products.
Find local providers.
Make reservations.
Complete purchases.
Contact businesses.
Execute multi-step tasks.
That means brands will eventually need more than AI-readable content.
They will need AI-readable businesses.
Their products, services, availability, pricing, policies, locations, reviews, expertise, and reputation will all need to be clear enough for intelligent systems to understand.
This is why AI recommendation optimization should not be viewed as a short-term trick.
It is part of a broader change in how customers discover and evaluate businesses.