Google worked side by side with designers Jane Wade and Sergio Hudson to create custom tools in Google Flow for New York Fashion Week. These AI tools helped Jane virtually style runway looks, and helped Sergio stage his show within a strict budget. The initiative was led by Envisioning Studio with support from Google Labs. The team set out to streamline creative workflows and remove friction on the path to the runway. Now anyone can build design tools in Google Flow using simple language, with no coding required.
How is Google Flow reshaping runway prep?
Google engineers partnered with Jane Wade and Sergio Hudson to build two specialized tools in Google Flow for their needs. The goal was clear: speed the journey from idea to runway and cut unnecessary iterations. The team designed a virtual styling solution and a budget-grounded show staging tool from the ground up.
How much time does a solo designer really have for design? In reality, most hours go to admin tasks, factory logistics, and vendor coordination. That is why the team focused on simplifying the creative workflow, keeping design at the center instead of routine steps.
Google’s Envisioning Studio, supported by Google Labs, integrated tools directly into designers’ existing ways of working. Google Flow is an AI creative studio where tools can be tailored to specific challenges. This custom approach kept designers in control and respected the unique needs of their shows.
The result: two custom solutions — Styling Suite for Jane Wade and Runway Visualization for Sergio Hudson. The first captured each detail of looks before sampling. The second simulated the venue and validated plans within budget.
Build your own tools in Google Flow using natural language — no code needed.
Virtual styling with Jane Wade: Styling Suite
Together with Jane, the team created Styling Suite — a Google Flow tool that mapped every facet of runway looks. It let her curate hair, makeup, accessories, shoes, and garments on digital models, balance head-to-toe looks, and spot what was missing before producing physical samples.
Why spend up to three full days on in-person casting and fittings if key choices can be made earlier? Those stages often take up to three days for a design team. By shifting work into a virtual space, Styling Suite focused attention on the substance of a look, not on logistics.
The tool let Jane quickly play with styling options, combine elements, and find balance before cutting and sewing. She could swap styling details on the fly and immediately see how they shaped the complete outfit on a model.
When an element was missing, the tool helped flag the gap before starting additional pieces. That reduced early uncertainty and supported a more coherent collection long before the physical production phase.
Runway visualization with Sergio Hudson: grounding design in a real budget
Sergio’s main challenge was staging his show without breaking the budget. In the past, every change to lighting or props added costs, since a new 3D rendering was needed for each revision. The Runway Visualization tool in Google Flow removed back-and-forth by simulating the runway and aligning the vision with real financial limits.
How do you keep creative scale and still stay on budget? Runway Visualization let him tune the venue layout, experiment with lighting and props, and pick options that fit the budget. It moved key production decisions into a safe, controlled environment.
Instead of long rounds of revisions with the production crew, the tool modeled the space and showed how changes affected the whole setup. Sergio could test several staging scenarios and element swaps before moving forward.
He also refined the paths models would walk to harmonize the show and the viewer experience. Exploring routes in simulation helped align the concept and audience’s journey before the real production.
From pilots to practice: what’s next for fashion AI?
The results of this collaboration were visible on New York Fashion Week runways. There is still room for more innovation. Many fashion AI projects remain stuck in theoretical testing, yet tools can smooth production when co-developed with designers and tuned to real processes.
How do you move innovation from pilot to daily practice? Co-develop tools that fit existing workflows and keep designers in the driver’s seat. This approach lowers resistance to change and preserves familiar steps without drastic shifts.
Ask an independent fashion designer about their day, and “designing clothes” rarely comes first. Admin tasks, vendor coordination, and production details dominate. That is where co-developed AI tools remove friction and return focus to creativity.
Most importantly, you can build bespoke tools in Google Flow using natural language, with no coding experience. That opens doors to faster decisions, clear simulations, and confident production control within real constraints.
AI works when tools are co-developed with designers and embedded into their processes.
Based on Google.
