AI Visibility Tools for Ecommerce: What They Track and How to Choose One

AI Visibility Tools for Ecommerce: What They Track and How to Choose One

A shopper doesn’t have to start with a search box when they want to find a product. They can ask ChatGPT, I need running shoes for wide feet. Tell me more about them; ask Gemini, Can you compare moisturizers for sensitive skin? or use a different AI assistant to generate a list of items that meet their specific need.

It’s a new visibility problem for ecommerce teams. A product can do well on a traditional search and still be invisible on an AI-generated list because the AI can’t locate or understand the product and its attributes. It can’t verify certain facts.

The amount is getting less difficult to quantify. AI-referred traffic to US retailer websites was 693.4% higher this year than in 2024, according to Adobe’s 2025 holiday shopping figures. Adobe additionally reports that as of July of this year, 38% of US customers it has surveyed have used generative AI within their personal retail shopping experiences. Just over half (52%) of US customers likewise say they expect to do the same this year.

(Source: https://news.adobe.com/news/2026/01/adobe-holiday-shopping-season )

For direct-to-consumer brands, then, AI visibility is more than just a matter of showing up. It’s the extent to which they can pack individual SKUs with the kinds of accurate, structured information that will enable AI systems to actually process them as consumers ask more pointed and granular queries.

How do AI assistants decide which products to recommend?

This is because AI assistants don’t have a singular public rank formula for product recommendations. Their responses can vary depending on the model and the prompt, the web sources available, the shopping services, and the product knowledge, as well as the signals used.

Product data does play a role here, but it’s not the whole picture. Structured data is information about a page that can be represented in a way that allows machines to understand. Examples of structured data on the web would be Product markup and JSON-LD, which describe the item being offered for sale with standardized properties. In cases like this, Google has said that using Product markup can help it surface the following bits of information to its users: price, availability, reviews, shipping information, and product variants.

The same goes when we’re talking about e-commerce content for AI. An e-commerce product page that is rich with specifics about the material, size and size options, ingredients, compatible pairings, configurations, components, and so forth, provides significantly more context for AI analysis than a short blurb centered on grandiloquent product marketing.

And FAQs fill out another content hole. Will this jacket protect against pouring rain? Will this serum aggravate sensitive skin? Will this backpack fit a 15” laptop? Putting the answers to these kinds of questions on a product page makes the information even more available to AI recommendations based on these specific queries.

Reviews provide yet another layer. They supply details on fit, durability, comfort, quality, and disclosures of common problems that a manufacturer’s description may overlook. Third-party references help an AI system obtain a clear picture, especially when the brand’s website link is unavailable.

Sentiment is a factor too, but it should be taken with a grain of salt. AI visibility systems have the ability to see how a brand or its products are being described across reviews. But sentiment scores, while available for consumers, are effectively opaque and should be treated as abstractions that blend a range of subjective opinions with the AI’s ability to recognize them. Sentiment scores are a proxy for how typical shoppers likely perceive a specific product. But they are not an actual rating of performance, reliability, or usefulness.

Why do good products get left out of AI answers?

But a great product can fall through the cracks if there’s not enough or consistent information online to answer a specific shopping question.

A company may have a trail shoe for wide feet, for example. The product page mentions it’s lightweight, durable, and good for hiking. The page doesn’t explicitly say if the shoe is (or isn’t) available in a wide fit. So, when someone uses an AI assistant to find trail shoes for wide feet, the absence of that key attribute can mean the product won’t meet the requirement.

The same problem occurs regardless of the category. A cosmetic product may have information on its components, while it provides no answers about whether the product is fit for a specific skin type. A food product may explain its taste, but there is no mention of whether the product includes allergens or is safe for sensitive groups to consume. A laptop bag may have product dimension information given but fail to mention what laptop models the product will fit.

Incomplete structured data can create yet another kind of hole. If key product facts, particularly those we would trust as the data most relevant to us, are present in natural language but are spotty or inconsistent in machine-readable data, the data becomes harder to understand across different systems.

This means AI visibility is partly an information-quality problem. The product itself may be perfectly suitable, but the digital information surrounding it may give an AI system too little evidence to make that connection.

How do you measure your brand’s AI visibility?

Start by tracking a library of real shopping queries rather than asking a few arbitrary questions. For example, combine generic searches like “best running shoes” with very specific searches like “trail shoes for wide feet” or “fragrance-free moisturizer for sensitive skin.”

Ask the questions, on a regular basis, of the AI platforms that your customers are asking. Track whether your brand surfaced, what products were mentioned, where in the answer they showed up, what other brands were mentioned, and what source the AI used or pointed the response to.

This defines the difference between brand and product visibility. A retailer may seem visible in AI to determine most of its mentions result from a single popular product mentioned across numerous queries. Meanwhile, dozens of other SKUs may have 0 visibility at all.

Voice share is another important metric. Rather than simply counting whether or not a brand is mentioned, teams can now find out more specifically how often a brand is mentioned relative to competing brands on the same set of prompts. In some cases, you can filter by position. You can also identify sentiment, changes over time, and cited sources.  Peec AI, for example, counts a brand as visible if the entity is mentioned in an AI response list of tracked AI output in the platform. It can also pinpoint the position, sentiment, share of voice, competing brands, and cited sources. 

The next frontier is tying visibility to business outcomes. Monitor AI-driven sessions in your analytics and investigate product-level traffic, conversions, and revenue when you have data to support that kind of insight. Adobe’s 2025 holiday analysis in retail found AI-referred traffic was converting 31% above other channels. That’s the kind of proof you need to validate AI visibility as more than an awareness credit.

What do AI visibility tools track?

The market includes several types of AI visibility software, and they do not all measure the same things.

Profound: Focuses on AI search visibility for brands and retailers, primarily tracking product visibility and placement within AI channels for shopping. It also traditionally includes shopper queries and competitor visibility as a retail solution.

Peec AI: Positioned as an AI search analytics tech provider. The company’s SaaS platform typically focuses on visibility, position, sentiment, share of voice, competitors, queries, and sources that AI indicates, and generally lacks granular product visibility for ecommerce.

OtterlyAI: Focuses on tracking brand and content visibility across AI search channels, including ChatGPT, Google AI Overviews, Google AI Mode, Perplexity, Gemini, Microsoft Copilot, and Claude. Tools are generally used to analyze mentions, sources, competitor analysis, and content analysis, meant to identify gaps.

Glara: An AI visibility platform for ecommerce brands on Shopify and Centra, built for fashion, beauty, FMCG, and wellness. It tracks how products appear in ChatGPT, Gemini, Claude, Perplexity, and Grok, shows which product attributes AI credits to competitors, generates fixes to descriptions, structured data, and FAQs that are pushed directly to the store, and connects visibility to traffic and revenue.

The main distinction for any ecommerce team is how high up the chain the tool actually operates. Many tools focus primarily on the brand and citation elements of Google’s picture; however, there are also tools that go deeper, serving the ecommerce agenda in terms of products and attributes, catalog data, and the sort of levers a team actually needs to pull in order to set a product live.

What should you fix first?

An AI visibility audit should lead to something, not another dashboard to check.

Start with product specs. Make sure that product names, descriptions, pricing, availability, colors, reviews, and other relevant pair attributes are complete and correct.

After that, check product content from the shopper’s perspective. Does this page answer all the questions a shopper would have before purchase? For apparel, the things you should think about are fit, fabric, measurements, use case, etc. For beauty, ingredients, skin type, how to use, etc. The latter point would matter more.

Add FAQ Content where there are real questions being asked currently and not answered. All answers should be as useful and to the point, not generic SEO copy filling up the page.

Next, review the consistency of the attributes between the channels. If you have an attribute showing a product on one channel as waterproof, but that same product listed in a feed is water-resistant, or maybe you have a size of the product on the product page and in the feed it’s something else, investigate why.

On platforms such as Shopify and Centra, product data such as attributes, metafields, and structured data is where many of these fixes happen, so the audit should reach into the ecommerce platform rather than stopping at the visible page.

For larger catalogs, turning this into a more automated, more operational way of working will make it easier to get the results more accessible and to maintain it. Have a platform like Glara turn the identified gaps (at product level) into change requests and push the change once approved into the store, making sure this can be done with a minimum of manual work at the product level.

What should an ecommerce AI visibility audit look like?

The key to controlling AI visibility is to set up an ongoing monitoring process as opposed to making another SEO project.

Instead of doing that, study out from the shopping prompts resonate well with your customers, how to measure brand and product visibility in AI channels relevant to those promotional prompts, what attributes and sources generate those responses, and how to match that to visits and revenue wherever possible.

The goal is the combination of the two. Appearing in an AI-generated response with a product specification that’s wrong, a price that’s out of date, or an attribute that’s simply untrue is a completely different matter from failing to appear at all.

For the operator of an ecommerce site, the point of origin is the product catalog itself. Having complete, well-maintained structured data, having useful questions and answers about what a shopper might want to know, making sure that attribute names and values have been cleaned up: all these things make the AI system’s life easier. But with all this done, you ideally need a visual way to see where the gaps are and be able to know, in a practical manner, whether the changes you are making are making a real difference.