Grayle – helps you find shoes that fit

Hacker News

Hey folks, my name is Kyle Moredock (kylemoredock), founder of Grayle (www.grayle.co) based in Portland, OR. Our mission is to establish a community that helps people find shoes that fit, especially when they’re shopping online. I’m a big guy (6’5 ~250lbs) so it’s always been challenging to find clothes and shoes that fit. E-commerce shops, while convenient, make this process even more difficult because you can’t try things on. Like many of you, I would end up buying much more than I needed and returning the rest. This is a big burden on brands/retailers, losing a bunch of money on returns, and wreaks havoc on the environment. It’s also a giant waste of time for the consumer. Grayle is designed to provide shoe fit recommendations via a combination of crowd-sourced fit data and biometric measurements (measurements of your feet based on a photo). That means we can provide you a standard measurement of your foot based on a scan (accurate to 5mm) and translate that into a shoe size. In addition to the scan, we also ask people to provide us with shoe reviews, sharing what size in various brands fit them best. The more info we collect, the better the recommendation gets. We’re working to combine the scan and review data to produce on optimal fit recommendation. Data collection is our biggest hurdle because we need a lot to make this work. The goal is to build out a community that matches you with other individuals who share the same foot measurements, as well as the same shoes in the same sizes. This allows you to discover new brands from the individuals you’re matched with and make confident purchases because you know the size is right. It also solves a huge problem all of us face when we go shop – where do I find reliable, neutral information? Grayle enables you to get that info from the people you trust most – your peers. Looking forward to your feedback! We’re also looking for a CTO if anyone has interest or knows of anyone.

Visit product View on Hacker NewsFeb 22, 2021

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best fitHighest predicted score across all platforms for this description.

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50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

41%41% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

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0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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