Summary
Google says its Envisioning Studio worked with designers Jane Wade and Sergio Hudson to create two Google Flow tools for styling runway looks and planning show sets. The tools use digital models and runway simulations to address sampling, lighting, props and budget decisions.
Google’s Envisioning Studio, with support from Google Labs, has worked with fashion designers Jane Wade and Sergio Hudson to create two specialised tools in Google Flow for New York Fashion Week preparation. One tool focuses on styling runway looks digitally; the other simulates runway production decisions such as venue layout, lighting, props and model paths.
Google describes Flow as an AI creative studio. In this collaboration, its engineers worked alongside the designers to shape tools around specific production bottlenecks rather than applying a general-purpose system to the entire design process.
Virtual styling before physical samples
The tool created with Jane Wade, called Styling Suite, lets a design team assemble complete looks on digital models. It covers garments along with hair, makeup, accessories and shoes, allowing the team to change combinations and assess the overall balance of a look virtually.
Google says in-person casting and fittings can take up to three full days for a design team. Styling Suite is intended to help identify missing elements before additional pieces are cut and sewn, reducing the need to make every styling decision through physical samples.
The workflow is not presented as a replacement for the designer. Instead, the digital model acts as a planning surface where a collection’s components can be reviewed together before production moves further.
Simulating a runway within a budget
Sergio Hudson used the second tool, Runway Visualization, to plan a show under a strict studio budget. According to Google, changing lighting or props traditionally required the production crew to create a new 3D rendering for each design revision.
The co-developed tool simulates the runway and allows Hudson to change the venue setup, lighting and props while comparing options that fit the available budget. It also lets him refine the routes models take through the space, helping align the movement of the show with its visual presentation.
This makes the tool useful at an earlier stage of production. Decisions about the set and lighting can be explored digitally before the team commits to physical changes, while model paths can be considered as part of the overall show design rather than as a separate logistical task.
From general AI to workflow-specific tools
The collaboration illustrates a more targeted use of generative AI in a creative industry. Rather than asking an AI system to produce a finished fashion collection or show, the designers and Google engineers built tools for particular decisions that consume time, materials and production resources.
Google says both designers used tools created around their existing processes, and that custom tools can be built in Flow by describing the desired workflow in natural language, without coding experience. That approach could make AI systems more useful to specialists who understand a domain deeply but do not build software themselves.
The announcement is a company-reported account of two collaborations, covering workflow examples rather than a broad industry deployment or quantified savings. Its significance lies in showing how an AI creative system can be configured for concrete design and production tasks while leaving the creative direction with the designers.