Ashwin Menon ran ten shopping queries against one supplements catalog, twice. Its own store search placed it top twenty every time. The shared catalog AI assistants read, not once
The CEO of a supplements brand opened with the question Ashwin Menon hears on most first calls: was this about optimizing branded searches versus generic ones. Menon asked him to pull his own attribution data, live on the call. He found roughly twelve orders that month from ChatGPT, plus a handful from Claude and Perplexity, arriving from a channel nobody in his company had ever worked. “He’d filed it under marketing when it was a distribution channel, and you don’t optimise a distribution channel, you staff it,” Menon says.
Ashwin Menon is co-founder of Nile in San Francisco, after four years inside Amazon’s third-party marketplace as a seller advocate for FBA brands.
Ten Shopping Queries, Run Twice
Three complaints reached Ashwin from sellers often enough to constitute a finding, and two of them were about cost. Acquisition was getting more expensive on every channel a brand already had, with every alternative running as another auction. Products that were genuinely better were losing to competitors who were better at buying clicks. “Sellers weren’t asking for a better auction,” Ashwin says. “They were asking for a channel.”
They were asking for a channel.
The third was not about cost at all. It was about description: what gets said about a brand when a shopper asks a question and the brand is not in the room. “A seller can control their listing. They cannot control the summary, the comparison, the recommendation,” Ashwin says. “On a marketplace you at least know the rules. On an AI surface, the answer is generated fresh every time and nobody has told brands they have any say in it at all.”
How large that gap runs is measurable, and at the start of August, Ashwin’s team measured it on one supplements catalog in a read-only audit. They wrote ten non-brand shopping queries, the kind a stranger types when they do not know who makes the thing they want, and froze the list before running anything. In the shared cross-store catalog that assistants pull from, the brand placed in the top twenty on none of the ten, at an average effective rank of around forty-five out of fifty. Run against the brand’s own on-site search the same day, the identical catalog placed in the top twenty on all ten, at an average position of two.
Why a Product Page Reads Badly to a Machine
“A product page is a persuasion document written for a human’s eyes: a hero image, a headline that makes you feel something, benefits you infer from a photograph,” Menon says. “An assistant retrieving against structured attributes gets almost none of that.”
A product page is a persuasion document written for a human’s eyes: a hero image, a headline that makes you feel something, benefits you infer from a photograph.
Adobe measured the consequence across the sector. Its analysis of US retail sites in the first quarter of 2026 scored product detail pages at around sixty-six percent for machine readability, with roughly a third of the content on them unreadable to AI crawlers. It was the weakest page type Adobe assessed: homepages scored higher, so did category pages, and so did the returns and help pages nobody optimizes. “The page a shopping query has to resolve to is the page least readable to the thing resolving it,” Ashwin says.
The gap survives partly because most brands have no instrument pointed at it. “Most brands cannot find their AI-driven sessions in their own analytics. It isn’t a channel; it lands in referral or direct or ‘other,’” Ashwin says. What testing does happen tends to be the easiest test available and the least informative, a brand typing its own name into ChatGPT, reading a flattering answer, and stopping there. “Two completely different jobs are going on: getting picked when a shopper names you, and getting into the answer when they don’t. Almost nobody checks the second,” he says.
One Diagnostic, Three Categories, Three Different Answers
Run the same measurement in another category and the diagnostic holds while the answer changes. “This does not work uniformly,” Ashwin says.
A marine-supplies merchant went looking properly in his own analytics and found ChatGPT had been sending him between five and fourteen sessions a day for weeks, before he had installed anything at all. The channel was already running. The only thing missing was anyone counting it.
A snack brand came out of the same audit in good shape. Measured before any work was done on it, its catalog placed in the top twenty on five of twelve need-based queries, which is not a brand with a legibility problem.
A confectionery brand launched and produced sixty-seven clicks, which is close to nothing. Its founder emailed and asked whether he was missing something. “He wasn’t; his category hadn’t produced the volume others had and our setup guidance for it wasn’t good enough,” Menon says.
Four merchants, one instrument, four different instructions. What did not vary was the diagnostic: ten queries, run twice, on a list frozen before anyone looked, and no decision about budget or headcount taken until the numbers came back. What varied was the category, and what each set of numbers told the merchant to do next. Two of the four were told to do very little, and those are the cases that argue hardest for running the test at all. A brand already placing on five of twelve queries does not need a project. A category that returned sixty-seven clicks has told its merchant something no forecast would have.
What E-Commerce Operators Can Steal Before Deciding to Wait
An operator weighing this usually asks whether the channel is big enough yet. Ashwin says that is the wrong question. “The question is ‘how long does this take to work,’ and the answer is longer than they think, which is exactly why waiting is the expensive choice.”
So start with the measurement, which is portable and costs nothing. Write ten shopping queries a stranger would type to find a product like the one being sold, with no brand name anywhere in them. Freeze the list before running it, so the queries cannot drift toward the flattering ones. Run them against the catalog assistants read and against the brand’s own on-site search, on the same day. The second run is the control, and it is what separates a product nobody wants from a product nobody can find.
Then go find the sessions. They are filed under referral, direct or the bucket labeled other rather than under a channel name, which is the whole reason nobody has counted them. That search takes about as long as the query test and tends to be the more startling of the two.
Resist the instinct to tidy the catalog first. Every merchant’s reflex is to kill the bundles and the variants and reduce a listing to one clean product, and an engineer would agree that duplicates are noise. “Each variant maps to a different buyer situation, and the agent needs the three-pack to answer ‘best value for a family of four,’” Ashwin says. He spent years on the other side of that pattern, looking at why a redundant, seventeen-variant catalog outsold a tidy one.
Regulated Categories Turn on What a Brand Forbids
Whatever tooling a brand uses, the controls worth insisting on are the negative ones. Claims, positioning, objection handling and the shopping questions a brand wants to be found for all sit on the same list. In supplements and health it is the prohibitions that decide it: the claims that must never be made, the competitors that must never be named. “A brand in a regulated category will tell you that the ability to say ‘never say this’ is worth more than anything we say on their behalf,” Menon says.
I’m telling them the measurement is free, it takes five minutes, and ‘wait and see’ is a decision they’re currently making without having looked.
Give it a calendar before giving it a verdict. Menon tells brands not to judge performance for two to three weeks, because the system spends that period learning a catalog and a category. “If it were instant, waiting a year would cost you nothing. Because it’s cumulative, a year of waiting is a year of not-learning that you can’t buy back later,” he says. The learning curve is what puts a price on deferral, and it is the part of the argument that holds whether or not the channel grows on anyone’s schedule.
“So I’m not telling operators the sky is falling,” Menon says. “I’m telling them the measurement is free, it takes five minutes, and ‘wait and see’ is a decision they’re currently making without having looked.”
Who Is Ashwin Menon?
Ashwin Menon is the co-founder of Nile, an agentic commerce company based in San Francisco, and spent four years inside Amazon’s third-party marketplace, closing his tenure as Voice of Seller for worldwide FBA, the marketplace’s internal seller advocacy function.
“I was the seller advocate for Amazon FBA brands,” Ashwin says. He ran programmatic updates to improve the product experience and cleared business-critical escalations when the standard channels fell short, work that put him across FBA’s product and engineering organizations and in regular contact with the founders and chief executives of the largest companies selling on the platform. He holds a master’s degree in analytics and business from the Isenberg School of Management at UMass Amherst.
Ashwin co-founded Nile in July 2026. The company installs on all e-commerce stores, downloads a merchant’s catalog and builds a second, dynamic, machine-readable version of it with every product mapped to the buyer situations it answers. That version is visible nowhere a human goes. It exists for assistants to read, and it points back to the merchant’s own checkout, with nothing on the storefront changed. Nile is priced as a share of the sales it can attribute, between two and three percent, with no subscription, listing fee or monthly minimum. Ashwin named the company himself, after the river: the longest channel in the world, and a wager that commerce conducted through machines ends up larger than the commerce it grew out of.
Four years of watching a channel move underneath the brands selling on it left Ashwin with one conviction about the next one. It moves before anyone in the business is looking for it, and the first cost of finding out is a list of ten questions.
Ashwin Menon is the co-founder of Nile, an agentic commerce company based in San Francisco, and spent four years inside Amazon’s third-party marketplace, closing his tenure as Voice of Seller for worldwide FBA, the marketplace’s internal seller advocacy function.
