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How a Chinese AI Start-up Helps Nike and Gap Predict Fashion Trends Faster and Boost Sales
Hangzhou Zhiyi Technology uses AI to help Nike, Gap, and Urban Revivo predict fashion trends by analyzing global style and sales data. This boosts sales and improves inventory accuracy.
How a Chinese AI Start-up Helps Nike, Gap, Urban Revivo Predict Fashion Trends
Hangzhou Zhiyi Technology, a Chinese AI start-up founded in 2018, applies artificial intelligence to apparel design and production. Its main focus is compiling style and sales data from global shopping and social media platforms to provide brands with data-driven insights on fashion trends.
Based in Hangzhou, Zhejiang province—a fast-growing technology hub—Zhiyi Tech supports major clients like Nike, Gap, and Urban Revivo in predicting what styles will sell next. This helps streamline operations and reduce guesswork in product development and inventory management.
Boosting Sales and Efficiency for Urban Revivo
Zhiyi Tech’s CEO Zheng Zeyu shared that their partnership with Chinese fast-fashion retailer Urban Revivo, which began in 2020, led to a 5 percentage point increase in the retailer’s chances of creating bestselling items within three years. Urban Revivo produces over 100,000 designs annually, making efficient trend prediction critical to its success.
The AI-driven insights also improved Urban Revivo’s sell-through rate—from 60% to 80% over three years. This metric measures the proportion of inventory sold compared to stock received, reflecting better alignment between supply and consumer demand.
Growing AI Adoption in China’s Fashion Industry
Zhiyi Tech exemplifies how AI adoption is expanding across Chinese enterprises, particularly in retail and fashion operations. Supported by government policies and mandates, China’s domestic AI market is projected to reach 5.6 trillion yuan (about US$780 billion) by 2030, according to China International Capital Corp.
For operations professionals, integrating AI-driven data analysis like Zhiyi Tech’s offers clear benefits: improved demand forecasting, reduced overstock, and faster product turnaround. These factors contribute to smoother supply chains and more responsive inventory management.
- Data-driven trend prediction helps reduce costly inventory errors.
- AI analysis accelerates design-to-market cycles.
- Higher sell-through rates translate into better cash flow and profitability.
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