Lily AI & Bloomberg On AI and Bringing Humanity to Shopping

Lily AI was in the spotlight on Bloomberg Markets: The Close. Discover how the company is scaling up its team to meet incredible retail AI demand.

Co-Founder and CEO Purva Gupta was recently featured on Bloomberg’s The Close. Throughout her segment, Gupta spoke on the unique value Lily AI brings to today’s dynamic retail market. As retail AI technology has been in the media spotlight for a handful of years, nowhere else is it more critical to success than within the retail vertical. 

Here, Gupta explains how Lily AI helps retailers think like their customers, how this drives 9-figure revenue lifts, and where her team is growing to meet demand.

Read on to discover three key takeaways from Lily AI’s feature on Bloomberg.

Lily AI Brings Humanity to Shopping. 

Quite literally, merchants and consumers speak very different languages. Think of it this way—Consumers don’t use retail industry jargon when searching for products. Often though, merchants communicate their offerings with descriptions that reflect their own language, sentiments, and aspirations. This leads to a communication breakdown between these two parties. Products aren’t purchased, much less found by shoppers. 

Lily AI bridges this gap. It enhances shopping experiences by injecting customer-centric language throughout retail ecosystems. 

The ways consumers speak is dynamic, vast, and ever-evolving, far beyond traditional retail vocabulary. With Lily AI, merchants can describe their products in ways that their customers actually connect with, while still running their businesses as internal teams need on the back-end. 

For example, a shopper may want to purchase an outfit exactly like the one in the image below. But in her search for the perfect vacation outfit, she’s likely using language that doesn’t line up the retailer’s product description and details, also known as attributes. This is exactly where the aforementioned communication breakdown comes into play.

retail AI's product attribution for fashion often looks different than customer language

So what could this breakdown look like? Here are some examples of how these two very different parties could describe the exact same product:


Customer Speak

Retailer Speak

  • Spring break matching set
  • Silky spring outfit with tropical pattern
  • Crop top and skirt set for warm weather
  • Lightweight rayon draping v-neck women’s shirt with palm leaf print
  • Lightweight rayon wrapped skirt with belt tie and tropical leaf print

As you can see, both sets of descriptions represent the product . . . But these differences leave many retailers missing out on ways to connect with potential customers.

As omnichannel shopping experiences become more important than ever, retailers must ensure that their products speak to customers across a variety of mediums. Lily’s retail AI injects customer-centric language, such as the terms in the example above, across retail tech stacks to personalize product discovery, deliver more enjoyable shopping experiences, and boost sales.

By injecting customer language across all facets of the shopping experience, Lily AI reflects back to consumers exactly what they’re most interested in, bringing their own humanity into retail experiences. 

In turn, Lily AI’s customers, which include some of the world’s largest retailers, see upwards of 9-figure revenue lift.

Customer Centricity Makes Lily AI Stand Out Above Other Solutions

Lily AI provides retailers with the most robust, structured, and accurate data attributes possible in order to really understand the dynamic and diverse language of the consumer. Powering the Lily AI products are: 

  • A suite of purpose-built retail AI technologies that include computer vision, natural language processing, machine learning, and vertical-specific large language models (LLMs)
  • The largest clean proprietary training data set in the world, and over 8 years of experience fine-tuning our models and attribution output
  • A proprietary 20,000+ and growing attribute product taxonomy that includes both objective as well as subjective, customer-centric vocabulary and terms, such as styles, occasions, and current trends
  • Our team of machine learning engineers and data scientists working alongside fashion, home, beauty merchandising analysts to ensure the most current and robust customer-centric product attribution available.

An example of Lily AI's retail product attribution

These efforts combined have led to the ability to automatically understand shoppers’ intents and unique contexts, resulting in personalized product discovery experiences. They’re rocket fuel for search engines, as well as retailers’ entire tech stacks. Due to the robust data and expertise powering Lily AI, shoppers are seamlessly connected to the exact products they want to purchase.

As Retailers Demand This Technology, Lily AI Is Growing

Retailers are actively integrating AI technology into their processes, as its potential is transforming their industry. The Lily AI team is growing to meet this demand.

Gupta has built an impeccable leadership team that is overseeing the growth of their Product, Technology, and Go-To-Market functions in order to meet the incredible demand Lily AI is seeing. Several current openings are posted on Lily’s website.

Gupta says that as AI is having a global moment, the company is prepared to scale with it. 

“Because of the growth we’re going through, we’re at a spot to get that next-level talent,” says Gupta. “We’re hiring across a lot of different types of roles . . . getting ready for that hockey stick scale that’s coming up for us next year.”

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Fashion model holding a teal purse while posing in a maroon sweater.