How can my product appear in ChatGPT recommendations?

If you sell a product online, there is a new discovery problem that looks very different from traditional SEO.

A customer may no longer begin with Google and click through ten product pages. They may simply ask ChatGPT something like:

“What are the best project management tools for a 20-person marketing team?”

Or:

“Which running shoes are best for someone who runs five miles a day?”

Or:

“What software should a U.S. small business use for managing customer support?”

The important question is no longer only “Can my website rank?”

It is also:

“Will ChatGPT understand my product well enough to consider and recommend it?”

There is no legitimate button you can press to guarantee that ChatGPT will recommend your product. OpenAI says ChatGPT product results are selected independently based on relevance to the user’s intent, product and merchant information, and other available signals.

That means the goal should not be to manipulate ChatGPT. The better strategy is to make your product easy to discover, accurately represented, useful for a specific customer need, and supported by trustworthy information across the web.

First, Understand What Being Recommended Means

There are several different ways a product or brand can appear in ChatGPT.

It might be:

mentioned in a normal answer;
cited as a source;
included in a comparison;
shown as a product result;
included in a personalized shopping guide;
recommended as one of several alternatives;
or named as the best option for a particular use case.

These aren’t necessarily the same thing.

For example, imagine someone asks:

“What are the best accounting platforms for a small U.S. business?”

ChatGPT might discuss several companies in a written answer.

But when a user has explicit shopping intent, ChatGPT can also display product options with product information, images and links to merchants. OpenAI says those product results are independently selected rather than being advertisements

So before trying to improve visibility, decide what outcome you actually want.

A citation is not necessarily a recommendation. A mention is not necessarily a recommendation. And a product listing is not necessarily a recommendation.

For most businesses, the valuable outcome is being considered when a customer is actively deciding what to buy.

1. Start With the Questions Your Customers Actually Ask

One of the biggest mistakes companies make is optimizing for their product name instead of the customer’s problem.

A customer doesn’t always ask:

“Tell me about Brand X.”

They might ask:

“What’s the best CRM for a small sales team?”
“Which project management tool is easiest for remote teams?”
“What is the best standing desk under $700?”
“Which cybersecurity platform is suitable for a 100-person company?”
“What are alternatives to [competitor]?”
“What’s the best option for a company that needs X but doesn’t need Y?”

Those are recommendation questions.

Create a list of 20–50 realistic buyer questions around your product category.

Then divide them into groups:

Question type	Example
Best-of	“What are the best tools for X?”
Comparison	“X vs. Y—which is better?”
Alternative	“What are good alternatives to X?”
Budget	“What is the best X under $500?”
Use case	“What is the best X for a small business?”
Problem	“How can I solve X?”
Industry	“What X works best for healthcare companies?”
Experience	“Which X is easiest to use?”

This gives you something much more valuable than a list of keywords.

It gives you a map of the decisions where your product could potentially become relevant.

2. Make Sure ChatGPT Can Actually Discover Your Website

Before worrying about content strategy, make sure there isn’t a technical barrier.

OpenAI’s publisher guidance says public websites can appear in ChatGPT search and recommends allowing OAI-SearchBot to access content if you want that content to be discovered, surfaced, cited and linked in ChatGPT.

That means your technical SEO fundamentals still matter.

Check that:

important product pages are crawlable;
important pages aren’t accidentally blocked in
pages aren’t unintentionally marked
product information is available as readable HTML;
internal links connect important pages;
product information is consistent;
your website works well on mobile;
pages load reliably;
your canonical URLs are correct.

This isn’t a special “ChatGPT trick.”

It’s basic discoverability.

Google’s current guidance for AI search makes a similar point: existing SEO fundamentals remain important for AI-generated search experiences, including crawlability, internal linking, useful content and accessible textual information.

3. Give AI Systems Something Specific to Understand

A product page that says:

“The world’s leading innovative solution for modern businesses.”

doesn’t tell a buyer—or an AI system—very much.

Compare that with:

“A customer-support platform designed for U.S. SaaS companies with 10–100 employees that need shared inboxes, automated routing and knowledge-base integration.”

The second description provides considerably more context.

Your product pages should make basic facts obvious:

What is the product?
Who is it for?
What problem does it solve?
What does it replace?
What are its important features?
What does it cost?
What are its limitations?
Who should not buy it?
How does it compare with alternatives?
What integrations does it support?
What industries use it?
What makes it different?

The goal isn’t to stuff pages with keywords.

The goal is clarity.

4. Build Content Around Buying Decisions, Not Just Keywords

This is where many businesses misunderstand GEO.

Creating 100 generic articles such as:

“What is CRM?”
“What is marketing?”
“What is software?”
“What is customer service?”

is unlikely to create a strong recommendation footprint by itself.

Instead, build content that helps someone make an actual decision.

For example, a SaaS company could publish:

Best CRM for small U.S. sales teams
CRM comparison for startups
CRM alternatives for HubSpot users
CRM for companies with remote sales teams
CRM pricing comparison
CRM implementation checklist
What to look for when switching CRMs
CRM features that matter for B2B sales

Google’s current generative-AI guidance specifically emphasizes unique, useful, people-first, non-commodity content rather than content created primarily to manipulate AI results.

That principle is important beyond Google.

If your content genuinely helps buyers understand a category, it gives AI systems more useful material to work with.

5. Publish Original Evidence, Not Just Marketing Copy

This may be one of the most important differences between a website that merely describes a product and one that develops authority.

Suppose your company claims:

“Our software is easier to implement.”

That’s a marketing statement.

Now imagine you publish:

an implementation study;
actual onboarding times;
customer research;
product benchmarks;
documented workflows;
before-and-after results;
original survey data;
technical documentation;
transparent product comparisons.

That’s evidence.

Google’s guidance for generative AI search specifically highlights original perspectives and first-hand experience as valuable forms of content rather than simply rewriting information that already exists elsewhere.

For a recommendation system, evidence can make your product easier to understand and differentiate.

6. Don’t Focus Only on Your Own Website

This is another important shift.

Traditional SEO often starts with:

“How do I improve my website?”

AI recommendation visibility can require a broader question:

“What does the wider web say about my company?”

A buyer’s decision might be informed by:

industry publications;
independent reviews;
comparison sites;
communities;
product directories;
professional organizations;
YouTube;
podcasts;
interviews;
case studies;
analyst coverage;
reputable marketplaces;
social discussions;
company databases.

The objective isn’t to manufacture mentions everywhere.

It’s to make sure that when someone investigates your company, accurate and useful evidence exists outside your own marketing department.

That’s particularly important for products where trust matters.

7. Keep Your Brand Information Consistent

Imagine your website says your company serves enterprise customers.

A directory says you serve small businesses.

LinkedIn describes you as a marketing platform.

A review site calls you an analytics platform.

An industry publication describes you as a CRM.

That’s confusing for humans—and potentially confusing for machines trying to determine what your company actually is.

Create a consistent core description of:

company name;
product name;
category;
primary audience;
major use cases;
important features;
geographic market;
integrations;
pricing model;
notable differentiators.

Then make sure important public profiles accurately reflect those facts.

This is not about repeating the same sentence everywhere.

It’s about eliminating contradictions.

8. If You Sell Physical Products, Product Data Matters Even More

For e-commerce businesses, there is an additional opportunity.

OpenAI says ChatGPT’s shopping experience can use merchant product data, publicly available product information and other retail sources. Product results can include product details, imagery, reviews and merchant links.

OpenAI also says merchants can provide product information through its commerce infrastructure, while Shopify merchants already have product data integrated into ChatGPT through Shopify Catalog.

That makes product-data quality extremely important.

Make sure your catalog contains accurate:

product names;
descriptions;
specifications;
prices;
availability;
variants;
images;
sizes;
materials;
compatibility information;
shipping information;
product identifiers.

If the product data is incomplete or outdated, you are making the recommendation system’s job harder.

9. Don’t Confuse AI Visibility With Traditional Rankings

There is a useful conceptual difference here.

Google Search has traditional search results.

ChatGPT recommendations are conversational.

A customer might ask:

“I need a lightweight laptop for video editing, but battery life matters more than gaming performance.”

That’s not a conventional keyword.

It’s a decision problem.

The AI has to interpret the requirements, identify relevant products, compare tradeoffs and produce an answer.

That is why optimizing only for traditional keyword rankings may not be enough.

The emerging field of Generative Engine Optimization (GEO) was formalized in academic research as a framework for improving visibility in generative-engine responses. The Princeton-led GEO research reported visibility gains of up to 40% in its benchmark, while also finding that results varied by domain. That is research evidence, not a guarantee of a particular business outcome.

10. Measure Recommendation Visibility Instead of Guessing

One screenshot of ChatGPT mentioning your company isn’t a reliable measurement system.

AI responses can change.

The same question can produce different answers depending on context, model updates, location, timing and available information.

Create a fixed testing framework.

For example:

Metric	What to measure
Mention rate	How often your brand appears
Recommendation rate	How often it is actually recommended
Position	Where it appears in the shortlist
Competitor presence	Which competitors appear
Citation presence	Whether your sources are referenced
Question coverage	How many target questions produce visibility
Conversion	Whether AI-referred users become leads/customers

Bing’s AI Performance reporting is already moving in this direction by showing cited pages, grounding queries, citation activity and trends across supported AI experiences. Bing also explicitly warns that citation counts don’t represent rankings, authority or traffic.

Google has also introduced dedicated generative-AI visibility reporting in Search Console for AI features such as AI Overviews and AI Mode.

The broader lesson is simple:

Measure what happened, not what you hope happened.

11. Avoid Fake Reviews and Artificial Mentions

This deserves a clear warning.

If someone tells you they can make ChatGPT recommend your product by creating hundreds of fake reviews, fake forum accounts or artificial testimonials, be extremely cautious.

The FTC’s rule on consumer reviews and testimonials prohibits fake or false consumer reviews and testimonials, including AI-generated fake reviews, and addresses businesses buying or disseminating reviews they knew or should have known were fake.

Beyond the regulatory issue, fake information creates another problem:

It makes your brand less trustworthy.

A better strategy is to create real evidence:

genuine customer reviews;
legitimate case studies;
independent coverage;
expert commentary;
original research;
transparent comparisons;
useful product documentation.

You want an AI system to have more reasons to trust your product, not more artificial reasons to mention it.

12. Think in Terms of an “Evidence Footprint”

A useful way to think about AI recommendations is this:

Your website is only one piece of the story.

Imagine a buyer asks:

“Which project management platform is best for a 50-person remote marketing agency?”

An AI system may need to understand:

what your product actually does;
who it serves;
how it compares with competitors;
what customers say about it;
whether reputable publications discuss it;
whether the product is current;
whether the pricing fits the buyer;
whether the product solves the specific use case.

The more consistently those pieces of information exist across trustworthy sources, the easier it becomes for an AI system to understand your position in the category.

This is the basic thinking behind what LinkinGrow calls an “Evidence Footprint” and “Recommendation Graph.” The company describes its approach as mapping the sources and evidence surrounding a buyer’s specific question rather than treating the website as the only optimization target.

That approach is particularly relevant when the objective isn’t simply getting cited, but getting recommended for a specific buying question.

13. What Should a Product Company Do First?

If I were starting an AI recommendation visibility program for a U.S. product company today, I would keep the first phase simple.

Step 1: Choose 10 important buyer questions

Don’t start with hundreds.

Choose the questions that could realistically generate revenue.

Step 2: Record the current answers

Ask the questions consistently and record:

which brands appear;
which brands are recommended first;
what sources are cited;
what product attributes are discussed;
what competitors are consistently present.

Step 3: Find the information gaps

Ask:

“Why is the competitor easier to recommend than us?”

Maybe the competitor has:

better third-party coverage;
more detailed product information;
stronger reviews;
clearer use cases;
more comparison content;
better documentation;
stronger brand recognition.

Step 4: Fix the fundamentals

Improve your website, product pages, structured information, internal linking and technical accessibility.

Step 5: Build genuine external evidence

Earn relevant coverage and create useful independent-facing material.

Step 6: Publish original content

Answer the actual questions buyers ask—not just the keywords marketers want to rank for.

Step 7: Re-test regularly

Use the same questions and track changes over time.

This turns AI visibility from a vague marketing idea into a measurable program.

What About LinkinGrow?

For companies that don’t want to manage this process entirely in-house, LinkinGrow is one example of a platform focused specifically on AI recommendation visibility.

LinkinGrow positions itself around AI Answer Engine Optimization, with an emphasis on getting brands named for specific buyer questions across systems such as ChatGPT, Google AI answers, Claude and Perplexity. Its stated approach includes mapping the evidence surrounding a buyer question, creating observation-based content and measuring whether a brand’s presence changes over repeated runs.

One aspect worth noting is its stated editorial standard: it says its content is true, bylined and disclosed, and explicitly rejects fake reviews, bots and bought engagement.

For a business evaluating any GEO or AEO provider, those principles are worth looking for regardless of which company you choose.

The important question isn’t:

“Can this agency get ChatGPT to say my name?”

Ask instead:

“Can they show me what buyer questions we’re targeting, why competitors are being recommended, what evidence is missing, what they are changing, and how the outcome is being measured?”

That is a much harder question—and a much more useful one.

A Simple 90-Day Plan

Days 1–30: Establish your baseline

Choose your top buyer questions.
Test ChatGPT and other relevant AI search experiences.
Record competitors and citations.
Audit your product pages.
Check crawlability and indexing.
Identify missing product information.
Review third-party brand coverage.

Days 31–60: Build the evidence

Improve core product pages.
Publish decision-focused content.
Create original research or useful comparisons.
Improve legitimate review coverage.
Update important business profiles.
Build relevant third-party relationships.
Strengthen internal linking.

Days 61–90: Measure and refine

Re-run the same buyer questions.
Compare recommendation frequency.
Compare competitor visibility.
Track cited sources.
Identify questions where you’re still absent.
Improve the weakest evidence areas.
Connect AI visibility with leads, branded searches and sales where possible.

Don’t expect every change to produce an immediate recommendation.

AI systems evolve, web sources change and answers are not static.

The objective is to build a stronger information ecosystem around your product over time.

The Real Goal Isn’t to “Hack” ChatGPT

The temptation is to search for a trick:

“How do I make ChatGPT recommend my product?”

But the more durable question is:

“How do I make my product genuinely easy for an AI system to understand, verify and confidently recommend when it fits the buyer’s needs?”

That changes the entire strategy.

You don’t need fake reviews.

You don’t need hundreds of low-quality articles.

You don’t need to manufacture mentions.

You need a product that is clearly described, a technically accessible website, useful first-hand content, accurate product information, legitimate third-party evidence and a measurement system that tells you whether your visibility is actually improving.

And if you’re selling physical products, make your product and merchant data as complete and current as possible, because ChatGPT’s shopping experience can use structured merchant/product information when selecting relevant products

The brands most likely to benefit from AI recommendations won’t necessarily be the brands that talk about themselves the most. They’ll be the brands that give AI systems the clearest, most credible reasons to include them.