FIT INTELLIGENCE FOR APPAREL RETAILERS

Real measurements. Better fits. Fewer returns.

Most apparel returns are fit returns. ShirtMath helps apparel retailers lower fit-related returns by helping shoppers choose better-fitting products using real garment measurements: it reads the measurements of a garment a shopper already owns, matches them against your own catalog, and returns a recommendation with an explicit confidence level — or an honest “not enough information” instead of a guess.

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Three catalog connectors built today: CSV feed, JSON feed, custom retailer API

A recommended size with the evidence behind it — or an honest “not enough information”

Your catalog only: no shared pool, no fallback to another retailer's products

Last verified: 25 August 2026

Sandbox example · b2b‑fit/1.0.0

From a shirt that already fits, to the size to order.

Shopper’s reference garment

Shoulder 17.8″ Sleeve 8.6″ Chest 20.0″ Length 28.4″

Gildan G500, size M — the shirt that already fits. 20.0″ is that garment’s published chest spec.

Those numbers become the request

POST /b2b/widget/match

chest20.0″
shoulder17.8″
length28.4″
sleeve8.6″
product_refC4017

Headers: X‑ShirtMath‑Key · X‑ShirtMath‑Session

The ShirtMath Fit API runs the comparison

ShirtMath Fit API

200 OK · authority SANDBOX

A size comes back, with the reason

recommended_size

Comfort Colors C4017 Medium · chest 20.5″ published spec

explanation The C4017 medium runs 0.5″ fuller through the chest than the shirt the shopper already wears — inside the 0.3″–0.8″ tolerance published specs carry.

Then the catalog that fits the same way

Heavyweight crew tee Size M
Long‑sleeve tee Size M
Pique polo Size M

Returned as candidates[] — the retailer’s own catalog, sized against the same reference garment.

Illustrative sandbox request against published garment specs. When a product’s measurements aren’t in the catalog, the API returns status FIT_AUTHORITY_UNAVAILABLE instead of guessing a size.

The hero diagram is illustrative — a sandbox request, not a customer's. Every fact in it is written out below as well, so this page reads the same with JavaScript turned off.

More confident shoppers

Reduce size uncertainty and hesitation.

Fewer returns

Lower return rates and support costs.

Higher conversion

Better fits lead to more completed orders.

Stronger loyalty

Great fit experiences build repeat business.

Built for apparel retailers

Why measurement-driven fit matters

Sizing is a leading reason apparel gets returned. ShirtMath removes the guesswork by using real garment measurements — not brand-specific size charts — to recommend the right size across your entire catalog.

Learn more about the product →

Personalized at scale

Deliver a made-for-me fit experience to every shopper, in real time.

Works across brands

Our engine understands sizing differences across brands, categories and regions.

Privacy-first by design

We handle the data you send us carefully: your product records only, nothing more.

Built for performance

A fast API designed to scale with your catalog and your traffic.

How it works

Powerful fit intelligence in three simple steps

See how it works in detail →

Get measurements
Collect shopper measurements via a guided flow or your own.
We run the engine
ShirtMath compares the reference garment against your catalog.
Show the best fit
Return a size recommendation and the products your shoppers trust.

Pricing