Unspun Fit-to-Factory Loop
Unspun
Unspun is trying to own the handoff between fit data and factory output, which turns sizing from a recommendation feature into a production input. A shopper scans with a phone, FitOS turns that body data into a garment pattern, and Vega weaves the pant leg shape from yarn with far fewer cut and sew steps. That is different from scan only tools that stop at size advice, or software layers that still depend on standard factory workflows.
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MTailor also starts with a phone based body scan, but its flow ends in bespoke tailoring. The app captures measurements and generates a custom pattern, then human tailors make the garment. That improves fit, but it does not redesign the production system itself.
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3DLOOK helps brands map a body to an existing size chart or virtual try on experience. In practice, the customer takes photos, gets a size recommendation, and buys from the normal assortment. The measurement data improves conversion and lowers returns, but usually does not create a one person, one pattern manufacturing loop.
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On the factory side, incumbents like Shima Seiki prove seamless production can work, but mainly in knitwear. Unmade shows the opposite model, software that routes online demand into existing machinery. Unspun sits between those poles by combining consumer measurement, pattern generation, and new woven apparel hardware in one stack.
If Unspun keeps pushing this loop into brand channels and microfactories, apparel starts to look less like buying from a size grid and more like ordering a file that gets made nearby. The companies that win will be the ones that can turn body data directly into fast, low waste production, not just better size advice.