
How fashion startups get accepted into tech accelerators - pshaw
https://www.voguebusiness.com/technology/fashion-startups-tech-accelerators-y-combinator-dream-assembly
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bobowzki
I'm surprised I haven't seen more fashion/clothing startups (maybe I have just
missed them?).

Specifically I see great potential in just-in-time production of garments
using 3D scanning, motion tracking, "curve" fitting algorithms and automatic
manufacturing. I would LOVE to be able to walk into a store and buy a pair of
jeans that actually had a great fit. Would definitely pay premium. Tailors
doesn't scale well.

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danpalmer
Unfortunately this is probably further than many in tech realise. The tech is
getting better but the manufacturing tolerances on most clothing is far too
far off.

Typical manufacturing tolerances on "high street" clothing (not high end) will
be about half a size. On budget clothing it could be as much as a whole size
out between batches.

It's not until you get to much higher end than most people are willing to pay
that you get to the good enough tolerances. a £50 shirt (which is pretty high
end for most people) will still be out by a lot. A £150 shirt might have the
potential to be close enough on the tolerances, but at that price point sizing
isn't the factor that people care about, it's luxury brand names.

I'd be happy to pay a premium for good fits, but the premium I'd be willing to
pay (and that I bet many/most would be willing to pay) is in the 10-30% range,
not the 200-300% range that it would likely need to be.

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koolba
One free trick to getting a better fit is to try on more than one of the
“right” size for the same article of clothing.

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trevyn
Free business idea: Don’t worry about loose manufacturing tolerances, but make
a way of precisely measuring each individual finished garment, then match the
specific garment to the specific customer.

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keenmaster
Personal AR devices with 3D cameras will make it easy for someone to get
precise measurements and re-scan after any weight change. On the manufacturing
side, I think we need to combine cameras with clothing material data and
robots that stretch the clothing in various directions to ascertain actual
size. The output measurements could be matched up with the 3D scans of
customers to make accurate size recommendations.

In addition, the manufacturer's measurements could be turned into a 3D model
of the clothing that can be "worn" by a deepfake digital version of the
customer. CGI footage would be generated of Deepfake Customer inhabiting a
curated branded setting, showing how the clothing would fit on the customer
from multiple angles. This wouldn't only be for the customer's viewing. An ML
Aesthete would view Deepfake Customer catwalking in tens of thousands of
permutations of the store's clothing, recommending the combination of
merchandise that would be most attractive, adjusting for the customer's
preferences.

If the customer is still undecided between several articles of clothing,
photos from the deepfake catwalk could be posted on a website like
Photofeeler.com, where human opinions would be rendered in minutes and
compared with the customer's baseline attractiveness to compute an
"attractiveness boost" score. ML Aesthete would be "punished" for differences
between their score and the human scores, until their scores match up and
human opinions are no longer necessary. Alternatively, the customer can post
an Instagram story with a voting mechanism. If anyone with ML and/or robotics
experience would like to work with me (I'm currently non-technical but have
business acumen) on the whole system described above, even if perhaps as an
aspirational side project, please reply below.

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finnthehuman
What’s to explain? Consumer tech is fashion products, not tools.

Or have you not noticed whenever you first start dating someone new your
choices in app brands elicits the same kind of responses as your choice in
clothing?

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natrik
Key takeaways:

Fashion companies are looking to invest in “enabling” startups that help them
implement new business models or technologies.

Venture capitalists and incubator executives like founders who don’t gloss
over the challenges they face.

Founders should have concrete data on the competition and potential consumers,
as well as preliminary results of having tested their idea.

