Linen Waffle Throw
Cozy up in effortless style. Warmth and texture for any space. 50″ × 60″.
Agent-ready commerce
Shoppers ask an AI now. It answers from your product data, and most catalogs don’t say enough to be picked.
We find the products agents can’t reach, fix what’s missing, and keep it true as your catalog grows.
See which of your products an agent can reach today. Free, in under a minute.
For Shopify brands of any size, in any category.
A shopping conversation with an AI assistant. The shopper types: linen throw, nothing synthetic, under one hundred dollars. The assistant searches stores and reports back that three of them say what they are made of, listing a Linen Waffle Throw at eighty five dollars, a Stonewashed Linen Throw at ninety five, and a Heavy Linen Throw at seventy eight, each with its material and finish attached. The shopper picks the waffle throw and says order it. The assistant confirms, the parcel arrives with all three attributes matching what was promised, and the shopper leaves a five star review.
Your store, right now
That conversation happens whether or not your products are in it. Give me your store’s address and I’ll ask Shopify’s catalog the way an assistant would, then tell you what came back.
Free. No email, and nothing to confirm.
Which store should I look for?
Asking the global catalog
We read Shopify’s public catalog and your store’s public agent profile. How we use this.
The same product page, read twice.
What a shopper sees
Linen Waffle Throw
Cozy up in effortless style. Warmth and texture for any space. 50″ × 60″.
custom.care_instructions “Machine wash cold.” Buried. Only your theme renders it.
What an agent sees
from the title
from the title
from the description
moved where machines look
no evidence. Never guessed.
Your catalog today
With Datathreads
One product read two ways. Left, the shopper's view: an illustration of a waffle-weave throw, the title "Linen Waffle Throw", and a description that praises it without naming its fabric. Right, the machine's view: five attribute fields (material, finish, size, care and origin) all empty. Datathreads then fills four of them and shows where each came from: material and finish from the title, size from the description, and care from a metafield only the theme rendered. Origin stays empty, marked for a person, because nothing proves it.
Illustrative. Every fact carries its sentence. A person approves before anything publishes.
None of them look like a catalog problem when the bill arrives.
01
Fragrance-free moisturiser. Organic cotton tee. Solid oak, not veneer. A question cannot reach a product whose field is blank, and there is one answer, no second page.
02
Meta and Google build product sets out of attributes. With none, the optimiser has nothing to work with.
03
“Not as described” is the return code for a page that never described it. Akeneo found 59% of shoppers have returned something for exactly that reason.
The question
“a linen throw, nothing synthetic, under $100”
What the assistant has to check
Two of three unknown. Three results come back. None of them yours.
Three of three match. Linen Waffle Throw, $85. You are the answer.
Unknown is not a match.
Your product feed, 36 of 6,000
1 audience. Every product identical. The optimiser bids on a name and a price.
Four product sets, from a field that now exists.
The missing piece. You can advertise a product. You cannot advertise a quality, because your catalog never recorded one.
The shift is already underway. A catalog takes 30 days.
Start with fifty, freeMost brands can’t answer any of them. We answer all three, free.
We look up every product by its own id. Across five brands we checked, bundles and gift sets were missing from every one.
On one brand, 100% of catalogued products said nothing about who they were for, so a shopper asking for a gift saw none of them. Across every store we measured, roughly a third carry no colour an agent can filter on.
Your search terms, re-run weekly. Watch the number move as the gaps close.
Present, reachable, found. In that order, and each one a number you can check rather than a claim we make.
Your domain is all we need. One minute, no email.
One team owns the whole record, because discovery runs on all of it, not just the parts that are easy to fill.
What’s in the index, what an agent can reach, and whether your terms bring you back. Re-run weekly.
Every fact carries the sentence it came from. Nothing is guessed.
A supplier sheet in, a description in your voice out. Never born blank.
Background, crop, lighting. Checked against your guidelines.
One record behind both, so they stop disagreeing.
Attributes reach Meta and Google, so campaigns bid on a quality, not a name.
Before and after, by material, finish and vendor. In figures you already collect.
Everything lands in a single approval queue. Fifteen minutes a week.
Four steps, then it repeats. Every number below came from a real store we ran this on.
01
Your terms, against Shopify’s catalog. On one brand 100% of listed products could not be narrowed to at all, and nobody had ever told them.
02
Colour, size, material, who it is for, each with the sentence it came from. You approve twenty, we do the rest.
03
Into Shopify’s structured fields, not your theme. One click undoes any batch for thirty days.
04
The same searches, every week. Found or not, position, and which products an agent can now reach that it could not before.
Fifteen minutes a week. One queue, one owner.
Bundles and gift sets go missing on every store we have measured. Out-of-stock products are almost never in the index at all. These are the things nobody tells a merchant, and they are the first things we fix.
Who acts free, generic
Your agent, Meta, Google, ChatGPT. They do the work. They don’t know anything, and they don’t remember.
What’s true knows, learns
Datathreads. What every product is, the sentence that proves it, and the vocabulary your team taught it.
Where data lives stores
Shopify, a PIM if you have one, the supplier’s spreadsheet. They hold what someone typed. They don’t know if it’s true.
“So it’s a PIM?” A PIM is where you put product data. We’re what makes it true, and what it teaches you.
Every tool tells you which products sold. Because your catalog now says what everything is made of, we show you which qualities stay after returns, and which quietly come back.
| Attribute | Sells vs. baseline | Returned | Kept, per 1,000 visits |
|---|---|---|---|
| Natural fiber | 2.3× | 14% | +$212 |
| Matte finish | 0.9× | 11% | +$88 |
| High gloss | 1.4× | 27% | +$14 |
| Stonewashed | 1.12× | 31% | −$41 |
The attribute that sells above average is the one you keep least of. A buying decision, a merchandising decision and an ad-budget decision in one line, invisible until the material is on the order.
Illustrative figures, invented for the example. Your table would come from your own orders and returns.
Four things we hold to, in writing, from the first batch onward.
Easy to try, easy to leave. The work we do is yours to keep either way.
Try it free, or start today. Most people look first.
Fifty products done and a report on what agents miss. Yours to keep.
Free
Up to 1,000 SKUs. The first 30 days are free; cancel any time.
$389 a month
Up to 5,000 SKUs, both channels kept in step.
$1,179 a month+ $5,000 one-time
Above 5,000 SKUs. We scope it with you.
$2,000 a month+ $10,000 one-time
A measurably better showing on the surfaces agents search, in 30 days.
About 0.6% of revenue at any size. A PIM costs $199 to $999 a month and still can’t tell you whether any of it is true.
A SKU is a variant. Monthly, cancel anytime; nothing is charged in Startup’s first 30 days. Cards are handled by Stripe; we never see them.