While most fabric merchants equate digitalization with opening an online store, one showroom on Dongsheng Road in Shaoxing has already applied AI tools to the sampling stage. Ge Ling Fashion's owner Hu Jiabin revealed that the team is testing an AI mini-program to upload fabric materials and generate trouser samples, compressing a sampling process that once took days to nearly instant. This small step signals a shift from experience-driven to tool-driven development in fabric design.
Background
Ge Ling Fashion has operated on Dongsheng Road for three years and has partnered with the Global Textile Network platform for nearly two years. Dongsheng Road, a traditional fabric distribution hub in Shaoxing, hosts thousands of merchants in fierce competition. In this context, merchants' adoption of digital tools is polarized: some still rely on offline relationships, while others begin to experiment with AI-assisted design. Ge Ling belongs to the latter, focusing its AI application on trouser sampling—a low-risk, high-frequency trial.
Hu Jiabin's attitude toward AI is not blind following; he treats it as a test subject, continuously evaluating its efficiency in style development and sampling. This pragmatic approach aligns with a 'small steps, fast iteration' digital strategy for SMEs. Notably, AI tools do not replace designers; they handle repetitive pattern generation, freeing designers to focus on creativity and fabric details.
Industry Impact
AI sampling tools are changing the response speed of the fabric supply chain. In the traditional process, after a buyer raises a requirement, the fabric merchant contacts a factory for sampling, which takes at least a week or up to half a month. AI-generated trouser samples can serve as initial references, helping buyers quickly confirm style directions before physical sampling, thereby reducing ineffective samples. For small and medium fabric merchants, this means they can serve more potential customers at lower cost, especially new customers with a trial-order mindset.
Customer segmentation is equally noteworthy. Hu Jiabin divides customers into old and new: old customers maintain communication via WeChat, forming stable contacts; new customers often have a trial mentality, requiring faster response and more flexible minimum order quantities. This segmentation strategy is not new, but with digital tools, maintenance costs for old customers are further reduced, and the conversion path for new customers is shortened. Meanwhile, customer needs are divided into cost-sensitive and quality-oriented types; merchants must balance price and quality, or risk losing one market segment.
Multi-category layout provides another safeguard. Ge Ling Fashion mainly produces knitted jacquard, brushed, rib, and woven fabrics, covering long-sleeve shirts, autumn jackets, denim-look, and gender-neutral shirts. The neutral style breaks gender boundaries, broadens application scenarios, and reduces reliance on a single category. This 'multi-category plus neutral style' strategy enables merchants to flexibly respond to seasonal fluctuations and trend changes, avoiding passivity from betting on one hit product.