Being Found by AI: How AI Shopping Assistants Choose Which Furniture to Recommend, and What Your Catalog Has to Hold
How AI shopping assistants use product facts, why furniture data is often hard to find, and how to fix it from the catalog. The complete guide, linking all 20 articles.
A shopper types a brief into an AI assistant: "a sofa under 80 inches wide, pet-friendly fabric, ships in under four weeks, around $1,500." The assistant may name a handful of products, with a reason for each. The shopper can ask for more, but the products in that first answer have already made the shortlist.
Several things go into which products it names: the shopper's context, price, availability, reviews, third-party coverage, imagery and the product facts it could find. Most of that you can't see. The product facts you can, and for furniture they're often the weak link: the width as text, the fabric composition as a value, the shipping window as a sentence, attached to the right variant. A sofa whose width is in a diagram, whose fabric is "performance", whose shipping needs a ZIP code and whose default variant is the wrong size gives the assistant less to work with, and an assistant can only say what it can find.
This guide is about that gap and how to close it. Twenty articles follow, in four parts: seeing the problem, understanding why it persists, choosing how to fix it, and doing it in Furniture Connect.
Part one: see it for yourself
Don't take this on trust. Ask an AI assistant for a sofa gives a ten-minute test: five shopper briefs, several assistants, a note of which brands come back and, for your own products, what the assistant could and couldn't find. The "couldn't find" column is the whole subject in miniature.
The shopper this matters for is the one who narrows the choice inside the assistant and only visits the pages it sends them to.
The shopper who never visits your site follows that journey and what it sees less of: your photography, your brand story and your reviews widget, all living on a page the shopper may never reach.
Your page now has two readers, the person and the machine, and they want almost the same thing. Your product page has two readers now shows where they agree, and the one place they conflict: design that hides facts in images and tabs.
One of the most common weak spots is dimensions as a diagram. Dimensions in the image explains why the habit exists, who it hurts beyond assistants (phones, screen readers, search, your own feeds), and how to keep the diagram and add the data.
What an AI assistant can read easily on a furniture product page, and what's harder walks the page element by element, title to structured data, with the fix for each.
Part two: why it persists
When the assistant named a competitor, two product pages won't tell you why, but they will show you what yours left out. What the AI found on your competitor's page that it didn't find on yours gives a side-by-side method: list the questions in the brief, score both pages on whether each answer is present as text, and come away with gaps worth fixing rather than a cause.
The facts are rarely unknown. Your buyers, customer service team and store staff know whether the legs come off and how deep the seat really is; the product record doesn't. The facts you know that aren't written anywhere an AI can read is about finding that knowledge and giving it a slot.
Nor are the facts usually missing from the page. They're in a PDF, a spec image, a tab that loads on click, a variant dropdown, a shipping calculator. Where furniture product data hides tours each hiding place and why every one is a reasonable decision that adds up to a page that tells a machine much less than it knows.
What exactly are shoppers asking? The questions shoppers ask AI assistants about sofas is a working checklist by fit, materials, durability, comfort, delivery and price, to score any page against. It's a checklist, not survey data; your customer service team's additions will be the ones that matter most.
And the reason fixing the best-seller's page doesn't help: why fixing one product page doesn't fix the range. A page fix is a copy that drifts; a catalog fix is a source that every page, feed and FAQ can read once it's mapped to them. The test for any proposed fix is whether the next product you add gets it for free.
Part three: choose the approach
GEO is a new acronym and much of the advice under it is SEO renamed. SEO, GEO and product data separates what carries over, what's actually new (an answer instead of a list, a whole brief at once, facts quoted so errors travel), and what sits underneath both: whether the facts exist as data at all. It also says where to spend effort, in order.
The obvious shortcut, pasting pages into a general AI and asking for better ones, is worth trying once because it teaches the lesson. Can you just ask an AI to rewrite your product pages? shows what a rewrite fixes (structure, consistency) and can't (facts it wasn't given, which it may invent), and what tool shape follows: writing from the record, refusing to fill gaps, running across the range.
From field to page follows one fact, seat depth, through six steps from supplier spec sheet to assistant's answer, and shows where the chain breaks at each handoff between teams.
Which brings up ownership. Who owns being found by AI: SEO can't fill attributes it doesn't know, ecommerce can't change families it doesn't control, merchandising forwards PDFs. The missing role is the owner of the product record, and once that's settled the other teams know what they supply and publish.
Before paying anyone, measure. How to test whether your range is readable by AI is a repeatable audit using a manageable product sample, shopper and named questions, several assistants and a scoring sheet. It also says what the test can't tell you and when an automated version earns its place.
Part four: doing it in Furniture Connect
Furniture Connect's AI visibility check runs the audit from your catalog. You set up your brand, website, competitors and regions, add shopper questions phrased the way a shopper would ask them, and track the products you care about. A check asks the tracked questions across ChatGPT, Claude, Gemini and Perplexity in your regions.
The report covers observed recommendations, cited sites, sales channels, tracked products, catalog gaps and individual questions. It shows a credit estimate before the run. One check is a sample rather than a precise measure, and the gaps are work worth doing rather than proof of why a product was left out. How to run an AI visibility check on your range walks through setup.
Reading the AI visibility report goes section by section and ends with the three lists to leave with: attributes missing across many products, wrong facts in named answers, competitor pages to study.
The workflow routes data gaps toward Autofill and imagery gaps toward Studio. Closing the gaps is the order of work: create the attributes and add them to the relevant family, fill from source fields you already hold with a sample run first [1], supply by hand the facts nobody has recorded, write copy from the filled attributes, produce imagery, and keep a person reviewing throughout.
Filled attributes help only when the store shows them. Getting the fixed facts onto your store covers the Shopify connection, which carries titles, descriptions, SEO fields, images with alt text, variants and supported furniture details between the catalog and your store [2], and the checks on the live page: facts in the source as text, a theme that renders them, per-variant values visible without a click.
Your first two weeks puts it in order: baseline on ten products, sort the gaps, define and fill range-wide attributes, push to the store, re-check, and write up the before and after.
What this asks of a furniture retailer
Not a marketing campaign. A product data habit. For each category, create the attributes that answer the questions shoppers ask and add them to the relevant family, then fill them across the range. Map templates, feeds and structured data to read them, and check each destination after publication. Give the record a named owner. Feed unanswered customer questions back into it. Then run the same check again, by hand or by tool, to see what changed.
It is the same work that makes marketplace listings complete, comparison tools useful and the website better for the person reading it. AI assistants are the newest reader of your product data and the least forgiving, which makes them a good reason to finish a job that was always worth finishing.
What it doesn't promise
A complete record doesn't make an assistant recommend you. Assistants sample, differ from one another and change; a published fact can be found and an unpublished one can't, and finding it is only part of what goes into an answer. Nothing here guarantees a position. What it does is remove a reason you might not have been considered, product by product, fact by fact, and let you see the difference in the questions you now appear for.
Where to start
If you're not sure the problem is real for your range, run the ten-minute test in ask an AI assistant for a sofa. If you're sure and want to measure it, follow how to test whether your range is readable by AI. If you're ready to work from your catalog, start with how to run an AI visibility check on your range.
