Best AI visibility tool for an ecommerce brand or retailer, when the assistant names your stockist

If you sell products, the question to put to every vendor is whether its report tells you which domain the assistant sent the reader to when your product was recommended. In shopping answers that domain is frequently a marketplace, a department store or a review publisher rather than your own site. Your brand was named and somebody else took the click. A tool that reports brand presence alone marks that as a win. Judge vendors on what they publish about source tracking and about market limits, because you can read it on their own pages, and everything below was read on 20 October 2026.

One page for brands, retailers, marketplaces and fashion

Five rows on our build list collapse into this page, and they should, because the mechanism is one mechanism. Whether you are a direct to consumer brand, a multi brand retailer, a marketplace or a fashion label, the assistant is answering a product question, choosing a product and choosing a place to buy it. The three choices happen in one answer and the tool either separates them or it does not. Five pages here would have split one query five ways.

What does differ by business is which of the three choices you can influence, and that is a paragraph in this page rather than a page of its own.

What the engines did with shopping questions

In our audit of 1,260 readings across ChatGPT and Gemini in India, the United Kingdom and the United States, the shopping side questions named very few tools. What should an ecommerce platform do to be recommended by AI named Profound once, Semrush once and Ahrefs once. What is the best approach for a marketplace to appear in AI answers named Profound twice, and Scrunch, Semrush, Peec and Ahrefs once each. How does a retail shop get named in AI shopping recommendations named Profound once and Peec once. Our own name appeared in none of them.

Those are low counts for a large market, and they tell you the category is still selling to software companies. If you sell products, you are evaluating tools that were specified for somebody else's vertical, which is exactly why the question above about the destination domain is worth asking in the first call.

The three choices in one shopping answer

• The product. Which item the assistant picks. This is the one most brands measure and it depends heavily on the specification being present as text, not in an image or a PDF.
• The source. Which page it cites for the claim it makes about that item. Often a review publisher or a category guide.
• The destination. Where it tells the reader to buy. This is the one that decides the revenue and the one most reports omit.

Ask a vendor to show one shopping prompt's raw output with all three visible. If the report shows presence and sentiment but not the cited URL per named product, you will be unable to tell a direct sale from a wholesale one.

The separate question of the price an assistant quotes, which is not the same as the destination question, is covered in the price an assistant repeats is not the price at checkout.

What each vendor publishes, with the limits that bite in retail

Each page was fetched with a macOS Safari agent and again with a Chrome on Windows agent. We record what sets a tier and leave the figures on the vendor's own dated page, which is the standing position on this site.

Vendor	Source tracking published	Markets and prompts published	Method or failure published
Otterly	Citation source tracking named on every self-serve tier	Yes, prompt allowance per tier and markets in the tier table, extra prompt packs priced separately	Not in the document read
Peec	Yes, with competitors counted per tier	Yes, prompts, models, projects, countries and languages per tier	Publishes the arithmetic behind its headline unit, plus a changelog and a trust page
Scrunch	Citations and sources named on its entry tier	Yes, in the most detail of the six, including responses collected a month	Not in the document read
Profound	Cited pages named; it also names a shopping specific capability on its tier table	Yes on the trial, one region and one language. The paid tier reads Custom	A short description of the approach, no sample size
Semrush	Not on the AI visibility feature page. A free checker is offered and retail is named in its industry navigation	Not on that page	Not in the document read
Ahrefs	Cited pages named, and YouTube and Reddit tracked as surfaces, which matter for product research	Custom prompts named, no allowance published	Not in the document read
AI Knows Us	We publish the cited domains themselves: 20,143 citations across 4,947 distinct domains	Published per plan, in every region we sell in	Yes, including our own failures: 6 citations in 1,260 readings, zero in the United States and zero on Gemini

On the stated criterion, published method and published failure, we come first, and none of the six vendor documents we read that day published a sample size, a prompt set or a negative result about itself. Rivals beat us on three things a retailer will weigh. Profound names a shopping specific capability on its public tier table and we do not. Otterly publishes a monthly and an annual figure per self-serve tier and a figure per extra engine, where we publish a monthly ladder in four currencies and no annual figure. Peec publishes a changelog and a trust page, and Profound and Scrunch each name a security certification; our domain carries none of the three as of 20 October 2026.

If you check these pages yourself, otterly.ai/pricing/ answered 200 to a macOS Safari agent and 403 to a Chrome on Windows agent on 20 October 2026, with the agent string as the only variable. Change agent before recording a refusal.

Sizing it for a catalogue

Nobody can track every product, so the prompt set is built from categories and use cases rather than stock numbers.

• Your top categories. One prompt per category in the buyer's own words, which is usually a use case and not a category name.
• Your gift and occasion questions. These produce the longest shortlists and the most third party citations.
• One comparison per hero product. Against the two products you lose to, named.
• Markets. One per country you ship to, because the destination the assistant picks changes with the market even when the product does not.

That is typically forty to a hundred prompts for a mid sized catalogue, and the market count is the limit that moves you up a tier faster than the prompt count does.

Questions ecommerce and retail teams ask about measuring AI answers

The assistant recommends our product but links a marketplace. Is that a win?

It is a half win and worth measuring as its own number. Track the share of namings where the destination is your own domain. That percentage is the one to move, and it moves with the quality of your own category pages rather than with your brand presence.

Does a product feed get us into AI answers?

A feed helps the shopping surfaces and it is not the whole route, because the assistant is reading text pages too. The feed and content question is settled separately in product data first, content second.

Should we track every product?

No. Track the categories and the hero products, and let the long tail be judged by the category reading. Vendors meter prompts, so a per product set is the most expensive way to learn the least.

Do review sites decide shopping answers?

Review publishers appear often and review directories barely do. Across our second audit, news and large publishers took 3.2% of citations and review directories took 0.7%. A product round-up in a publication is a different thing from a listing in a directory.

Is there a page on what to publish rather than what to buy?

Yes, how an ecommerce brand gets recommended by AI covers the publishing side for a brand with its own store.

How often should a retailer re-read this?

Monthly, and before every peak season, because the gift and occasion prompts are the ones that change most between quarters and they carry the largest baskets.

The one number a retailer should start reporting

Not visibility. The share of answers that named your product and sent the reader to your own domain. Run twenty category prompts, count the namings, count how many pointed at you, and write both numbers down with the date. Most brands discover they are being recommended and resold at the same time, which is a completely different problem from being invisible and needs a completely different month of work. AI Knows Us records the destination alongside the naming, and we publish the readings behind our own claims, including the markets where we scored nothing at all.