While most fabric traders equate digitalization with setting up online stores, a merchant at Dong Sheng Road market in Shaoxing has already introduced AI tools into the sampling process. Hu Jiabin, head of Geling Fashion, revealed that his team is using a mini-program called 'AI Cloth' to upload fabric materials and generate trouser samples, compressing the original multi-day sampling cycle to minutes. This seemingly minor attempt signals a shift in traditional fabric trade from 'experience-driven' to 'tool-driven' operations.
Background
Geling Fashion has operated a physical store at Dong Sheng Road market for three years and partnered with the Global Textile Network platform for nearly two years. This dual-line layout of 'offline storefront plus online platform' is not uncommon in the Keqiao fabric business circle, but what is noteworthy is the regional characteristic of its customer base—mainly concentrated in the two major garment industry belts of Guangzhou and Wuhan. This means its products must simultaneously meet Guangzhou's demand for quick response to fashion trends and Wuhan's sensitivity to cost-effectiveness, forcing the company to strike a balance in development.
Hu Jiabin's customer segmentation strategy is quite representative: old customers rely on WeChat for high-frequency communication, forming stable trust; new customers, mostly with a trial-order mindset, need a lower decision threshold. This distinction is not just sales rhetoric but directly reflects in the positioning of fabric products—some customers prioritize cost, while others value texture and finished quality, being less price-sensitive. Accurately matching these two customer groups is key to securing stable orders.
Industry Impact
Fabric development is shifting from 'closed-door creation' to 'multi-channel inspiration capture.' Geling Fashion's R&D system covers fashion shows, Guangzhou designer websites, and industry member content, extracting details like embroidered floral and decorative elements from show images to incorporate into its own designs. This 'reference plus improvement' model, combined with the factory's own R&D capabilities, enables rapid product iteration. Notably, its raw material ratios are not fixed but flexibly adjusted according to market trends, ensuring fabrics meet current garment market demands.
In terms of product structure, Geling Fashion mainly deals in knitted jacquard, brushed, rib, and woven fashion fabrics, with applications covering long-sleeved thin quilted shirts, autumn jackets, denim-look apparel, and gender-neutral shirts. The gender-neutral fabric layout is particularly noteworthy—it breaks traditional gender boundaries, expanding the application scenarios of the same fabric, which means wider selection space and lower inventory risk for buyers. This strategy is especially effective in the spring-autumn garment track, where styles are diverse and seasons change quickly, and a multi-category layout can effectively hedge against the risk of single-product sluggishness.
The application of AI tools in sampling, though still in the testing phase, is significant. Traditional sampling requires repeated adjustments of yarn, structure, density, and other parameters, which is time-consuming and costly. AI assistance can quickly generate sample effects, allowing salespeople to instantly display visual solutions when customers inquire, greatly improving communication efficiency. More importantly, it reduces trial-and-error costs, allowing small and medium merchants to explore more design possibilities with lower investment, thus remaining agile in the competitive fabric market.