The data layer is invisible to customers — until it isn't.

Your Shopify product data was built for launch. Since then, new tools, new channels, and new team members have layered complexity on top of it. The friction you're feeling — in search, in filters, in team capacity — usually traces back to this layer.

Run the free audit →Or read about what we find ↓
20–30%
of returns traceable to inaccurate product data
30–40%
of product team time spent on data maintenance
>20%
conversion lift from clean, unified product data
60%
of ecommerce data errors from disconnected systems
See the complexity

What your product data architecture probably looks like

Horizon Trail Runner
Stride Athletics
Horizon Trail Runner
$149.00
★★★★½"4.5 out of 5"
Lightweight trail running shoe.
Men'sTrailWaterprooffil|gender:mens
custom.* — the junk drawer
custom.genderMensduplicated in tags
custom.activityTrail Runningduplicated in tags
custom.terrainMixed
custom.fitStandard
custom.description28 charsthin content
custom.featuresGore-Tex, Vibram
custom.materialsRecycled mesh
custom.careSpot clean
custom.plc_maintrue
custom.plc_salefalse
custom.coming_soonfalse
custom.waterproof_rating"Very waterproof"stored as text
custom.cushion_level"Medium"stored as text
custom.weight"9.2 oz"stored as text
custom.drop"8mm"stored as text
custom.lifestyle_imgtrail_hero.jpg
custom.title_tagempty
custom.description_tagempty
Tags — structured data hiding here
fil|gender:mensfilter tag
fil|activity:trail-runningfilter tag
sc-running-shoessize chart
S25season
pill-newUI badge
149price as tag
Fullpricecap collision
fullpricecap collision
Dead fields — no longer in use
mm-google-shopping.titleapp removed
mm-google-shopping.descriptionapp removed
plc.full_pricelegacy system
plc.salelegacy system
plc.tacticallegacy system
custom.inv_us_atsmigration artifact
checkoutblocks.upsellinactive
AI agents see: title, price, 1 image. Missing structured specs, thin description, no JSON-LD.
Data complexity
78
Consolidate namespaces
Migrate tags to metafields
Type your fields correctly
Remove dead fields
See what your data actually looks like →

What we find in every audit

Part 1
Your Shopify Product Data Is Holding You Back
Why your search tool, redesign, and CRO program are all underperforming for the same reason. The friction you're feeling isn't a UX problem — it's a data architecture problem.
Read Part 1 →
Part 2
The Product Data Audit — What We Actually Find
The patterns that show up in every audit: redundancy, archaeology layers, the custom.* junk drawer, and manual processes that don't scale.
Read Part 2 →
Part 3
The Product Data Cleanup You Need Is Also Your AI Strategy
The same cleanup that fixes operations makes your catalog visible to AI agents. One audit, two outcomes — and the timing matters.
Read Part 3 →

How the audit works

1
Export your product data
Use Matrixify to export your Shopify products as a CSV. Takes about two minutes.
2
Upload and run
Drop the CSV into the audit tool. Add your store URL and team context for a richer analysis.
3
Get your findings
Instant structural analysis. AI-powered findings, recommendations, and an AI readiness assessment.

The audit is a directional starting point — not a diagnosis. It identifies patterns worth investigating. Your raw data never leaves the browser; only a structural summary is used for the AI-powered analysis.

Run the audit →

Beyond the free audit

The free tool gives you a directional snapshot based on a product export. A full Product Discovery engagement maps every field, every feed dependency, and every process across your complete catalog. The deliverable is a prioritized remediation plan your team can execute — with two pathways: one focused on optimizing your existing Shopify architecture, and one that introduces ERP and PIM integration for full automation.

Talk to V+V →
Metafield architecture review
Product lifecycle routing
Channel state management
Inventory governance
Content management workflows
Search & discovery taxonomy
ERP & PIM integration readiness