Had a fun weekend building Merisole ☀️ Designed with Google Stitch.

I'm a senior product designer working on e-commerce sites every day, so my real question going into this challenge was practical: how easily does Stitch slot into my actual workflow? Could I use it to spin up a polished, client-ready option in a single sitting or even take it all the way to a shippable site? To test that, I asked AI to create a fictional footwear brand, Merisole, and treated it like a real client project from brief to launch.

How Stitch fit into the workflow Before generating anything, I wrote a custom design.md. Type scale, color tokens, spacing rhythm, the editorial tone I wanted Merisole to carry and uploaded it into Stitch as a design system. That single step is what made the rest of the build feel coherent. Every screen Stitch streamed onto the canvas already spoke the brand's language, so I wasn't fighting drift between sections. This is exactly the part of my normal e-com workflow that usually eats hours! Getting AI tools to respect a brand spec was something that Stitch handled cleanly. By the way, the stream on canvas feature was one of the coolest interactions I've seen so far! It was like having a designer design on your screen remotely through AnyDesk!

From there I worked mostly through in-place AI edits: clicking a section, telling Stitch "to add it to another variation," or "add a hover animation on the cards," and getting changes in seconds. The streaming generations meant I was reacting to a real interface taking shape instead of waiting for a static result.

The motion work is where Stitch surprised me most. Native hover states, shader backgrounds, and scroll-linked animation. All authored inside the tool, no handoff to a separate motion app, no code wrestling. Then I exported the build out to Netlify in one step.

New features I leaned on

Custom design.md - design system applied across the whole project (kept every screen on-brand from the first generation)

Streaming generations to canvas (the whole first pass)

In-place AI edits via prompts + point-and-click (most of the polish phase)

Native motion, hover states, and shaders on HTML canvas

Netlify export (final deploy)

Feedback on the platform Coming at this from a daily e-com design seat: Stitch genuinely shortens the distance between brief and client-ready. The design.md - design system flow is the underrated hero, uploading a brand spec once and having Stitch honor it across every streamed generation is the closest I've felt to "AI that respects a brand." The streaming canvas is amazing to say the least! It collapses the "describe - wait - judge - re-prompt" loop into something closer to direct manipulation. In-place edits felt the most like real design work. I stopped writing long prompts and just pointed at things. Two things I'd love next: An option to tweak elements without having to prompt it. And a way to save in-place edit "recipes" so I can reapply a vibe across sections without re-describing it.

Stitch didn't feel like an AI tool I was wrangling. It felt like a canvas that happened to understand what I meant. Merisole came together in a single sitting because of that, and I'm walking away convinced Stitch has a real place in how I'll pitch and build e-commerce work going forward.

Check it out:

Live site (really fun to click around): https://merisole.netlify.app

Stitch project: https://stitch.withgoogle.com/projects/834230787052196219

Amazing!

𝗠𝘆𝘁𝗵: 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 𝗮𝗿𝗲 𝗺𝗮𝗶𝗻𝗹𝘆 𝗮 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻.

They are not.

They are an operations decision first.

Most businesses ask:

❌ “𝗪𝗵𝗶𝗰𝗵 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁 𝘀𝗵𝗼𝘂𝗹𝗱 𝘄𝗲 𝗯𝘂𝗶𝗹𝗱?”

Better question:

✔️ “𝗪𝗵𝗮𝘁 𝗿𝗲𝗽𝗲𝗮𝘁𝗲𝗱 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗶𝘀 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝗰𝗹𝗲𝗮𝗿 𝗲𝗻𝗼𝘂𝗴𝗵 𝘁𝗼 𝗱𝗲𝗹𝗲𝗴𝗮𝘁𝗲?”

Because an agent needs boundaries.

-> What starts the task? -> What information should it read? -> What action can it take? -> When should a human review? -> Who owns the result?

If these answers are missing, the agent becomes another thing the team has to manage.

The best automation is not the flashiest one.

It is the one your team can trust, check, and actually use.

Worth thinking about before adding another AI tool.

This is the exact conversation more businesses should focus. AI agents should amplify operational efficiency without creating additional barriers for the team.