One of fashion’s biggest challenges is also its most basic: fit. The lack of standardized sizing across the industry leads to ill-fitting clothes and high return rates, which harm consumer trust, brand finances, and the environment. A recent Vogue Business survey highlights the scale of the issue, coming at a time when brands are already dealing with a wider slowdown in luxury sales. The survey found that poor fit (43%) and inconsistent sizing (36%) are major reasons consumers avoid buying from certain brands or retailers, and ill fit is the top cause of returns, accounting for 38%.
Few have focused on fashion’s fit problem as much as Phoebe Gormley, who founded the first women’s tailor on London’s Savile Row at age 20. “For ten years, I’ve heard women complain about sizing,” she tells Vogue Business. “No matter their age, budget, or where they shop, they all ask the same thing: why is sizing so bad? It seems like the fashion industry has completely lost touch with garment sizing.”
Gormley believes AI could be the solution. She created Fit Collective, a fit operating system that uses AI to give brands data from customer returns, helping them improve how clothes are made based on size and fabric insights. The startup recently secured £3 million in pre-seed funding from AlbionVC, Superseded, True, and January Ventures, plus a £324,000 UK government grant. Gormley says the funds will mainly go toward hiring engineers to advance the machine learning behind Fit Collective. Since launching in late 2023, the company has gained 10 clients, including Rixo, Boden, Ro & Zo, L’Estrange, and The Sports Edit (part of Marks & Spencer), providing ample data to analyze where fashion sizing fails.
Why Fashion Gets Fit Wrong
Gormley notes that sizing issues affect women more than men. While men’s return rates are around 15%, women’s can reach 40–50%, depending on the price point, according to Fit Collective data. For luxury womenswear, returns are even higher, at about 60%. This gap is largely due to womenswear designs straying further from original size templates than menswear, thanks to more intricate designs and a wider variety of fabrics, from very light to highly stretchy, which distort fit. As brands base new sizes on previous garments, the deviation from original templates grows over time.
“In luxury, the returns problem is worse because customers buy less and think more carefully about each purchase,” Gormley explains. “This means luxury brands have far less returns data than mass-market brands.”
Gormley admits that fundraising for the pre-seed round was tough, as fit tech is often disliked by investors due to the many existing tools that haven’t significantly reduced return rates. Current solutions usually involve AI body scans that require customers to upload photos or “find my size” buttons that ask for height, weight, and typical size. Both methods add friction and demand a lot of input, leading to low adoption—only 3% of consumers use these tools among Fit Collective’s clients.
Beyond the need for customer input, the main challenge for brands is inconsistency in sizing across different items. For example, Gormley analyzed one high street brand’s published measurements and found a 66cm difference inFit Collective’s software dashboard reveals a significant variation in the actual measurements of women’s shirts all labeled as size 12 across 179 different styles it stocks.
Founder Gormley noticed that while many companies were focusing on website tools to help customers find their size, few were addressing the root issue: inconsistent clothing sizes from the start. Her startup aims to use AI to help brands standardize their sizing.
Brands, particularly large retailers, collect vast amounts of data from customer purchases and returns. While sales data often informs future production, the reasons for returns are frequently overlooked. Fit Collective’s software is designed to analyze this neglected data, examining returns, fabric behavior, and sizing inconsistencies. It acts as a “co-pilot” for brands, helping them make smarter sizing decisions to improve products and reduce return rates.
Eighty percent of Fit Collective’s product is a backend software that allows brands to analyze their garments’ performance. The dashboard evaluates each product (SKU) with a red, amber, or green rating based on its commercial success versus how well it fits. This assessment considers the sell-through rate, return rate, the financial loss from returns, and their proportion. The software gathers insights on fit and fabric quality from customer return information and suggests where to apply this data, using additional sources like manufacturer logs. It also predicts how recommended changes could affect return rates and revenue.
For brands using Shopify, the platform integrates easily as a one-click app, connecting via API keys to their other data tools. Linking transaction and returns data is crucial, and additional data like customer reviews can be added for deeper insights. Brands not on Shopify can connect through an API to their data warehouse.
As a SaaS company, Fit Collective uses a subscription model. Costs are based on a brand’s revenue and return rates; for example, a womenswear brand with $10 million in revenue might pay around £1,000 per month. The return on investment typically materializes within six to 12 months.
The startup also provides consumer-facing insights on product pages, detailing how a garment fits. Unlike separate “find my size” tools that require extra steps, this information is integrated directly into the product description, reaching all shoppers. Gormley reports that these updates have helped client brands recoup their annual contract costs within just three months.
She acknowledges that influencing production is a slower process, taking six to 12 months for new items to hit shelves. However, during that time, the consumer sizing recommendations on product pages are already active and starting to reduce returns.”Come down,” she says.
As OpenAI’s ChatGPT and Google expand further into online shopping, Gormley explains that the platform’s partnership with Shopify—which integrates checkout within the chatbot—will allow these AI systems to use the startup’s sizing recommendations. This means shoppers can receive more accurate fit advice even when they’re not on a brand’s website.
Beyond this, Fit Collective doesn’t intend to venture into virtual try-on technology. Instead, Gormley believes that major tech firms like Apple could enhance their camera-based measurement apps to help users determine their body measurements. This would likely be framed within a health data context, enabling consumers to update their measurements a few times a year.
“I’m excited about a future where consumers have their measurements stored on their phones and can use ChatGPT or Google shopping to search for items like white jeans,” Gormley says. “Then, with our technology integrated into brand websites, the AI will be able to sift through thousands of results and recommend the 100 pairs that are most likely to fit you.”
She points out that even well-known brands can have inconsistencies in sizing, such as with jeans.
For now, Gormley sees a significant financial and sustainable opportunity for the fashion industry in assisting brands to produce the right products. “If we help retailers create better items, return rates will drop, allowing them to invest more in improving their products,” she explains.
Gormley describes a “negative spiral” that brands are caught in: they produce items quickly and inexpensively, leading to high return rates and financial losses. This cycle leaves them with less capital each year, forcing them to cut costs further on production.
“I want our software to break that cycle and turn it into an upward trend, giving brands the confidence to invest in better quality products ahead of time,” Gormley states. “They’ll do this because they have reliable data showing that the garments will fit customers better and, as a result, won’t be returned at a 60% rate.”
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– How can fashion brands get AI campaigns right?
Frequently Asked Questions
Of course Here is a list of helpful and concise FAQs about AIs role in ensuring proper clothing fit
Beginner Definition Questions
1 How can AI help with clothing fit in the first place
AI analyzes vast amounts of datalike body measurements fabric properties and customer feedbackto predict how a garment will fit different body types and suggest improvements to the design and sizing
2 What does AI for clothing fit actually mean
It means using smart computer algorithms to tackle the ageold problem of inconsistent sizing Instead of relying on generic size charts AI can create personalized size recommendations and help brands design betterfitting clothes from the start
3 Is this just a fancy size chart
No its much more dynamic While a size chart is a static guide AI can learn from how real people with different body shapes actually fit into clothes constantly refining its predictions for accuracy
Benefits How It Helps Shoppers
4 Whats the main benefit for me as a shopper
The biggest benefit is reducing returns Youll be more confident that what you order online will actually fit saving you the hassle of shipping items back
5 Will AI tell me what size to buy
Yes Many online stores now have size recommendation tools You input your height weight and sometimes other details and the AI suggests the best size for you in that specific brand or item
6 Can AI help me find clothes for my specific body shape
Absolutely Advanced AI can identify if you have a pear apple or athletic shape and recommend brands and styles that are known to flatter that silhouette going beyond just basic measurements
Common Problems Limitations
7 Whats the biggest challenge for AI in getting fit right
The biggest challenge is the lack of standardized sizing across the industry A Medium can mean different things at different brands which confuses both shoppers and the AI
8 Do I have to share my body measurements for this to work
For the most accurate results yes While some tools can estimate from height and weight providing specific measurements gives the AI the precise data it needs to make a perfect recommendation
9 Can AI account for personal fit preference
This is an area of active development Some systems are starting to learn if you