The catalog that
knows what fits.
A shopper picks the one garment that already fits them. Your whole catalog re-sorts around it, measured per SKU.
We don't predict fit. We measure it.
Two numbers that move.
A new way to discover fashion. Shoppers browse your catalog by fit, not by size charts. Fit-led experiences lift conversion 3 to 8% at catalog scale.
Fit-driven returns run 24.4% across the industry. Measured data cut them to 5.5% for fashion sellers using Size AI.
Shop by fit.
Not by size.
Lower returns. Higher sales. A new shopping experience for both new and returning buyers.
Ancient artifacts.
Brands publish them, but shoppers skim past or misread them. The chart goes unused, the order ships anyway, and the misfit shows up at home.
- Provided by brands, skipped by shoppers
- Skimmed, rarely understood
- Customers find out at home
Size isn't fit.
Recommenders map shoppers to a size label from survey data. But a label says little across brands, and nothing about how a garment actually fits.
- Built on surveys, not garments
- Gives a size, not a fit
- Never measures the garment
Wrong half of the problem.
A body scan captures the shopper's body. Fit also depends on the garment, and on whether they want it loose or tight. A scan sees neither.
- Measures the body, not the clothes
- Blind to baggy or tight
- Makes shoppers self-scan
Size AI for BrandsBuilt on a garment they already love.
Every shopper owns something that fits just right. Size AI measures your catalog against it, per SKU, to the millimeter. Fit becomes real data, not a guess.
- True garment measurements
- Measured per SKU
- Built for catalog scale
Real measurements. Per SKU. At catalog scale. The way no one else can.
The Size AI Fit Engine
Every garment gets a fit profile, the shopper's included. The engine compares fit profiles, not size labels, and every surface gets the answer.
The same live engine behind the Size AI app: measurement, label data, descriptions, and imagery. Per SKU, at catalog scale. See the platform →
Questions brands ask first.
The procurement-and-integration questions that come up most often. If yours isn't here, talk to us.
They infer fit from past shopper behavior or body surveys. We measure the garment. Different layer of the stack, garment data sits underneath any rec engine, including the one you're already running.
Garment data feeds them. We don't replace your rec engine; we give it the input it's been missing.
No, most engagements use catalog ingestion. You ship samples or send your catalog spec, we measure each SKU and return structured rows. iPhones not required on your side. For marketplaces or seller-side workflows, we also offer an iOS SDK that embeds capture inside your app, see /platform for the technical spec.
Yes. See the JSON sample on /platform. Every garment returns up to 12 measurements and 200+ structured data points, including real fiber composition, stretch estimate, and background-removed product imagery.
REST API, SDK, or batch CSV ingestion. We shape the integration around your stack, not the other way around.
Most brands see a measured-data sample within 2 weeks of the discovery call. Full pilot deploys in 4 to 8 weeks.
Conversion lift and returns reduction typically show in trailing-30-day data 60 to 90 days after surface deployment.
We size pricing to your catalog volume and use case. Per-SKU and revenue-share models are both available, we walk through the right shape on the call.
Data isolated per partner, encrypted at rest and in transit, deleted on contract end. SOC 2 in progress. GDPR-compliant. EU residency available.
12-month default, no auto-renew. On termination, your data is returned in structured format and our copy deleted within 30 days.
See it on your own catalog.
45 to 60 minutes. We measure your garments live on the call.
1M+ measurements captured · 10,000+ fashion sellers · built by the team behind Sizely
