Get your products recommended in
ChatGPT, Gemini, Copilot, or Google AI Mode
AI assistants recommend products they can match to a shopper’s question. Most catalogs don’t store the details those questions depend on: what a product fits, who it’s for, what it solves. Paste your store URL and see what yours is missing.
Free. No install, no login, no Shopify permissions.
The same product data powers your search and the AI assistants.
A search engine can’t find an attribute you never wrote down.
Keyword, semantic, vector, AI. Every search engine does the same basic job: it ranks what’s in your product records.
So when a shopper asks for the bracket that fits a 2019 F-150 with the 3.3L and your catalog doesn’t store engine variant, there’s nothing to rank. The engine didn’t fail. It told you the truth.
Most catalogs describe what a product is: name, price, size, colour, some marketing copy. Very few describe what it’s for, what it fits, or what problem it solves. That’s what shoppers ask about.
What the shopper asked
“waterproof hiking boots, wide fit, under £150”
What your catalog stores
title · price · colour · size · vendor
No width fitting. No waterproof rating. No activity.
What any engine can return
Nothing to match on.
Fix the data first, and every engine gets smarter, including ours.
Most stores fail the same questions.
This isn’t a problem with your store. It’s a problem with how catalogs are built. Baymard Institute’s 2026 benchmark of 170+ ecommerce sites found that most still can’t handle the questions shoppers actually type.
56%
of sites fail to adequately support shoppers’ search needs
44%
have issues with compatibility searches (“fits my…”)
37%
have issues with symptom searches (“something for dry skin”)
Source: Baymard Institute, Ecommerce Search UX benchmark, 2026. source
One gap. Four places it costs you.
Every system that finds products for shoppers reads the same product records. When those records are thin, all four break at once, and only one of them tells you.
AI shopping assistants
Shopify shares eligible catalogs with ChatGPT, Gemini, Copilot and Google AI Mode by default. Being there is free. Being recommended depends on whether your data can answer the shopper’s question.
In the channel. Not recommended.
On-site search
Need-based questions return nothing, or the wrong thing. It’s the only failure you can see, which is why most merchants think it’s the whole problem.
0 results
Collection filters
Shopify builds filters from your product options and metafields. Empty fields mean 4,000 products, two filters, and a shopper doing the sorting alone.
2 filters · 4,000 products
Shopping feeds
Missing attributes get listings limited or disapproved in Google Shopping and marketplaces. Your ads compete on data quality before they compete on bid.
812 items · limited
Fix the records once, and all four improve together.
Source: Shopify, March 24, 2026. shopify.com/news/agentic-commerce-momentum
You can’t see this loss in your analytics. That’s what makes it expensive.
A zero-result search gets logged. Almost nothing else does.
The shopper who opened your filters, found three, and left. The product listing that never served. The AI assistant that recommended someone else. None of it shows up in Shopify Analytics or GA4.
Your conversion rate is measured on the shoppers your catalog managed to keep. It looks fine because the ones it lost were never counted.
After a failed search, around 8 in 10 shoppers say they’re more likely to leave and buy elsewhere.
Source: Algolia, 40+ stats on ecommerce search. source
Your store should answer like your best salesperson.
Your best staff member knows what fits, what it needs, and what to suggest when something’s out of stock. Your catalog should know the same things, for every product, for every shopper, at 2am.
The question
A shopper types “L-bracket for Sony A7 IV”.
The answer
Your store shows the brackets that fit, best match first, with “you’ll also need: Arca-Swiss plate.”
The result
One order, two items, no “will this fit?” email, no return.
Fewer “will this fit?” emails
Filters that actually narrow things down
Shopping feed listings that serve
A catalog AI assistants can recommend with confidence
The kind of catalog big retailers pay whole teams to maintain. Kept up to date for you, with your approval on every change.
See what your store could answerFind the gap. Fill it. Prove it worked.
We test your catalog the way shoppers do
Not a checklist of empty fields. We run your real products against the questions shoppers in your category actually ask, and show where your catalog has no answer.
We fill in what’s missing, across the whole catalog
Use case, compatibility, suitability, substitutes. We work out how your products relate to each other, not one product at a time. Anything we’re not confident about goes to you for review. We never guess into your store.
We write it back into Shopify, and measure
Structured metafields and taxonomy attributes, in your own store. Your search, filters, feeds and AI channels all read them. Then we measure what moved.
Shopper asks: “waterproof hiking boots, wide fit, under $150”
Trailmark · Ridge GTX
Norpeak · Alpine Wide
Kestrel · Trek Pro
Harlow · Trailwalker Wide
Not shown to shopper — missing width fitting, waterproof rating
Harlow · Trailwalker Wide
Matched on wide fit, waterproof membrane
Trailmark · Ridge GTX
Norpeak · Alpine Wide
Kestrel · Trek Pro
Pick a category. Same shopper question, same catalog — before and after the missing attributes get filled in.
Keep your search app. Or use the one built for this data.
ReLUnit makes any search engine better, because it gives the engine something to find. But most engines treat new attributes as more text to match.
ReLUnit Search reads the structure we write: what fits what, what’s required, what replaces what. So when a shopper asks “will this fit a 2019 F-150 3.3L,” they get the part that fits, plus the part it needs.
Use your current search
- Best for
- Stores already on a search app they like
- What you get
- Richer catalog data your engine reads automatically
- Setup
- Nothing to change on your storefront
Use ReLUnit Search
- Best for
- Stores on Shopify’s native search, or unhappy with their current app
- What you get
- Search that uses compatibility, use-case and substitute links directly
- Setup
- Replaces your search bar and results page
What ReLUnit replaces, and what it doesn’t.
A search app
Ranks the data you already have.
Keep it, and we give it more to find. Or switch to ReLUnit Search.
Shopify’s native tools
Define around a thousand category attributes and add the fields.
We fill those fields in, using what we learn across your whole catalog.
A PIM
A governed place to store attributes.
A PIM stores attributes; we produce them. Under 50,000 SKUs on Shopify, you probably don’t need a PIM yet.
A bulk AI editor
Fills one field on one product at a time.
That’s the easy half. Knowing this part fits that vehicle is the half shoppers ask about.
An agency cleanup
A one-time snapshot.
It fades with the next supplier feed. We keep running.
ReLUnit isn’t for you if you have a few hundred SKUs, a hand-curated single-brand catalog, or products people find by name.
Shopify’s native filtering will do the job. Keep your money.
You’ve paid for tools that promised results and delivered a dashboard. This one only gets paid if it delivers.
09The pilotA 90-day pilot, measured against a target we agree on first.
Before we start, we agree on three targets together:
- How many of your category’s shopper questions your catalog can answer
- How complete the attributes are on the fields your search, filters and feeds depend on
- Your zero-result search rate
If we don’t hit them in 90 days, you don’t pay for the pilot.
We also measure revenue impact against a holdout group and report it honestly, including when the result is unclear. Ninety days on a live store is noisy, and we’d rather tell you that than sell you a number we can’t defend.
Want ReLUnit Search in the pilot too? We’ll test it against your current search on the same questions, so you see the difference side by side.
Pilots are scoped after your free audit. from $X/month
Straight answers.
Shopify defines the fields: over a thousand category attributes. It doesn’t fill them in for your catalog, and it works one product at a time. Shopify also shares your catalog with AI channels, but it shares whatever data you have. We make that data worth sharing.
It is, and it’s a good baseline. But AI search still ranks what’s in your records. If your catalog doesn’t say what a product fits or solves, no engine can find it. We fix the records first.
Keep it. ReLUnit gives it more to find. If you’d like to compare, we can test ReLUnit Search against it during the pilot.
No. The catalog layer works with any search app, your filters, your feeds and AI channels. Search is optional.
Confidence thresholds, review queue, reversibility. Be specific about the threshold and the review step — this is the top objection in regulated categories.
Not without your approval. The audit is read-only and needs no install.
It stays in your Shopify catalog. That’s the design.
The audit takes about a minute. Writing data back takes days. Measuring impact takes the full 90.
Concrete answer: what you store, for how long, who can see it, what scopes the app requests. Not "security is our top priority".
Find out what your store could be answering.
Paste your store URL. We’ll show you the questions your products miss today, and what they’d answer once your catalog is fixed.
Check my catalogFree. No install, no login, no Shopify permissions.
Talk to us about a pilot
Tell us about your store. We’ll reply within one business day to scope a 90-day pilot.