Introduction
With breakthroughs in machine learning, it’s common to witness companies using ML algorithm-based solutions to do fashion trend forecasting, spotting winning products, forecasting demand for new products, inventory optimization across the value chain, etc. Adding to this list, companies have also started using algorithm-generated, ethnically-diversified digital models to test and validate the look of a certain product. This approach not only promotes inclusivity but also helps in unparalleled speed to market a product at a fraction of the cost of regular studio model pictures. Furthermore, it holds the potential to help brands and retailers across the value chain, ranging from design, buying, merchandising, and allocation, to make better-informed decisions, eliminate guesswork, and reduce waste with the help of AI-powered startups.
In this blog post, we are going to take a look at five AI-powered startups which are doing a fabulous job in the given realm.
Now, let’s dive in!
1. Stylumia
Source: Stylumia
Stylumia, the AI-led fashion intelligence start-up founded by former Myntra COO, was also included by Gartner in the Market Guide (June 2022) for Retail Assortment Optimization Applications in Merchandizing, analyzes and derives actionable intelligence from unstructured data sources by using advances in NLP and Computer Vision.
Stylumia has the potential to change the face of the fashion industry by optimizing inventory levels for retailers around the world. It provides AI-driven solutions that address critical questions for any fashion or lifestyle brand: Which trends should be adopted? How much and what should be produced? When and where should the product be sold?
Source: Stylumia
It offers several solutions like Stylumia Customer Intelligence, Stylumia Fashion Intelligence, Stylumia Apollo, Stylumia Store.Y, and Stylumia ImagGenie. The proposed solutions derive their intelligence from photos, user behaviour data, social media textual descriptions, and implicit signals gathered from retail sites. As a result, fashion and lifestyle specialists worldwide can make informed decisions about current course corrections and future directions and hence also help reduce waste.
Stylumia Customer Intelligence Solution: It gathers a wealth of data from the web, global designers, brands, retailers, and runways, eliminates the noise from the data, and reveals the actual consumer demand. This helps with the instant validation of products and trends.
Stylumia Fashion Intelligence Solution: This solution is driven by computer vision, and it provides a simple, intuitive, visual business intelligence to understand the in-season and post-season success rate of the created products and take immediate action.
Stylumia Apollo: This solution is a customized prediction engine that helps in predicting demand for a new product. It is inspired by human visual perception through an ensemble machine learning model. It enables leveraging images in conjunction with textual attributes powered by global fashion intelligence and transaction data.
Stylumia Store.Y: This solution maps the channel/store with the inventory of available products and personalizes the distribution using a taste match engine.
Stylumia ImaGenie: It is a predictive design tool that processes and analyzes millions of images and textual attributes of fashion styles from runways, fashion websites, and e-commerce to generate winning designs.
Stylumia serves 100+ leading brands and retailers, and given the promising solutions it provides, this number will only grow.
Read here| https://wwd.com/business-news/business-features/stylumia-trend-dynamics-growth-of-demand-sensing-retail-1234866080/
2. ZMO.ai
Source: ZMO.ai
Traditionally, the whole cycle of garment manufacturing usually takes two to three months – from design, fabric selection, pattern making, and modeling to hitting the shelves. But, considering the trend’s rate changes, the process needs to be shortened.
ZMO.ai founded by a group of Chinese entrepreneurs, can create unique-looking fashion models ten times faster and at a fraction of the cost of regular studio model pictures. It reduces the hassle of booking photographers, models, studios, and post-production. Plus, it aids in unparalleled speed to market. One can test different looks by simply putting the product’s image on the model, getting the preferred on-model photos, and starting selling as soon as your first sample is ready. One can also customize the style of the models from the ample options available at the fingertips.
Caters to the needs of fashion e-commerce enterprises struggling to find and afford models due to their increasing number of stock-keeping units (SKUs), or styles, as trends or customer tastes change.
ZMO.ai has developed software that leverages generative adversarial network (GAN) to create virtual full-body models by providing simple parameters like face, height, body shape, skin colour, and pose as input. ZMO also intends to leverage the GPT-3 model, which uses big data and deep learning to imitate the natural language patterns of humans. As spooky as it may sound, the feature would enable e-commerce companies to churn out TikTok videos quickly and cheaply for promoting products.
This startup aids in getting a realistic view of the products on an ethnically diversified assortment of digital models.
The AI-powered startup has 30 “medium and large-sized” customers, including Tencent-backed Chicv, and over 100 “small and medium” customers, such as dropshipping vendors.
Read here| https://techcrunch.com/2022/05/17/zmo-ai-generate-human-8-million/
3. Lalaland
Source: Lalaland
Lalaland, a dutch startup, provides a self-service platform where one can create their own hyper-realistic AI-driven virtual fashion avatars in a matter of minutes. Moreover, size, body type, body shape, identity, hairstyle, and even the level of happiness of the virtual fashion model can be customized based on the need. This solution indeed helps in reducing the time to market.
It also aspires to promote inclusivity by helping e-commerce enterprises capture humankind’s incredible diversity by generating artificial, full-body fashion models. With digital avatars, e-commerce companies can showcase and validate 3D garment designs on avatars that match buyers’ demographic areas before a physical sample is produced. Moreover, Lalaland not only promises to help the retailers but also the consumers while shopping online. One can choose a model who matches their size, age, and skin color and then views how clothing looks on that model.
Adopting this AI-based technology is a practical benefit; if clients see how garments look on a model that looks like them, they can make a better decision. But, this may further lead to a long-term, more important psychological effect. Users who see relatable models are likely to feel heard, recognized, and respected, resulting in increased confidence. While it is too early to conclude if such an effect will be accomplished with this technology, there is no reason to doubt it.
Lalaland’s technology contributes to a higher first-time right hit rate and reduces the need to order several sizes. While the long-term effect has yet to be shown, and return rates vary widely between types of garments, Lalaland’s experience with its first clients has demonstrated that return rates in women’s wear can reduce from around 40% to about 30%.
The decrease in returns means that fewer garments are discarded. It also indicates fewer shipments—both to the customer and the retailer—saving on carbon emissions from transportation and positively influencing profitability.
Read here| https://www.forbes.com/sites/jeroenkraaijenbrink/2021/03/18/how-this-ai-startup-plans-to-shake-up-the-online-fashion-industry/?sh=6db8b6563eb7
4. TRUE FIT
Source: True Fit
True Fit is a Massachusetts-based AI-powered startup that is a data-driven personalization platform for footwear and apparel retailers that decodes personal style, fit, and size for every consumer, every shoe, and clothing. It helps apparel shoppers by comparing fit data from brands with the closest products and provides personalized size and fit guidance.
This startup leverages the manufacturing machine learning algorithms in conjunction with design data gathered from thousands of leading apparel and footwear brands, anonymous consumer order data from top retailers, personal preference data of registered True Fit users, and anonymous shoppers to determine what clothing and shoe shoppers keep and return after buying.
Furthermore, customers are asked to input products from any brand which they feel fits them perfectly; True Fit’s technology then uses AI to scour its data and inform customers browsing online how closely products match their perfect fit. This can help drive conversion, reducing returns, waste, and other overheads.
Read here| https://www.chargedretail.co.uk/2022/05/04/the-very-group-partners-with-true-fit-for-personalised-ai-size-and-fit-guidance/
5. Stitch Fix
Source: StitchFix
Stitch Fix is an online personal stylist retailer that leverages AI algorithms and human stylists working in conjunction to make recommendations to clients about clothing, shoes, or accessories. They use various AI techniques, but statistical machine learning is the core method. Machine learning models are leveraged to provide insights about the company’s operations, including styling, marketing, supply chain, and customer support.
The customer fills a style profile, and a personal stylist hand picks pieces to meet their interests, needs, and budget—and mails them directly to their door. Each box includes five pieces of apparel, shoes, and accessories for you to try on at home.
This startup aims to present clients with a “Fix” —a mailed box of five personalized clothes choices—that are a close fit to their style, size, and price preferences.
Read here| https://www.forbes.com/sites/tomdavenport/2021/03/12/the-future-of-work-now-ai-assisted-clothing-stylists-at-stitch-fix/?sh=206b1c6f3590
Conclusion
To summarize, in this post, we explored about following AI-powered startups:
1. Stylumia: AI-led fashion intelligence start-up founded by former Myntra COO offers numerous solutions like Stylumia Customer Intelligence, Stylumia Fashion Intelligence, Stylumia Apollo, Stylumia Store.Y, and Stylumia ImagGenie. These proposed solutions derive their intelligence from photos, user behaviour data, social media textual descriptions, and implicit signals gathered from retail sites and aid in making educated decisions about current course corrections and future directions.
2. ZMO.ai: Founded by a group of Chinese entrepreneurs can create unique-looking fashion models (using GANs) 10 times faster and at a fraction of the cost of standard studio model pictures. This startup also plans to leverage GPT-3 in the future.
3. Lalaland.ai: A dutch startup that provides a self-service platform where one can create their own hyper-realistic AI-driven virtual fashion avatars in a matter of minutes. Moreover, size, body type, body shape, identity, hairstyle, and even the level of happiness of the virtual fashion model can be customized.
4. True Fit: A Massachusetts-based startup that is a data-driven personalization platform for footwear and apparel retailers. This startup leverages the manufacturing machine learning algorithms in conjunction with design data accumulated from thousands of leading apparel and footwear brands, anonymous consumer order data from top retailers, personal preference data of registered True Fit users, and anonymous shoppers to determine what clothing and shoe shoppers keep and return after buying.
5. Stitch Fix: An online personal stylist retailer that leverages AI algorithms and human stylists’ knowledge to recommend clothing, shoes, or accessories to clients. It uses various AI techniques, but statistical machine learning is the core method technique, and statistical machine learning is the core method used. Furthermore, ML models are leveraged to provide insights into a company’s operations, including styling, marketing, supply chain, and customer support.
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I'm a Researcher who works primarily on various Acoustic DL, NLP, and RL tasks. Here, my writing predominantly revolves around topics related to Acoustic DL, NLP, and RL, as well as new emerging technologies. In addition to all of this, I also contribute to open-source projects @Hugging Face.
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