Make your products
findable inChatGPT
Listwiser turns your Shopify catalog into what AI needs to recommend it, so you get suggested instead of skipped.
- No card required
- Read-only Shopify scope
- 2-minute connect
What’s happening right now
Here’s what AI tells your buyer.
For 25 years, buyers searched. Now they ask. Product discovery is moving from Google’s ten blue links to one answer from ChatGPT, Perplexity, or Gemini.
Watch what happens when that one answer is about a product you sell.
recommend lightweight men’s running shoes under $120
Based on lightweight build, breathability and your price ceiling, three options stand out:
- AeroRun Lightweight Trail: breathable mesh, ~$95, ships in 2 days
- Strider Cloud 9: cushioned ride, $109
- PaceLab Echo: everyday trainer, $99
Want me to compare materials or fit profiles?
Notice what’s missing →
Trailfox Lite
$89Excluded from every specific running-shoe query.
Velocity Mesh Trainer
$104AI can’t match a buyer’s size or fit to a variant.
Northpath Air
$112Nothing for AI to cite, so it cites a competitor instead.
+ 124 more in your catalog with the same blockers. AI couldn’t read them, so it didn’t cite them.
Your products weren’t outranked. They were unreadable.
The trap
You can’t fix this with a ranking tracker.
Ask ChatGPT the same product question twice. You’ll get two different answers. Ask it tomorrow, different again. Reword the prompt, different again. Same prompt ten minutes later, different again.
That isn’t a glitch. It’s how these models work. Every answer is generated fresh. There is no ranked list. No “position #3 in ChatGPT.” No leaderboard to climb.
So when a tool sells you “your AI visibility score,” it’s selling you the weather. The only thing that holds steady across every model, every update, every query is the data underneath.
AI doesn’t rank bad data. It ignores it.
What other tools sell you
Your “AI ranking” over 30 days
+/- 84 pts/day
LLMs sample from a probability distribution every answer. There is no “rank” to track. Just variance dressed up as a metric.
What you can actually move
Your product data completeness
The same product, with the same data, gets the same recommendation across every model, every update, every prompt. That’s the lever.
One of these is signal. The other is the thing other tools sell you.
You’ll hear this called AEO, GEO, or AIO. We just call it being readable by AI.
The pivot
So fix the only thing you actually can.
AI systems can only surface what they can read. These are the four signals they read first.
Findability
AI can match your product to what buyers actually ask for.
Specs
Facts in fields, not paragraphs.
Rich listing
Real images, real proof. Not empty shells.
Commerce
In stock, ships by, fresh data — not ghost listings.
See your catalog the way AI sees it
Most catalogs are nearly invisible to AI.
Toggle to see what changes after one structured pass. Same products. Same store. A different data foundation.
AI Readiness
AI surfaces can’t resolve types, parse specs, or compare your variants.
Numbers shown are illustrative averages from our private calibration runs. Your baseline may differ.
How Listwiser fixes it
One catalog intelligence layer. Four moving parts.
Each step is something you can see in the product today. Make products recognizable, structure the facts AI quotes, queue the next blocker, ship to every surface AI reads.
Make every product recognizable to AI shopping queries.
We read your titles, tags, metafields and platform context, then reconcile what your product actually is: what it’s for, who it’s for, what it works with. No more “uncategorized”. No two-source disagreements.
- Buyer-intent matching
- Variant-aware scope
- Auto-resolves clear cases, flags edge cases
Trailfox Lite Runner Men’s
SKU TFL-001 · Shopify
Detected candidates
Resolved
Source
title + tags
Registry
listwiser/v3
Reviewed
auto · ok
Turn paragraphs into fields. Turn variant chaos into a matrix.
Material, fit, weight, care: the things AI compares on become typed fields, not prose. Variants become an explicit matrix the way buyers actually shop.
- Typed fact extraction
- Variant matrix builder
- Per-attribute provenance
Facts (typed)
Variants (matrix)
| Size | Color | Stock |
|---|---|---|
| 41 | Black | 12 |
| 42 | Black | 8 |
| 42 | Storm | 4 |
| 43 | Black | oos |
| 43 | Storm | 6 |
| 44 | Storm | 3 |
We tell you the next blocker. You click. It’s done.
Listwiser surfaces the smallest, highest-impact fix on every product: clarify what it’s for, add a brand, split a flattened variant. Most are one click.
- Severity-ranked queue
- One-click apply with preview
- Skip with audit reason
Next blockers
3 open · 1 doneResolve product type
BlockerAI couldn’t classify this. We suggest running_shoes / men.
Add brand
SuggestedNo brand metafield set. Detected from title: AeroLite.
Split variants
Suggested"Size: see title". We found 6 SKU rows. Apply matrix?
Verify material
DoneEngineered mesh + rubber outsole. Source: description.
One catalog. Every surface AI reads.
One structured version of each product, mapped cleanly to Google Shopping, Amazon, social commerce, your PDP, and AI discovery surfaces, without per-channel rewriting.
- One product, every channel
- Per-channel adapters
- Audit log per output
{
"product_type": "running_shoes",
"title": "Lightweight Running Shoes",
"brand": "AeroLite",
"material": ["mesh", "rubber"],
"weight_g": 248,
"variants": [
{ "size": "42", "color": "black" },
{ "size": "43", "color": "storm" }
],
"channel_outputs": { ... }
}Distributes to
One structured object → many surfaces. No copy-paste, no per-channel rewrite.
Honest scope
What we don’t claim. What we actually measure.
Every other line on this page is built on this. We’re a data-quality engine with honest measurement, not a ranking dashboard pretending LLMs behave like Google.
We don’t claim
We do measure
"You rank #2 in ChatGPT"
Real AI-referral traffic from ChatGPT, Perplexity, Claude, Gemini
"Your AI visibility score: 73"
Your structured data completeness, blocker count, intent coverage
A higher "AEO / GEO score"
Facts AI can actually extract, measured per attribute, per product, with provenance
Guaranteed AI placement
Products at top readiness see meaningfully more AI-driven traffic in our calibration studies
Live per-merchant LLM polling as a feature
Internal calibration runs that translate readiness into expected pickup
FAQ
Plain answers, no marketing voice.
The questions buyers, ops leads and CTOs actually ask us. If yours isn’t here, we’d rather you ask than guess.
Still wondering?
Grab a 20-min walkthrough with a founder. No deck, just your catalog on a shared screen.
hello@listwiser.aiNo. Ranking trackers measure noise. LLMs sample from a probability distribution every answer, so there is no stable rank to track. Listwiser fixes the data underneath, which is the only thing that stays constant across every model and every query.
10 questions
Stop being unreadable
Make your catalog the part of your business AI can finally read.
Connect your store in two minutes. We’ll run a real readiness scan and show you the exact blockers AI sees in your catalog today. No card required.