A True Fit alternative built on garment measurements

What is True Fit?

True Fit is a fit and sizing platform sold to retailers. Its home page describes it as "Fit & Sizing Intelligence for Retailers" (truefit.com home page, retrieved 24 August 2026). The same page describes its recommendations as converted from purchase, fit, keep and return signals gathered across the brands in its network (truefit.com, retrieved 24 August 2026).

Its public materials describe two ways it reaches a shopper. One is an embedded shopping agent that can sit on product detail pages, listing pages, search and the homepage, described as Shopify Plus certified. The other is a fit intelligence layer exposed through the Model Context Protocol, so a retailer can feed it into agents, search or personalisation systems it already runs (truefit.com about page, retrieved 24 August 2026).

This page states only what truefit.com documents publicly. The shopper-facing pages checked on 24 August 2026 do not spell out what a shopper enters to receive a recommendation, so nothing is claimed here about that. Pricing is not published on the pages checked, and none is invented below. The audience and transaction figures its marketing pages carry are not restated here, because we have no way to verify them.

What does ShirtMath do instead?

ShirtMath compares garments, not shoppers. It reads the flat measurements of a shirt you already own and puts them next to published measurements for other garments, so the comparison is inches against inches. There is no purchase history behind it and no behavioural network. The unit of comparison is a number on a spec sheet.

That is a deliberately smaller claim. A recommendation engine tells you which size to order. A measurement comparison tells you what the garment measures and lets you decide. The second is easier to check, because you can lay a tape across the shirt in your drawer and see whether the figure holds.

How do the two approaches differ?

Neither column is a criticism of the other. They answer different questions for different buyers.

What do garment measurements actually show?

Here is the case that made ShirtMath worth building. Four blanks, all labelled Medium.

The ladies' G500L publishes 17.5" at Medium against 20.0" to 20.5" for the unisex Mediums. That is a 3 inch gap on the same letter, and it is wider still at Large. The word Medium is naming a different garment depending on the cut, which is the whole problem a letter size cannot solve. Our page on vanity sizing covers why the letter drifts in the first place.

Numbers like these are the entire input to a garment-first comparison. If you want the full spread across styles, the blank comparison lays it out, and the chest measurement data holds the underlying table.

When True Fit is the better choice

If you are a retailer, True Fit is buying you something ShirtMath does not sell, and you should take that seriously.

1. You want a recommendation on every product page — True Fit documents deployment across product detail pages, listing pages, search and the homepage. ShirtMath is a site you send a shopper to, not a layer that sits inside your catalogue.
2. You want behavioural signal, not just specs — purchase, fit, keep and return outcomes carry information that a measurement chart cannot contain, such as how a fabric behaves after a wash or which sizes come back. ShirtMath has no equivalent data and does not pretend to.
3. You sell footwear as well as apparel — True Fit's public materials cover both. ShirtMath's live capability is shirts.
4. You need an enterprise contract and support — True Fit is an established vendor with a sales organisation. ShirtMath is early, and an early tool is the wrong choice for a catalogue-wide rollout.

A retailer wanting a body-based recommendation embedded across a whole catalogue, with purchase-history signal behind it, is describing a product category ShirtMath is not in. If that is the brief, the comparison ends here. If you want to know what a measurement-led approach could add alongside it, the returns page sets out the narrow version of that argument.

When garment measurements are the better tool

The reverse case is just as real. A recommendation engine lives on the retailer's site, which means it is available where the retailer bought it and nowhere else. If you are shopping secondhand, buying blanks for printing, or comparing two listings on different sites, there is no engine in the page to ask.

Measurements travel. A flat chest figure means the same thing whoever is selling the shirt. If you already own a shirt that fits, its measurements are a specification you can carry to any listing that publishes numbers. That is the case for garment-first sizing, and it is why the comparison is inches against inches rather than a size letter against a size letter.

The distinction between measuring a body and measuring a garment is worth its own page, and we wrote one: body measurement versus garment measurement.

What ShirtMath cannot do today

Being specific about the boundary is more useful than a feature list.

1. It reads shirts, not bodies — there is no body scanning, and no plan on this page to claim otherwise. You photograph a shirt laid flat with a scale card.
2. A scan is not a tape — readings come from a photograph and carry error, so treat one as a starting point and check anything borderline with a tape.
3. Sleeve opening and bicep are the least reliable readings — anything the scanner cannot read confidently is left blank rather than guessed.
4. Shoes and eyewear are reference material — those pages explain how to measure and how to read frame numbers. A photograph alone cannot tell you your shoe size; that needs your known size or a foot measurement.

The methodology page covers how readings are produced and where the error comes from.

Common questions

Where to go next