Interview Questions for Purva Gupta, Co-Founder and CEO of Lily AI
In the ever-evolving world of fashion retail, Purva Gupta, co-founder and CEO of Lily AI, has identified a significant issue - the failure of many American retailers to comprehend the intricate details shoppers use to describe items. To address this problem, Lily AI was founded in 2015 with a mission to build a shopping experience that understands the emotional context of shoppers.
The platform, now an integral part of many retailers' existing stacks, injects the language of the customer, thereby bridging the gap between retailers and shoppers. This customer-centric approach is revolutionizing the way retailers operate, helping them to order earlier, make predictions earlier, and ensure that their decisions are more precise.
Without a core layer of customer language, retailers continue to make inaccurate guesses about products and inventory, struggling to break through the average 2.5 percent conversion rate from online search. However, with the help of Lily AI, retailers can improve online product searching by understanding the language of the customer and building the right product taxonomy.
This improved product intelligence not only enhances on-site search conversion but also aids in personalized product discovery and demand forecasting. Retail giants like The Gap, Bloomingdale's, Macy's, and thredUP have benefited from Lily AI's services, experiencing improvements in their online shopping experiences.
The future of AI in retail involves optimizing both in-store and online shopping experiences. AI will create highly personalized and intuitive product search experiences, suggesting products with greater accuracy, reducing choice overload, and increasing customer engagement and conversion rates.
AI-driven demand forecasting will become more precise by analysing a broader range of variables in real time, such as market trends, seasonality, customer behaviour, and supply chain fluctuations. This will enable retailers to optimize inventory management, reduce waste, and prevent stockouts, ensuring product availability aligns closely with demand.
AI will also transform physical retail via technologies like computer vision and agentic AI systems. Computer vision can track foot traffic heat maps, optimize store layouts, monitor shelf stock levels automatically, and personalize offers using in-store smart displays. Agentic AI will autonomously manage staffing, restocking, and marketing plans dynamically based on real-time data.
Additional benefits include voice commerce via AI assistants, seamless omnichannel integration, and improved operational efficiencies from automation and logistics optimization. The future of retail, driven by AI, promises to deepen personalization, streamline operations, and innovate the in-store and online retail experience, driving higher engagement and efficiency across the retail value chain by 2030.
Lily AI has extended its concept into an enterprise-grade, AI-powered product attributes platform. The platform plays a role in forecasting by providing better and more granular product attribution data. Having better product attribution data ensures the right size, color, and style mix of items will still be ordered ahead of longer lead times.
Gathering proxy products through Lily AI-powered computer vision aids in forecasting demand for new product lines. Lily AI also assists retailers in transitioning from wholesale pre-orders to a leaner, demand-led, made-to-order model. Retailers risk not selling products at full margins due to inaccurate forecasting, leading to discounts or underbuyings. However, Lily AI's AI-powered platform improves forecasting accuracy, reducing timelines, and increasing sales at full margins.
- The integration of Lily AI's platform into retailers' existing stacks signifies a significant step in the automation of retail, as it injects the language of customers, enhancing product intelligence and bridging the gap between retailers and shoppers.
- In the future, AI will not only optimize online shopping experiences but also transform physical retail by employing technologies like computer vision and agentic AI systems, which will automate tasks such as store layout optimization, in-store smart displays, and dynamic staffing plans.
- The AI-powered platform developed by Lily AI plays a crucial role in demand forecasting by providing better and more granular product attribution data, ensuring that retailers order the right mix of sizes, colors, and styles, thereby reducing the risks of discounts or underbuyings.
