Google Builds Custom Flow Tools for Fashion Week Styling and Runway Planning

Google’s Envisioning Studio has worked with designers Jane Wade and Sergio Hudson to create two custom Google Flow tools for New York Fashion Week: one for assembling complete runway looks digitally, and another for planning a show’s set, lighting, props and model paths before production. The project is a practical test of whether a no-code AI studio can reduce the logistical work that surrounds fashion design without taking creative control from the designer.

Google Labs supported the collaboration. Google engineers worked with Wade and Hudson to build specialized workflows in Flow, which Google says lets users create custom design tools by describing a desired workflow in natural language rather than writing code. The announcement is about the two co-developed tools and their Fashion Week use, not a newly disclosed standalone Flow feature.

A digital fitting room before the sample room

Wade’s tool, Styling Suite, placed garments alongside hair, makeup, accessories and shoes on digital models. The aim was to assess each look as a whole before producing physical samples. Google says the tool helped Wade spot missing elements before cutting and sewing additional pieces.

That is a narrower proposition than automating a collection’s design. The tool concentrates on coordination: whether the pieces of a look work together, and what may be absent, while the designer decides what belongs in the collection. Google says in-person casting and fittings can consume up to three full days for a design team, though it did not quantify how much time Styling Suite saved.

A runway plan constrained by a budget

Hudson’s Runway Visualization addressed a different bottleneck: planning a show within a set budget. Google says that changes to lighting or props previously required new 3D renderings, adding cost as a production team revised a plan. The Flow tool simulated the venue, allowing Hudson to change its setup and try lighting and prop options intended to fit the budget.

It also let Hudson refine model walking paths. That combines several decisions normally handled across design and production into a single visual planning exercise: how the space looks, how models move through it and whether proposed changes remain compatible with the budget. Google says the results of both collaborations were visible at New York Fashion Week.

A product experiment, not proof of a broader payoff

The collaboration illustrates an appealing path for generative AI in creative work: use it to make choices earlier, when a revised outfit, prop or lighting scheme is still digital. But the evidence so far comes from Google’s account of two partnerships. The company has not published measurements of materials saved, production costs avoided, output quality, or whether the tools transfer cleanly to other designers and show formats.

Google’s next challenge is not simply making Flow capable of producing more images or video. It is showing that people without engineers beside them can turn a plain-language description of their own workflow into something dependable enough for a high-pressure production schedule. This Fashion Week project provides a concrete example of the target: AI tools that serve a designer’s existing process rather than dictate a new one.