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Women’s clothing brand Binks wants to solve fitting issues; gets Y Combinator backing


Finding perfect fit clothes online has been a tremendous challenge for Indian women. Reasons: a majority of apparel brands in India are foreign ones, and they factor in European or US shapes and sizes while tailoring clothes. Moreover, the Indian sizing chart is yet to be standardised.

As a result, women generally get their clothes altered with the help of tailors.

Bengaluru-based Binks wants to change this by providing clothes that fit urban women well. The company, which is a part of the Y Combinator accelerator programme, solves the fitting issues through using computer vision and machine learning.

Users just need to choose a style, share their photographs and answer a set of questions such as height, size measurements and preferred brands that are close to their fitting. Further, a female Binks consultant calls buyers to discuss the customisations.

Binks claims to deliver the product within three days and charges anywhere between Rs 1,300 to Rs 3,000 for its styles. Currently, it offers eight categories, including tops, bottom, kurtis, and jumpsuits, amongst others.

Founded by Aamna Khan and Raj Vardhan, Binks started with an offline store in Bengaluru. It started taking online orders in June this year. According to a TechCrunch report, sales have doubled since June. Importantly, the company claims that 30% of customers placing a second order within the first month with a negligible return rate – less than 1%.

While Binks seems to be confident about making big with its India specific clothing solution for working women, but it’s not the first attempt to do so. In the past, a Delhi-based startup 335th had tried solving fitting issues with its clothing line. However, the company didn’t survive, and eventually got shut down.

Since finding perfect-fit clothes has been a big problem for Indian women, Binks offering seems to have the potential to become a successful business. What do you think?

Image credit: TechCrunch.

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