Bold Metrics alternative

What is Bold Metrics?

Bold Metrics is a business-to-business sizing company that sells body-data tools to apparel brands. On its own site (boldmetrics.com, retrieved 24 August 2026) it lists five named products: Smart Size Chart, Virtual Sizer, Virtual Tailor, Apparel Insights and the Agentic Sizing Protocol. The same page describes building "a digital twin for each shopper, mapping over 50 individual body measurements", and states that the approach needs "No scanning, photos, or measuring tapes".

Bold Metrics publicly names apparel customers across activewear, outerwear, menswear, formalwear, kidswear and workwear or uniform supply. Everything above is quoted or paraphrased from Bold Metrics' own public marketing. We have not tested the product, we hold no accuracy figures for it, and we will not invent any. If you are evaluating it seriously, ask Bold Metrics directly for validation data on your own category.

The shape of the offer matters more than the feature list. Bold Metrics is bought by a brand and runs inside that brand's store, against that brand's catalogue. The shopper does not choose it. That is the structural difference between it and ShirtMath, and it decides most of the comparison below.

What does ShirtMath do instead?

ShirtMath measures the garment, not the person. You photograph a shirt laid flat next to a scale card, ShirtMath reads its flat dimensions, and it matches those dimensions against published garment measurements across a catalogue of styles. No body model is built at any point. There is no body scan, no height and weight prediction, and no digital twin.

The two approaches answer different questions. Body prediction answers "what size should this person buy in this brand". Garment measurement answers "which garments are shaped like the one that already fits". Neither is a substitute for the other, and the longer version of that argument lives on body measurement vs garment measurement and garment-first sizing.

Why does measuring the garment change the answer?

Read the Medium column. A ladies' Gildan G500L Medium publishes 17.5" flat chest. The unisex Mediums publish 20.0" and 20.5". That is a gap of two and a half to three inches on the same letter, from the same manufacturer, and it reaches a full 3.5" at Large. "Medium" is not a size here. It is a label attached to a different garment depending on the cut.

One correction while we are in the table, because the internet repeats it constantly: a Comfort Colors Medium is not 22" and it is not "two inches wider than a Gildan Medium". C4017 publishes 20.5" at M against 20.0" for the G500, half an inch rather than two, and level with the fitted Next Level Medium. The 22.0" figure people quote is a size L number on the unisex ladder; the C4017's own Large is 22.5". C4017 is garment-dyed, so it also carries the wider end of the tolerance range at about ±0.8", which that half inch sits comfortably inside.

A body-prediction engine can be entirely correct about a shopper's chest and still land them in the wrong garment, because the mapping from body to letter is the brand's to define and every brand defines it differently. That drift is the subject of vanity sizing, and it is why we publish numbers rather than letters. If you want the same comparison across more styles, see which blank runs biggest.

When Bold Metrics is the better choice

Bold Metrics is the better tool in several situations, and pretending otherwise would waste your time.

1. You are a brand fitting your own catalogue — Bold Metrics is sold to brands and runs inside the brand's own product pages. If your goal is a size recommender on your PDP, that is the product category you are shopping in, and ShirtMath's shopper-side scan is not a like-for-like replacement.
2. You need body measurements, not garment measurements — Bold Metrics states it builds a digital twin mapping over 50 body measurements. ShirtMath produces zero body measurements. If your grading, pattern or design workflow needs body data, ShirtMath cannot supply it at any price.
3. The shopper must not have to do anything — Bold Metrics states its approach requires no scanning, photos or measuring tapes. ShirtMath's scan requires a real shirt, a flat surface, decent light and a scale card in frame. That is a genuine friction cost, and for a checkout flow it may be the deciding one.
4. You are sizing categories we do not read — the ShirtMath scanner reads shirts laid flat. It does not measure trousers, dresses, footwear or eyewear from a photograph. A vendor covering your whole range in one contract is a fair reason to pick that vendor.
5. You need an enterprise vendor relationship — support commitments, security review, contracted integration. ShirtMath is pre-launch. We publish no customer list, no case studies and no return-reduction figures, because we do not have any yet. Our brand side is early.

When garment measurements answer better

The reverse case is narrower but real. A garment-first approach is the better fit when the question crosses brands rather than sitting inside one. A body-prediction engine deployed by Brand A cannot tell you how Brand A's Large compares with the shirt you already own from Brand B, because it only sees Brand A's catalogue. Measuring the shirt you own puts every brand on the same axis, which is the whole of what size am I in a different brand.

It also helps when the reference garment is the answer. If one shirt in your wardrobe fits and the rest do not, the useful output is not a letter, it is the flat chest and body length of that shirt and a list of styles that publish similar numbers. Our methodology sets out how we match, and the t-shirt size chart shows the published figures we match against.

How do the two compare side by side?

What ShirtMath cannot do today

There is no body scanning of any kind, now or planned for this release. There is no shoe identification, no shoe fit from a photograph, no sunglasses measurement from a photograph and no face-shape analysis. Our shoe and eyewear pages are reference material, not scanning features. We hold no accuracy percentage, no return-reduction percentage and no conversion figures, because we are pre-launch and have not measured any. Anyone quoting you such a number for us is quoting fiction.

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