Galileo AI

The core of the product. You describe the interface in natural language, and Galileo AI returns high-fidelity screens within roughly 30 to 60 seconds, usually as several variations you can compare side by side. Output quality tracks how specific your prompt is. A request for "a nice login screen" produces something generic, while naming the fields, the layout order, the theme, and the target platform gets you something closer to a real design. So what actually works? Specific prompts. It understands common patterns like dashboards, feeds, and checkout flows, and it handled data-heavy dashboard layouts better than most of its contemporaries.

Screenshots, existing UI captures, and hand-drawn sketches all work as inputs. The tool reads the layout structure, component arrangement, and visual hierarchy from the reference, then rebuilds it with editable elements, often filling in details the sketch left out, like icons, avatars, or chart placeholders. This made it useful for modernizing an aging interface without redrawing it from scratch. Upload quality matters more than people expect: a sharp screenshot with clear separation between elements converts noticeably better than a blurry or heavily compressed one.

This was Galileo AI's strongest differentiator. Generated designs didn't leave the platform as screenshots. They arrived in Figma as editable vectors, text layers, and grouped components that followed the layout logic of the original, so you could select a button and change it rather than tracing over a picture. Designers who tested it repeatedly called out the layer structure as unusually clean. That's the difference between a throwaway mockup and a usable starting file.

Before exporting, you can adjust the global look of a generated design: fonts, accent colors, and corner radius, applied consistently across every screen in the project. That consistency is what stopped the output from looking like a pile of unrelated AI images. It's less granular than editing a full design system, which is exactly why it stays approachable for people who don't do design work.

Every prompt returns several options rather than a single answer. You pick the closest one, or borrow a header from one and a card layout from another. The team behind the tool described this as one of the most valuable things they learned internally. Seeing four directions at once helped people decide faster than iterating on one design through many rounds.

The current Google Stitch version of the technology can pull a design system out of any URL and store your rules in a DESIGN.md file, an agent-readable document you can move between projects and developer tools. It's a meaningful step past one-off screen generation, since it means the tool can keep brand rules consistent instead of rebuilding the same decisions each session. If you need to apply the same visual language across several projects, this is the feature that saves the most time.