Revolutionising Fashion Through AI And Immersive Tech: Dinis Guarda Interviews Anton Jefcoate, CTO Of Lyst

Revolutionising Fashion Through AI And Immersive Tech: Dinis Guarda Interviews Anton Jefcoate, CTO Of Lyst

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By Pallavi Singal

Anton Jefcoate is the Chief Technology Officer at Lyst, a global fashion shopping app, one of the most-downloaded premium fashion shopping apps on iOS globally. Featuring in the latest episode of the Dinis Guarda Youtube podcast, he highlights the changing face of fashion, with the integration of emerging technology, including Artificial Intelligence and immersive tech. The podcast is powered by openbusinesscouncil.org and citiesabc.com.

Revolutionising Fashion Through AI And Immersive Tech: Dinis Guarda Interviews Anton Jefcoate, CTO Of Lyst

Highlighting the key achievements, the CTO, Anton, tells Dinis that Lyst is the world’s leading fashion brand store, with a catalogue of around 17,000 brands and over 4 million fashion products. With about 10 million in-shop products and about 30,000 designers, the platform reaches to across 1,000 retailers.

We are a 100% platform approach. We don’t have an inventory. We only connect partners with customers. For our partners, it’s really about leveraging our technology, position, and scale to connect with their audience.

For our customers, it’s really about giving them well insights, to be able to make informed decisions”, said Anton.

How Lyst leverages its membership model for the evolution of fashion industry

Lyst is a shopping app and fashion technology platform that provides customers with targeted and personalised shopping experiences using its proprietary data engine.

Fashion is a really hard space, because there is no standard, no one-size-fits-all, no API definition, or any standard structure for sharing fashion catalogues. We have a decade long history of building these connections, integrations, and relationships.

It’s really about offering the choice to the customer and leveraging the catalogue to allow our customers to make informed choices about the type of fashion that they want to purchase”, Anton told Dinis.

Explaining about the idea behind the Lyst strategy, he said:

Our technology for business is more like a multi-sided  marketplace. It’s like a customer front end which is ultimately collecting that catalogue and that database up and listening to the way data is moving and presenting that in ways that is helpful for our customers.”

He also highlighted about the ongoing study and research of the platform for ethical and ecologically sustainable offerings.

Fashtech with AI: Enhancing the personalised experience of customers

FashTech represents a dynamic synergy that shapes the future of the industry. From smart fabrics and wearable tech to augmented reality (AR) in retail, FashTech transcends traditional boundaries, offering a seamless integration of aesthetics and functionality.

AI-driven algorithms analyse the vast datasets, enabling brands to understand individual preferences, style choices, and sizing nuances with remarkable precision. During the interview, Anton told Dinis that as technology becomes more and more robust, the concept of virtual fitting rooms might revolutionise the fashion industry.

I’m not quite sure that virtual fitting room type technology is really fully there yet”, he adds.

He believes that from personalised recommendations to virtual try-ons, AI enhances the overall shopping experience, offering customers curated selections tailored to their unique tastes. This not only streamlines the decision-making process but also fosters a deeper connection between consumers and brands. “I think there is a lot to be still done in this space”, he tells Dinis.

He also explained that the Lyst Index tracks the popularity of fashion brands and products based on online searches and sales data.

Lyst has a long history of Machine Learning”, he says. “We have applied  a lot of Machine Learning across the products since 2013 to solve the problem of such a large catalogue.

We have also invested quite a bit of time and money to use an open source AI based on Large Language Models to interpret fashion attributes and build a view from these for better customer interaction and personalisation experience.”

He also highlighted the use of visual recognition and collaborative filtering techniques being used by the platform for better customer experience.

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