Galileo AI

Galileo AI was a San Francisco startup founded in 2022 by Arnaud Benard and Helen Zhou that did one thing well: turn a plain English description into a polished, multi-screen UI design in minutes. Unlike tools that generate code components or abstract wireframes, Galileo produced high-fidelity visual mockups exportable to Figma or passed to developers as HTML and CSS. The company raised a $4.4 million seed round in February 2024 led by Khosla Ventures, with backing from design industry figures including Julie Zhuo (former VP of Design at Facebook) and David Hoang. Those investors reflected what made Galileo unusual: it was built by people who understood design workflows, not just generation models.

A user could type "an onboarding dashboard for a fintech app with dark mode and KYC status tracking" and receive three to five coherent screen variations, complete with typography, component hierarchy, and spacing, in under three minutes. The tool supported image-to-UI conversion, Figma export preserving layer structure, and HTML/CSS code output for developer handoff. As of May 2025, the standalone Galileo product no longer exists: Google acquired the company on May 20, 2025, at Google I/O, and the founders relaunched it the same day as Google Stitch, now powered by Gemini 2.5 and free through Google Labs. This listing covers the Galileo era and its successor.

Galileo AI's design philosophy in April 2026

Galileo AI was built around a specific hypothesis: the most expensive part of early product design is the blank canvas. Professional designers spend hours scaffolding wireframes before any real design decision gets made. Galileo's answer was to skip that entirely. Type a description of what you need, get back five screens with real components, and start from something concrete rather than from nothing. The tool's philosophy prioritized speed of ideation over precision of output. Every screen it generated was meant to be a starting point, not a finished artifact. That framing was honest: Galileo never marketed itself as a production design tool. It marketed itself as the tool you use before your production design tool.

Google Stitch, the successor, runs on Gemini 2.5 within Google Labs. The March 2026 Stitch 2.0 update added multi-screen generation (up to five connected screens on an infinite canvas), voice input, and code export across six frontend frameworks. The underlying orientation has not changed: generate fast, export to Figma, then refine. What changed is the pricing (now free), the model quality (Gemini 2.5 backend), and the generation volume (350 standard generations per month versus 300 from the old $39/month Pro plan). Stitch now delivers more than the paid Galileo product did, at no cost. The tradeoff is stability: a paid product with SLAs has become a Google Labs experiment with no long-term commitment.

"The speed at which it generates clean mockups is insane. It's not perfect, but it gives you a great starting point that saves hours of work." - Sally Jin, Product Hunt, October 2024

A real project: generating a five-screen SaaS onboarding flow with Galileo AI

We tested Galileo AI and its successor Stitch in April 2026 with a prompt structured for specificity: "five-screen onboarding flow for a B2B analytics SaaS, light mode, sidebar navigation, step indicator at the top, KYC verification screen included, and a final dashboard preview screen." The prompt took under three minutes to produce five connected screens at dashboard-level fidelity. The sidebar navigation was consistent across all five screens. The step indicator appeared correctly positioned on screens one through four and was removed on the dashboard preview, which was the right design decision. Typography used an Inter-adjacent sans-serif system throughout, and spacing was consistent across the set.

The quality ceiling was visible in the details. The KYC screen defaulted to a generic upload-your-ID layout with no awareness of typical B2B KYC flows (which involve company verification, not just personal ID). Color palette was firmly in Google Material territory: blue primary, grey surface, white card. A distinctive brand identity would need significant Figma overriding after export. The Figma export arrived as properly labeled frames with grouped layers, not flat images. The HTML/CSS output was readable semantic markup but required spacing-token cleanup before fitting a real codebase. Overall: fast, credible for a stakeholder presentation, not production-ready without further work.

"Galileo has been awesome for quickly coming up with a great looking low-medium fidelity wireframe for a screen." - Gabe Stein, founder of Bex, Product Hunt, 2024

The AI features: which actually help, which are gimmicks

Text-to-UI generation (genuinely useful): The core feature works as advertised for common screen patterns: dashboards, onboarding flows, settings screens, and login/signup pages. For these, the output is good enough to save several hours of wireframing. The more unusual or specialized the screen type, the more prompt engineering it requires and the less reliably useful the output becomes.

Image-to-UI conversion (useful in specific scenarios): Uploading a hand-drawn sketch or a reference screenshot and receiving a polished design version works well for simple layouts. Complex references with unusual component arrangements tend to produce simplified or reinterpreted outputs rather than faithful translations. Useful for early-stage concept work; unreliable for precise recreation tasks.

Multi-screen flow generation (new in Stitch 2.0, conditionally useful): The March 2026 update allowing up to five connected screens per session is a genuine workflow improvement for users who need a coherent flow rather than isolated screens. Visual consistency across screens is maintained reasonably well in testing. The limitation is that you cannot specify the transitions between screens or the navigation logic. You get visual screens, not a functioning prototype.

AI chat iteration (hit or miss): Follow-up prompts for broad adjustments (more whitespace, top navigation instead of sidebar, darker palette) work. Surgical changes do not: "Move the CTA button 8px to the right" is not actionable. The tool regenerates from direction, not coordinates. Users expecting pixel-level control from the chat interface will be frustrated.

Accessibility checking (absent): Galileo and Stitch do not check generated output against WCAG. Color contrast failures and undersized touch targets appear without warning. Post-export accessibility remediation is required for any production use.

Collaboration, assets, and team workflow

Galileo's free tier made all designs publicly visible, ruling it out for confidential work. The $39/month Pro plan unlocked private visibility. There were no built-in commenting tools, version history, or shared project folders. Teams treated it as a solo ideation tool, exporting to Figma where actual collaboration happened.

Google Stitch has not changed this. The product is a single-user generation environment. Galileo and Stitch do not accept external design tokens, component libraries, or brand kits. Every generation starts from the tool's trained defaults. If your team has an established Figma component library that new designs must conform to, Galileo cannot source from it. You will always be working backward from Galileo's output to your design system. For solo founders and small teams at the concept stage, this is a minor constraint. For design-system-mature teams, the absence of component-library-aware generation is the core reason to use Figma AI instead.

Free vs. Pro: where the real ceiling lives

Galileo AI's free tier, at 10 generations per month, was too tight for professional use. The $19/month Starter plan gave 1,200 credits; the $39/month Pro plan gave 3,000 credits with private design visibility. One credit equaled roughly one screen generation. A designer running three directions for a client pitch across five screens each would burn 15-plus credits per client, clearing the free tier in a single project. Private visibility was often the harder constraint: confidential client work could not live on a platform where the free tier made all designs publicly searchable. Teams working on unreleased products were effectively required to pay for Pro regardless of generation volume.

Post-acquisition, both constraints are gone. Google Stitch is free, private by default, and provides 350 standard generations per month. The real ceiling now is the Google Labs experimental status: no SLA, no guaranteed uptime, no paid tier as a stability signal. Teams building workflows around Stitch depend on a product Google could sunset with the same abruptness it shuttered Google Reader, Stadia, or Inbox.

Export quality and format support

Figma export from Galileo produced clean frames with logical layer grouping. Individual UI elements arrived as grouped shapes rather than editable Figma components, which meant they could not be swapped with your actual Figma component library variants without manual relinking. For concept review, the export quality was sufficient. For production design, every frame required component substitution before it could be used in a real design system.

HTML and CSS code export in the Galileo era produced semantic markup with inline styles and class names that followed a flat naming convention. The code was readable and gave developers a credible starting point for implementing the design, but it was not architect-level code: no CSS custom properties, no design tokens, no component abstraction. Google Stitch 2.0 expanded code export to six frontend frameworks, though specific framework details beyond "HTML/CSS and React/Tailwind variants" were not publicly detailed at time of review. Developer feedback on the Stitch code export is that it produces cleaner Tailwind output than Galileo's raw CSS, but still requires adaptation before it fits a real codebase's conventions.

Neither Galileo nor Stitch produces SVG asset exports, icon libraries, or standalone image assets from generated screens. What you export is either the Figma file or the code. Designers who need to pull individual illustrated elements from generated screens will need to do so manually after Figma export.

Galileo AI vs. v0 by Vercel vs. Figma AI

v0 by Vercel flows from text to executable React code: components with Tailwind CSS and shadcn/ui, copy-paste-ready for a production codebase. Its output is not a visual mockup; it is component files. A developer using v0 wants to ship a button into an existing codebase. A designer using Galileo wanted to present a three-screen concept to a stakeholder before any code gets written. v0 is component-level and developer-first. Galileo was screen-level and designer-first. Both are now free, so combining them is a realistic workflow at no cost.

Figma AI generates designs inside Figma's canvas, editable immediately within your existing file, including your component library and design tokens. Galileo was a standalone tool whose Figma export created external frames without access to your existing Figma components. Figma's Dev Mode code panel produces static HTML/CSS reference snippets for documentation, not runnable components. Galileo's code export was production HTML/CSS designed to be implemented. Designers who are Figma-native should use Figma AI. Designers who want fast, external concept generation before committing to a Figma file found Galileo's standalone approach faster for pure ideation.

Skip Galileo AI and Google Stitch if your project requires animation, brand-kit-aware generation from your own component library, or WCAG compliance guaranteed by the tool. Choose it for fast, credible-looking screen mockups from a text description, with Figma refinement as the required next step.