Cofounder Purva Gupta discusses how Lily AI leverages machine learning and consumer-centric taxonomies to enhance online shopping experiences, backed by a recent $25 million Series B investment.
Lily AI cofounder Purva Gupta discusses the importance of teaching retail search engines to speak the same language as their customers.When Purva Gupta emigrated to the U.S. from India and did some online shopping here, she found herself surprisingly frustrated. Why was it so hard to find the specific type of dress she was seeking? Thinking it might be an immigrant problem resulting from a language or cultural barrier, she did some hefty local research to “test her hypothesis.
Data scientists and AI engineering experts are also a part of the many humans behind Lily AI’s unique “consumer-oriented” product taxonomies versus pure-play automation, constantly refining models and ensuring the absolute best in data quality, accuracy and relevance. This combination of experts is constantly training and refining the algorithms, resulting in an ML that matures over time, continuously getting ‘smarter’ and ever more accurate with every training input.
But even with great prompts, for search to “find” what a person seeks, we still need the relevant product details and attributes to be properly labeled to power the discovery.Planning and forecasting are prioritized focus areas for many of our clients due to the massive margin increases to be realized from improved pre-season and in-season models.
The underlying technology has been evolving rapidly and getting smarter by the day, and the wave of deep learning excites us for its ability to learn to make connections between input and output and requires less spoon-feeding than earlier ML techniques needed. And now generative AI has pushed ML capabilities from analyzing or classifying existing data to being able to create something entirely new, including text, images, audio, synthetic data and more.
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