iMuse.AI: Virtual Apparel Development and 'Superhero' Designers

In 2026, the AI industry may hit a turning point, moving from an era of concepts and narratives to a stage of practical, market-tested applications. On January 10, at the AGI-Next summit, Tsinghua University professor and (Zhipu) founder (Tang Jie) offered a key conclusion: "Since DeepSeek arrived, competition within the Chat paradigm is largely over, and the next step is moving to real work." Yao Shunyu also noted that, compared with the ToC market, users in ToB scenarios are more willing to pay for model capabilities, because these AI tools can help business owners work more efficiently.

For a long time, however, ToB software in China was not considered a "sexy" business. Although several ToB companies abroad have reached market caps in the hundreds of billions of dollars, many Chinese ToB companies have fallen short of expectations, and the industry has taken a relatively cautious, even pessimistic, view of China's ToB market. On one hand, Chinese companies have long been reluctant to pay.

Many are used to free tools, and when they can't get something for free, they often switch to cheaper alternatives. On the other hand, the decision chain for enterprise software purchases is complex, and the final deal often depends not only on the product itself but also on business relationships, channel resources, and personal networks. In this environment, vendors lack the incentive to keep investing in product R&D, and the market gradually drifts toward homogeneous competition: price wars and feature bloat become the norm, and competition grows increasingly exhausting.

But iMuse founder (Gu Yingying) believes AI is breaking this old logic. In her view, the AI era will reaffirm the value of "product is king" and give companies with real product strength reasonable pricing room. "So-called 'product is king' isn't just a slogan; it means continuously investing in R&D to build products that genuinely create value for customers," (Gu Yingying) stresses.

That value must meet two conditions: first, the effect must be visibly apparent; second, the benefit must be clearly quantifiable. With this in mind, (Gu Yingying) and her team built iMuse.AI, a virtual development platform covering the full design and development process.

iMuse.AI, China's first intelligent platform specializing in apparel design, aims to solve several long-standing structural problems in the apparel industry: severe creative homogeneity, brutal price wars, and a mismatch between product development and actual market demand. Within just 6 months of launch, iMuse.

AI signed partnerships with several leading apparel companies, including (Ellassay Group), (Beyond Group), and (JNBY). (Gu Yingying), the founder of iMuse.AI, has more than 10 years of experience in the technology and apparel industries.

She notes that the apparel industry currently faces a widespread dilemma. Some companies choose to follow popular styles, sustaining scale through copying and price competition, but their margins keep shrinking and R&D spending becomes hard to sustain, ultimately creating a vicious cycle. Other companies try to build differentiated brands but often run into high upfront investment, steep trial-and-error costs, and an unacceptable risk of failure.

In 2023, (Gu Yingying) realized that AI could offer a new way out of this dilemma. With low-cost virtual development, companies can run a large number of design experiments without physical production and move market feedback into the development stage, reducing risk and enabling differentiated innovation. This shift changes the development logic of the apparel industry.

To get there, iMuse.AI covers the full range of design scenarios, from finished garment design and fabric and print design to on-model garment presentation and spatial display previews, aiming to connect the entire chain from design inspiration to finished-garment presentation and retail visual merchandising. With these features, designers only need to enter change requests via images and natural language; the AI model can understand the design intent and generate and adjust images, completely eliminating repetitive manual work such as cutting and pasting images in traditional Photoshop.

Efficiency gains this large are bound to drive industry change. Companies can separate "virtual development" from "physical development": before moving to physical prototyping, they first validate the market with virtual designs, making physical development more efficient and reducing waste. AI can deliver realistic presentation, from style design to model styling, without relying on "physical finished garments."

Building on this, some iMuse clients have started holding "virtual review meetings": the marketing team gives feedback based on AI-generated on-body renderings and adjusts designs in real time over online meetings, keeping styles that don't match market demand out of physical development. Some companies even hold "virtual trade fairs" ahead of traditional trade fairs, showing virtual products to franchisees or key clients and simply skipping physical production for styles that don't receive orders. Virtual development not only cuts costs but also significantly shortens the market feedback loop.

(Gu Yingying) said that under the traditional model, the ratio of rejected samples in the apparel industry is typically between 1:2 and 1:3, while virtual development can cut sample waste by more than 60%. More importantly, virtual designs can be changed at any time, shortening the market feedback loop. Design adjustments can be made repeatedly before the sales season begins, avoiding the double loss of time and money that comes from overly long revision cycles and missed listing windows.

In the future, "pre-sales without physical products" may become the norm: brands could first use AI to create hundreds of virtual designs to test the market, then mass-produce only the small number of styles with the best data. Through this kind of "data-driven design," the apparel industry's long-standing inventory risk could be addressed to the greatest extent possible. Compared with what it offers companies, iMuse.

AI's empowerment of designers may be even more direct. In (Gu Yingying)'s view, AI is changing the skill structure of the design profession. In the traditional apparel industry, designers are often strictly confined to a single subcategory such as womenswear, menswear, or childrenswear, and work such as print design or IP co-branding usually requires outside collaboration.

This isn't a preference for division of labor; it's dictated by skill sets: different categories have completely different learning paths, and developing each skill takes a long time. iMuse.AI has, to a certain extent, broken down this boundary.

With AI tools, designers can quickly switch between design fields; techniques that once took years of training are compressed into tool capabilities, and aesthetic judgment begins to translate directly into productivity. This change is already visible inside iMuse. (Gu Yingying) said that nearly all of the company's current client case studies were completed by a young designer who graduated about six months ago, and they span a range of styles and categories.

This designer graduated from (Donghua University), has now returned to the alma mater to teach, and takes part in courses jointly launched by iMuse and (Donghua), sharing how they use AI as their core tool to shape a new way of working. "In the past, for a beginning designer to reach this all-around level of ability, it could have taken more than a decade, or even a lifetime, to cover so many dimensions of design ability," (Gu Yingying) commented. However, (Gu Yingying) also stressed that AI has not erased individual differences; on the contrary, by "leveling skills," it has widened the gap in "thinking."

Skills are a "form" that tools can replace; what really widens the gap is aesthetic judgment, cultural understanding, information restructuring, and value creation, in other words, higher-level abilities. Put differently, AI achieves "skill leveling" but does not bring about "ability averaging." Indeed, as individual capabilities grow, companies' staffing structures will change: among iMuse's client partners, some companies cut roughly 50% of their personnel costs within three months.

But (Gu Yingying) believes this doesn't simply mean "replacing people with AI"; rather, AI to some extent pushes people to explore and rethink new boundaries of value. She notes that once designers are no longer limited by technique and the supply chain achieves "zero trial-and-error cost," the apparel industry could become more personalized and diverse, and the fashion industry could truly unleash an explosion of creativity. Discussing iMuse's main directions for future development, (Gu Yingying) said the first is helping companies accumulate digital assets.

At traditional apparel companies, a popular style is often treated merely as the outcome of repeated reviews, and companies never really capture the reference sources, design path, or the designer's judgment logic and thought process at the time. By systematically preserving every step of the design process, iMuse lets companies make full use of this knowledge in future designs. For the first time, this turns apparel development, which was heavily dependent on personal experience and unstructured, into a structured, measurable, and continuously optimizable systematic process.

The other direction is building new content and commerce scenarios around "dematerialization." iMuse has launched iChuanyi, a content platform without physical products, which tries to integrate design and development, content presentation, and e-commerce pre-sales into a single virtual system so that validation and distribution can happen without moving to physical production, lowering overall trial-and-error costs for the industry. Back to the bigger question: does ToB have a chance in China in the AI era?

(Gu Yingying) answers yes. In her view, it isn't that Chinese companies don't want to pay; rather, they prefer to pay for certainty in the present. In this context, "product is king" will be tested anew: when a product's capabilities show a leap-ahead lead, it can command higher prices even in ToB scenarios and create a virtuous cycle, with higher prices in turn funding continued R&D investment and ultimately building a systemic moat in the real sense.