When the clock ticks down to New York fashion week, most independent designers find their calendars packed with logistics rather than creativity. Tasks such as coordinating factories, managing vendor contracts, and handling on-site production can swallow the majority of a design team’s day, leaving little room for the actual act of designing garments. To address this bottleneck, Google’s Envisioning Studio partnered with two designers, Jane Wade and Sergio Hudson, and built bespoke applications inside Google Flow its AI-powered creative studio.
The collaboration blended the technical expertise of Google engineers with the hands-on experience of the designers. By speaking natural language prompts, the creators could shape the behavior of their tools without writing a line of code. The result was a pair of purpose-built utilities: an AI styling suite for pre-show look creation and a runway-visualization platform for set and lighting planning. Both prototypes were trialed in the weeks leading up to the 2026 New York Fashion Week, and the outcomes were displayed on the runway.
Jane Wade’s AI styling suite transforms pre-show look planning
Wade, who runs a small studio that handles both design and pattern-making, traditionally relied on hand-drawn sketches and physical fittings to decide how a collection would appear on a model. In the new workflow, the AI styling suite digitizes each garment, allowing the team to experiment with colorways, fabrics, and accessory pairings on virtual mannequins. The system remembers past collections, brand photography, and editorial mood boards, using that data to suggest combinations that stay true to Wade’s aesthetic.
The tool’s most tangible benefit is financial. By visualizing a complete look before any fabric is cut, Wade estimates a saving of roughly $1,000 per style, a figure that quickly adds up across a full season. Additionally, the suite generates model cards—print-ready PDFs that detail each outfit’s components—through an intuitive interface that replaces a multi-step Adobe Illustrator process. This capability lets team members without advanced design software produce accurate production documents, further reducing the need for external assistance.
Balancing digital and physical fittings
Wade stresses that the AI suite augments rather than replaces traditional fittings. After a digital mock-up is approved, the physical model still tries on the garments, ensuring that the tactile qualities of the fabric and the movement of the pieces meet runway standards. The combination of rapid virtual iteration and final physical confirmation keeps the creative vision intact while trimming days off the pre-show timeline.
Sergio Hudson’s runway-visualization tool keeps the set on budget
Hudson’s primary hurdle was staging his show without inflating costs. Conventional practice involves commissioning 3-D renderings for each design change, a procedure that can become prohibitively expensive for an independent label. The runway-visualization tool built in Google Flow recreates the venue’s dimensions, lighting schemes, and prop placements within a single digital environment. By toggling variables such as spotlight angles, backdrop colors, and prop locations, Hudson could instantly see how each alteration impacts the
This sandbox-style approach enabled Hudson to run dozens of spatial configurations before settling on a final design that honored his artistic intent while respecting his budget ceiling. The tool also allowed him to experiment with model pathways, adjusting the choreography of the runway walk to enhance audience engagement without incurring extra construction costs. The result was a cohesive visual narrative that retained the drama of a larger-scale production, yet was assembled with a modest studio budget.
Protecting creativity through simulation
Hudson notes that the AI platform did not diminish the creative spark; rather, it gave his team room to explore ideas that would have been dismissed as too risky under traditional cost-tracking methods. By front-loading the financial implications of each design decision, the simulation acted as a safety net, allowing the designer to push aesthetic boundaries while staying financially accountable.
Implications for the wider fashion community
The success of these pilots signals a shift from experimental AI projects to production-grade tools that sit comfortably within designers’ existing workflows. Both Wade and Hudson retained full creative control, using the AI to offload repetitive, cost-intensive tasks such as iteration, visualization, and documentation. This model—where artificial intelligence serves as a process accelerator rather than a creative author—offers a scalable template for independent brands seeking to compete with larger houses.
Google Flow’s promise of code-free tool creation means that any designer with a clear problem statement can prototype a custom solution. The underlying philosophy, voiced by UX lead Yeawon Choi, is that technology should be co-crafted with creatives, not imposed upon them. As more fashion houses adopt similar collaborations, the industry could see a new era where runway concepts move from sketch to stage in days rather than weeks, and where budget constraints no longer dictate artistic ambition.
