AI DiscoveryShoppers are asking AI what to buy. Make your products worth quoting.
AI assistants recommend the products whose data answers the shopper’s question. Renable writes that data — product highlights, use-case tags, structured product details — from your own catalog, and ships it in the feed you already export.
Trail shoe blue 42–47 — $89
Running shoe in blue. Free shipping on orders over $50.
Answer-ready- Waterproof membrane keeps feet dry down to freezing
- Lugs that grip wet rock and mud
Good for winter running?
Yes — dry to freezing, with grip for wet trails. Sizing runs half a size small.
The shift, as of mid-2026
Search is turning into an answer — and the answer names products
People increasingly ask an assistant to recommend a product instead of scrolling ten blue links. The assistant reads structured product data and names the ones that fit. These are reported figures about the outside world, as of mid-2026 — not Renable results.
Tens of billions
Product listings in Google’s Shopping Graph — the data behind AI Overviews, AI Mode, and Gemini shopping — refreshed continuously.
Answers, then your store
ChatGPT assembles shopping answers from merchant feeds and public product pages, then links the shopper out to the store — so your product data is what it has to work with.
One catalog
Microsoft Copilot reads Microsoft’s product catalog, and Microsoft Merchant Center accepts Google-formatted feeds — so one clean catalog reaches more AI surfaces.
The thing that decides whether an assistant can recommend you isn’t a new channel — it’s whether your product data answers the question. That part you control.
How AI shopping answers choose products
An assistant doesn’t match keywords — it reads product data and recommends what answers the question. A feed with a title, a price, and a thin description has nothing to quote, so it never makes the shortlist.
A shopper asks
“a waterproof trail shoe for winter?”
The assistant reads product data
highlights, Q&A, use-case tags, specs
It recommends what answers
the product whose data fits the question
“What’s a good waterproof trail shoe for winter?”
For winter trails, look for a waterproof membrane and deep lugs. One that fits: the Trail Runner 300 — its membrane keeps feet dry down to freezing and the lugs grip wet rock. Reviewers note it runs half a size small.
Trail Runner 300
Waterproof · wet-grip lugs · wide fit
“Still dry inside after a river crossing.” — Sofia
Illustration of how an assistant assembles an answer from product data. Renable can’t put you in the answer — no one can — but a catalog that answers the question is the one thing that makes it possible.
The content AI assistants actually quote
Renable’s AI-discovery pass fills the missing layer, product by product — the answer-shaped fields an assistant can lift straight into a recommendation. Most of it is written from your own data; the answers only you know, you write once and Renable carries.
Product highlights
Use-case tags
Structured product details
Buyer-intent Q&A
Generated content is written from your product data and fact-checked against it, and it lands in the same review pipeline as every other piece of content in Renable — approve a piece and it’s locked, so later runs never overwrite what you signed off.
The free AI-readiness audit
Find out where your catalog stands — in minutes
Connect a feed and ask Aimée. She reads your actual products and reports two kinds of gap: the content assistants quote that your catalog is missing, and the fields each AI channel expects that your feed doesn’t fill yet. A diagnosis, not a vanity score.
Aimée
Aimée, how AI-ready is my catalog?
I read all 214 products. 3 of your top 10 sellers have no answer to the questions shoppers ask most — “is it waterproof?”, “does it fit wide feet?” — and your feed is missing two fields Google’s AI surfaces read.
AI-readiness audit — coverage by content type
I can draft the missing highlights and use-case tags now — 214 credits, and you review everything. The Q&A answers are yours to write: add them once on the project and I’ll carry them into the feed.
Go ahead.
AI channel readiness · 214 products
Google AI surfaces
Highlights & details present
Microsoft Copilot
Checked against Microsoft’s own spec
Buyer-intent Q&A
3 top sellers need answers from you
Review-backed answers
Trustpilot connected — quotes scored
ChatGPT export
OpenAI approves who may deliver a feed
See your gaps before you write a word
The audit is free and read-only — connect a feed and Aimée shows you exactly where your catalog falls short. Free for your first 100 items, no card.
Answers backed by your customers — not just your spec sheet
Where a real customer review makes the buyer-intent answer undeniable
The questions shoppers ask an assistant aren’t all about specs. “Is it actually good for winter?” “Does it hold up?” — these are opinion-shaped, and content written from a spec sheet answers them with claims. Renable can answer them with proof.
Renable connects your review platforms — Trustpilot, Bazaarvoice, Yotpo, TestFreaks — and Aimée extracts the line worth quoting from each product’s reviews, scores her own confidence in it, and can carry it into the content that ships in your feed — and it’s the evidence in front of you when you write your buyer-intent answers. Every quote is fact-checked against the review it came from and attributed by first name.
AI-discovery content written from a spec sheet all reads the same. Answers grounded in your customers’ own words are yours alone — no other platform pairs native review mining with AI-discovery enrichment. It’s the same honesty line as everywhere here: this makes what the assistant reads stronger; it never dictates what the assistant says.
From your reviews
Third winter in these and they keep earning it. Still dry inside after a river crossing, and they came up true to size.
Aimée extracts the quotable line — 88% confidence
Is it good for winter?
Yes — the waterproof membrane keeps feet dry down to freezing. And a customer backs it up:
“Still dry inside after a river crossing.”
— Sofia
Google first: it ships in the feed you already export
The enriched content rides real Google Merchant fields — product highlights, product details, richer descriptions. No new infrastructure and nothing extra to maintain: if Renable builds your Google feed, AI discovery is a switch, not a project.
Those fields feed Google’s Shopping Graph — the product data behind Google Shopping, AI Overviews, AI Mode, and Gemini’s shopping answers. Better fields in, stronger candidate out.
Your existing Google Merchant feed
- g:product_highlight Written by the AI-discovery pass
- Lugs that grip wet rock and mud
- g:product_detail
- Terrain: wet rock, mud, hard-packed trail
- g:question_and_answer Written by you
- Good for winter running? — Yes, dry to freezing, with grip for wet trails.
- g:description
- Built for long runs in bad weather, with a membrane that keeps feet dry down to freezing…
Real Merchant Center fields — the same feed that supplies Google’s Shopping Graph, AI Overviews, and Gemini shopping.
Does my feed qualify? Renable checks, channel by channel
Every AI channel expects a slightly different set of fields. Renable validates your feed against each one and shows you what’s present, what’s missing, and whether it’s required or just recommended — before your data ever goes out.
Checked against Google AI surface requirements
2 recommended fields missingRequired — all present
AI attributes — recommended
All twelve prebuilt destinations carry their own field requirements and their own pre-publish checks — Perplexity’s are Google’s, since it ingests the Google Shopping product spec. Required and recommended fields as of mid-2026; these specs move, so Renable tracks them and re-checks your feed against the current requirements.
Every AI surface, and how Renable gets you ready
One honest map. Google’s AI surfaces ride the feed you already export. ChatGPT is a channel you can build today, with OpenAI deciding who may deliver one, and Microsoft and Perplexity are prebuilt channels rolling out now. Renable ships twelve prebuilt destinations in all — ad channels, AI shopping and price comparison — plus a custom feed for anything that isn’t on the list.
Google — AI Overviews, AI Mode, Gemini
Rides your existing Google Merchant feed. The enriched fields are already in it.
Microsoft Copilot & Bing
A prebuilt Microsoft channel with its own pre-publish checks — Microsoft’s availability spelling, its image formats, its 30-day feed expiry. Microsoft Merchant Center does accept Google-formatted feeds, so you can be reachable without us; the named channel is what catches the differences. Renable for Microsoft
ChatGPT shopping
A channel that builds your catalog in OpenAI’s feed format and delivers it over SFTP. Being listed is OpenAI’s call: its merchant program is approval-based and US-first as of mid-2026, so we tell you plainly what applies to your store — and point you at the Google feed, which ChatGPT’s answers lean on heavily, meanwhile.
Perplexity shopping
A channel that ships your catalog in the Google Shopping product spec Perplexity ingests — at a hosted URL, or pushed to the SFTP credentials Perplexity issues at onboarding. Its merchant program is free, application-based with business verification, and US-focused as of mid-2026. Perplexity carries no advertising, so its shopping results are organic and it decides what it shows.
Any surface that reads a product feed
Build a custom feed with any field set — XML, CSV, TSV, JSON or JSON Lines, gzipped if the destination wants it — delivered at a hosted URL or pushed over SFTP. The escape hatch for a channel we don’t name natively yet.
Nobody can promise you a spot. So we make your data undeniable.
Google, OpenAI, and Perplexity decide what surfaces in an answer — and any tool that guarantees you placement is selling something it can’t deliver. What you control is your data. Renable’s job is to make it the strongest candidate it can be, ship it in the right fields for every channel, and show you exactly where it still falls short. That honesty is the whole point.
Review at your pace — and what you approve stays put
AI discovery at catalog scale is a volume problem: hundreds of products, several pieces of content each. So generated content goes into your feed as soon as it’s written — review is an editorial pass, not a gate, and a routine highlight never waits in a queue for a human to release it.
Structural changes are gated the other way: the enrichment configuration and the per-channel field mapping are drafts until you publish them, versioned and undoable. So content flows by exception, and configuration stays under your hand.
Approving a piece locks it, so later runs never overwrite what you signed off — the one thing you can’t undo by accident. Running an agency? The same review surface widens per client: approve a single piece, a whole product at once, or everything you’ve selected.
Trail Runner 300
Highlight · “Grips wet rock and mud”
Alpine Coat
Highlight · “Bone dry in wet snow”
Summit Blazer
Use cases · “Commuting”, “Light rain”
Generated content is in your feed straight away — approving a piece locks it so later runs never overwrite it. The configuration behind it stays a draft until you publish.
Frequently asked questions
What is AEO / GEO, and do I need it?
Will this get my products into AI answers?
Do I need a new feed or integration for Google?
Can I export a feed to ChatGPT?
What about Perplexity?
What about schema markup and crawlability on my own site?
Who approves what gets published?
What does it cost?
Find out how AI-ready your catalog is
Connect a feed and ask Aimée. The audit shows you the gaps; the fix loop closes them — with your approval on every piece. Free to start, no card required.