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We have a special soft-spot for fit because

Existing fit tools simply don't work; they're expensive & they don't reduce returns. 

Why? They all clothing is cut correctly. The body measurement chart is reliable. 

We know the problem is every single item is cut differently, due to human judgement, which leads to garment size being unreliable, which is why consumers don't know which size to pick.

That set us on a mission to help fix fit pre-production. Pre-production means we're able to boost in-store sales as well as e-com sales.

But in order to access all the data we need to be able to fix fit pre production, we also end up with all the data you'd need to fix fit post-production.

 

ie we know your customers are going to return that oversized top at 63% for running too large, we can auto update the website to flag that & prevent those returns.

Or we know dresses return mainly for length issues & no one uses the measurement, let's put annotated imagery of the length of this unique dress over women between 5ft and 6ft.  

And as it happens - we also end up with all the data you need to make production improvements that aren't fit related, like which manufacturers are having the most quality issues, which colours are popular, what fabrics do consumers find too scratchy, etc. 

So we broadened our scope to feedback & suggestions across production/buying. But so long as fit is 71% of returns, it'll be 71% of our focus. 

Here's how garment fit works:

1. The designer decides the design and gives it to a pattern cutter to make the size small. 

2. The pattern cutter has the brand's body measurement chart & the design. Let's say they're making a size 12 women's top. The body measurement is 92cm. The pattern cutter decides, based on expertise, what size to cut the top. 92cm.... 96cm.... 100cm... it depends on the fabric & what layer it is, eg sports bra vs cardigan.

3. There is no calculator, the pattern cutter makes that decision for this garment. For a $1B retailer, they might work with 20 pattern cutters in house, and hundreds at the manufacturers. Therefore you can have thousands of people all making these estimates based on their own opinions/expertise/best guess. 

4. This means every single size 12 top comes out a slight different size. You'd think it's within a tolerance - right, about 10cm plus or minus the 92cm? Wrong again. For a £200m retailer we found a 62cm difference between the biggest and smallest size 12 tops. Imagine buying a 4 seater car, and sometimes there's one seat, and sometimes 7. It just doesn't happen in any other industry. 

 

5. Now - here's the juicy bit. All existing fit businesses, think 'silly consumers not knowing their size', they use ML to guess your body measurements. They have NO IDEA if this size 12 top is 92cm, 104cm or 120cm. They will only be able to tell you to order a size 12. Meaning - they don't work. No brand we've spoken to has found them to have an impact on reducing returns. 

 

6. Plus - in a few years lidar will work and consumers will know their body measurements (you won't scan every time you shop, it'll be within the apple/google apps and you'll do it every so often and come back to the measurements when shopping)

 

7. Then all those measurement apps will be irrelevant. The only thing that will reduce your returns, is if your garments are fit in the most commercially successful way (what's the most commercially successful fit relative to your target audience (age, location, demographics) & body measurement chart, subject to trends (eg perfect jean now vs 5 years ago) layer intention and fabric material science - stretch & weight. You see why we need a Savile Row tailor + an AI scientist to figure this out?)

8. We are the only tool fixing fit pre-production. Working with us means only making commercially successful fits. Fit is the leading cause of returns, returns are the leading cause of lost profits. Therefore fit accuracy is what will separate the brands who die and thrive in the next decade of retail. 

 

9. Not to mention all the interesting fashion 3.0 opportunities there are around fit. Use AI to generate a design, then someone still guesses how you want it to fit...? On demand production only works if you can guarantee fit success / not have excessive returns. 




 

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