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Why GarmCheck

The try-on that reads the body, not the database.

Built for fashion brands who'd rather see their own garment on their own customer than guess from someone else's returns.

Editorial fashion photography demonstrating photo-based fit
Editorial photo of a model used as input for AI fit detection
01 — Photo, not history

Reads the body in the photo. Not the data of strangers.

Most size tools learn from other people's returns. GarmCheck detects measurements directly from your customer's image and maps them against each garment's specific cut — so even when a brand's sizing shifts between product lines, the recommendation is always based on that garment, that body, that fit. No historical data required. Accurate from day one.

Portrait demonstrating identity-preserving try-on output
02 — Identity preserved

It's still your customer in the mirror.

Cheaper try-on tools quietly swap your customer for a generic AI avatar that vaguely resembles them — same garment, different person. We don't. Face, skin tone, hair, posture — preserved. The garment changes; the person doesn't. The result is a try-on that customers actually trust enough to buy from, and share. Every render is a piece of UGC waiting to happen.

Rack of garments with distinct silhouettes
03 — Garment-aware

Each cut analysed. Each fit respected.

Oversized tees, tailored blazers, slip dresses — they don't fit the same body the same way. GarmCheck parses the garment's silhouette and ease, then pairs it with the detected body. No flat substitution. No 'one size suggestion fits all'.

Boutique fashion store interior
04 — Built for £1M–£20M brands

Enterprise capability without the £200k build.

Zara committed nine figures to virtual try-on infrastructure. The same class of underlying technology now ships to your storefront in a single line of code. No SDK, no rebuild, no 18-month roadmap.

Folded apparel inventory representing reduced returns
05 — Returns down, conversion up

The economics that justify the line item.

93% of fashion returns are size-related. GarmCheck is built to reduce size-driven returns and lift PDP conversion. The ROI calculator on the home page shows you the maths before you commit.

What you actually get

No quiz. No SDK. No 18-month roadmap.

  • Photo-based fit detection — no quiz, no history
  • Garment-specific cut analysis on every product
  • Identity-preserving renders customers want to share
  • One-line install. Works with Shopify, Dawn, Debut, custom themes
  • Results in ~10 seconds per try-on
  • GDPR-aware. Photos processed, not retained, by default
Who's building it

Ex-Google AI engineers. With time on the shop floor.

We're ex-Google, deeply versed in applied AI and machine learning — the same disciplines that power large-scale computer vision and recommendation systems are what sit under GarmCheck.

And we know fashion from the inside. In previous roles we've worked with Adidas, New Balance, 424 on Fairfax, and the LVMH Group — so the product is built around how fashion brands actually merchandise, size, and sell, not a generic ML demo retrofitted to apparel.

  • Ex-Google AI / ML
  • Adidas
  • New Balance
  • 424 on Fairfax
  • LVMH Group
Why GarmCheck?

The only virtual try-on tool that reads your customer's body — not their purchase history.

 GarmCheckAntlaGenlook
Core features
Virtual try-on from a single photo
One-click Shopify install, no code
Analytics dashboard
Klaviyo integration
Fit intelligence — GarmCheck only
AI reads body measurements from photo
8-point measurement detection
Per-garment size recommendation
Oversized range support
Hard block for extreme size mismatches
Per-product custom size charts
Customer intelligence — GarmCheck only
Customer segment targeting
CRM body measurement export
Klaviyo body measurement profile sync
Return correlation tracking

See your garment on a real body in 10 seconds.