iMuse.AI: Virtual Clothing Development and Designer 'Superheroes'

In 2026, the artificial intelligence industry may reach a turning point, transitioning from the era of concepts and narratives to a stage of practical application proven by the market. On January 10 at the AGI-Next summit, Professor Tang Jie from Tsinghua University and founder of Zhipu drew a key conclusion: "After the emergence of DeepSeek, competition in the Chat paradigm has been largely completed, and the next step will be the transition to real work". Yao Shunyun also noted that compared to the ToC market, users in ToB scenarios are more willing to pay for model capabilities, because these AI tools can help business owners improve work efficiency.

However, for a long time, ToB software in China has not been considered a "sexy" business. Despite several foreign ToB companies with market capitalizations in the hundreds of billions of dollars, the development of many Chinese ToB enterprises has not met expectations, and a relatively cautious or even pessimistic attitude toward the Chinese ToB market prevails within the industry. On one hand, the willingness of Chinese enterprises to pay has long been weak.

Many enterprises have become accustomed to using free tools, and if they cannot obtain them for free, they often switch to cheaper alternative solutions. On the other hand, the decision-making chain for corporate software procurement is complex, and the final deal often depends not only on the product itself, but is closely linked to business relationships, channel resources, and contact networks. In this environment, service providers lack motivation for continuous investment in product research and development, and the market gradually moves toward homogeneous competition, price wars and feature expansion become the norm, and competition becomes increasingly exhausting.

But iMuse founder Gu Yingying believes that the emergence of AI disrupts this old logic. In her view, the AI era will re-affirm the value of "product as king" and provide companies that truly possess product strength with reasonable pricing power. "The so-called product as king is not just a slogan, but continuous investment in research and development to create products that truly create value for customers", emphasizes Gu Yingying.

This value must meet two prerequisites: first, the effect must be visually apparent, and second, the benefit can be clearly calculated. Based on this, Gu Yingying and her team developed the iMuse.AI virtual development platform, covering the complete design and development process.

iMuse.AI, China's first intelligent platform specializing in clothing design, seeks to solve several structural problems that have long existed in the fashion industry: serious creative homogeneity, intense price wars, and misalignment between product development and actual market needs. Just six months after launch, iMuse.

AI has established partnerships with several leading clothing manufacturers such as Ellassay Group, Beyond Group, and JNBY. Gu Yingying, founder of iMuse.AI, has more than 10 years of experience in technology and the textile industry.

She notes that currently the fashion industry universally faces a dilemma: some enterprises prefer to follow popular models, maintaining scale through copying and price competition, but profit continues to decline, investments in research and development are difficult to maintain, ultimately leading to a vicious cycle; other enterprises attempt to create differentiated brands, but often encounter problems of high initial investments, large trial-and-error costs, and unacceptable failure risks. In 2023, Gu Yingying realized that the emergence of AI technology could offer a new solution to this dilemma. Through affordable virtual development, enterprises can complete a large number of design experiments without resorting to physical production, and transfer market feedback to the development stage, thereby reducing risks and realizing differentiated innovation.

This change transforms the development logic in the fashion industry. To achieve this goal, iMuse.AI covers a full spectrum of design scenarios, from finished clothing design, fabric and pattern design, to model displays in clothing and spatial exhibition preview, seeking to connect the entire chain from design inspiration to finished clothing display and retail visual effect presentation.

Through the above-mentioned features, designers only need to input change requirements using images and natural language, and the AI model can understand design intentions and complete image creation and adjustment, completely eliminating repetitive manual operations such as cutting and pasting in traditional Photoshop. Such a significant efficiency improvement will inevitably lead to industry changes. Enterprises can separate "virtual development" from "physical development": before proceeding to physical prototyping, first complete market verification through virtual design, thereby improving the efficiency of physical development and reducing waste.

With the help of AI, real presentation from style design to model combinations can be achieved without the aid of "physical finished garments". Based on this, some of iMuse's clients began to use the "virtual review meetings" method - the marketing team presents feedback and adjusts designs in real time through online conferences based on images of upper body effects generated by AI, in order to prevent styles that do not meet market needs from reaching the physical development stage. Some enterprises even conduct "virtual trade fairs" before traditional trade fairs, showcasing virtual goods to franchisees or key customers and directly stopping production of physical goods for styles that do not receive orders.

Virtual development not only reduces costs but also significantly shortens the market feedback cycle. Gu Yingying reported that in the traditional model, the rejection ratio of samples in the fashion industry typically ranges from 1:2 to 1:3, and thanks to virtual development, sample waste can be reduced by more than 60%. More importantly, virtual development can be modified at any time, shortening the market feedback cycle.

Design adjustments can be completed repeatedly before the sales season begins, to avoid double losses of time and costs due to overly lengthy change cycles and missed listing windows. In the future, "pre-sale without physical goods" may become the norm: brands can first use AI to create hundreds of virtual designs to test the market, and then mass-produce only a small number of styles with the best data performance. Through such "data-driven design", long-term inventory risks in the fashion industry can be solved to the greatest possible extent.

Compared to the corporate side, the empowerment that iMuse.AI provides to designers may be more direct. In Gu Yingying's view, AI changes the ability structure of the designer profession.

In the traditional fashion industry, designers are often strictly confined to a single subcategory, such as women's, men's, or children's clothing, and work on pattern design, IP co-branding, and the like typically requires external collaboration. This is not a preference for division of labor, but is determined by the skill system - different categories have completely different learning paths, and developing each skill means lengthy time investment. The emergence of iMuse.

AI to a certain extent has broken this boundary. Through the intervention of AI tools, designers can quickly switch between different design areas, and methods that previously required years of training are compressed into tool capabilities, and aesthetic judgments begin to directly translate into production efficiency. This change has already manifested itself in practice within iMuse.

Gu Yingying stated that currently almost all client examples of the company have been completed by a young designer who graduated about six months ago, and cover various styles and categories. This designer graduated from Donghua University, currently returned to his alma mater for teaching, and participates in courses jointly opened by iMuse and Donghua to share how he uses AI as the primary tool to shape a new mode of production. "If in the past a junior designer needed to reach such a comprehensive level of ability, it might have taken more than ten years or even a lifetime to cover so many dimensions of designer capabilities", commented Gu Yingying.

However, Gu Yingying also emphasized that AI did not erase individual differences, but rather increased the gap in "thinking" through "skill equalization". Skills are "form" that can be replaced by tools, and what truly increases the gap is aesthetic judgment, cultural understanding, information restructuring, and value construction - that is, higher-level capabilities. In other words, AI achieves "skill equalization", but does not bring "averaging of abilities".

Indeed, when individual capabilities increase, the personnel structure of companies does change - among iMuse's partner clients, some companies reduced approximately 50% of staffing costs within three months. But Gu Yingying believes this does not simply mean "replacing people with AI", but rather that AI to a certain extent forces people to explore and contemplate new boundaries of value. She notes that when designers are no longer limited by technique, and the supply chain realizes "zero trial-and-error costs", the future fashion industry may become more personalized and diverse, and the fashion industry may truly open an explosion of creativity.

Speaking about the main directions of iMuse's future development, Gu Yingying stated that first, it is helping enterprises accumulate digital assets. In traditional textile enterprises, popular styles are often viewed only as the result of repeated review, and enterprises do not actually master the source of references, the design path, as well as the logic of judgments and the designer's thought process at that time. By systematically preserving each link in the design process, iMuse enables enterprises to fully leverage this knowledge in future design.

This transforms the clothing development process, which was originally heavily dependent on personal experience and was unstructured, for the first time into a structured, measurable, and sustainably optimizable systematic process. Another direction is building new content and commerce scenes around "dematerialization". iMuse launched the iChuanyi content platform for goods without physical products, attempting to integrate design and development, content demonstration, and e-commerce pre-sale into a single virtual system, to complete verification and distribution without transitioning to physical production, thereby reducing the overall trial-and-error costs in the industry.

Returning to a more macroscopic question: does ToB have opportunities in China in the AI era? Gu Yingying gives an affirmative answer. In her view, Chinese enterprises do not lack the desire to pay, but rather want to pay for current certainty.

In this context, "product as king" will be tested anew: when product capabilities demonstrate leap-ahead leadership, even in ToB scenarios it can maintain higher prices and form a positive cycle - higher prices in turn support continuous investment in research and development, ultimately creating a systemic barrier in the real sense.