Prototypes that feel like the product.
Modeinspect is an AI prototyping tool for teams changing a product that already exists. Prototype the actual product features, not mockups. Use your product and design system to build high-fidelity prototypes: real data, dynamic states, real flows.
Real prototyping
Start from your real product. Build functional features in code using the prompts and design controls you already know, with no throwaway artifacts.
Visual consistency
Modeinspect uses your design system one-to-one. No more mockups that feel far from the real feature. Whether you're sharing with stakeholders or exploring a product idea, get to production quality fast.
Live data
Mocking live data is a thing of the past. Explore new ideas on your live product with real user data.
Demo
Demo your prototype the way you would the actual product. Interactive mode lets you fully click through and interact with everything you build.
Share
Share your prototypes with users and stakeholders through live preview links.
Pull request
When the prototype is approved, open a pull request from the canvas in one click. Modeinspect writes the title and description, and engineers review and merge it like any other change. The prototype is already the code, so nobody rebuilds it.
Prelude went from idea to merged pull request in 13 days.
Prelude's designers build on their own codebase and open the pull requests. Engineering review still gates every merge.
Read the Prelude story- Idea to merged PR
~31 days13 days- Handoffs per feature
5+1
Prototyping guide
Prototyping tools: from Figma click-throughs to code on your product
Which prototyping tools fit which question, why fidelity and real data matter, and when a prototype on the real codebase beats a mockup.
What a prototype is for
A prototype exists to answer a question before the build pays for it: does the flow make sense, does the value land, does the interaction feel right. The question decides the fidelity and the tool.
Read this section in the guide ↗Low fidelity vs high fidelity: which to build
Low fidelity prototypes test structure and flow cheaply. High fidelity prototypes test the real thing. Research finds both surface the same major usability problems.
Read this section in the guide ↗Click-through vs coded interactive prototypes
A click-through links static screens with hotspots. A coded prototype runs logic, holds state and reads real data, and can carry over into the build instead of being redrawn.
Read this section in the guide ↗The best prototyping tools, compared
Figma, Framer, ProtoPie, v0, Lovable, Bolt, Cursor, Claude Code and Modeinspect in one dated table: fidelity, where the output lives, real data, price tier and where each stops.
Read this section in the guide ↗AI prototyping on a new project vs on your codebase
AI prototyping tools build a fresh project by default. On an existing product the prototype has to use your components, tokens and data, or an engineer rebuilds it.
Read this section in the guide ↗Why prototype with real components and data
Real components keep the prototype honest about the design system, and real data exposes the empty, long and error states a mockup hides.
Read this section in the guide ↗When to skip the prototype and go straight to code
When the change is small, the codebase is known and a designer can make it with design controls, going straight to code is faster than a prototype that gets rebuilt.
Read this section in the guide ↗How to demo and test a prototype
Demo on real flows with interactive mode, share a live link, and test with five users per round across three rounds rather than fifteen users once.
Read this section in the guide ↗How to choose between prototyping tools
Start from the question, the fidelity it needs and whether the answer has to ship. A decision list for concept tests, fresh apps and changes to an existing product.
Read this section in the guide ↗Questions
What is the best prototyping tool in 2026?
There is no single best prototyping tool in 2026, because the right one depends on the question the prototype has to answer. Figma suits click-through concept tests. AI generators such as v0, Lovable and Bolt suit fresh apps, and codebase tools such as Cursor, Claude Code and Modeinspect suit changes to a product that already exists.
Can AI generate a prototype automatically?
Yes, tools such as v0, Lovable, Bolt and Figma Make build a running app from a text prompt in minutes. By default the output is a new project on the tool's own backend. It answers concept questions well but does not use your design system or data unless the tool works inside your codebase.
What is the difference between a click-through and a code-based prototype?
A click-through prototype links static screens with hotspots, while a code-based prototype runs real logic, holds state and can read real data. In a click-through every state is a separate screen and the data is typed in by hand. A code-based prototype can carry over into the build instead of being redrawn by an engineer.
Which prototyping tools produce production-ready code?
Tools that work in your existing repository, such as Cursor, Claude Code and Modeinspect, produce code in your codebase that engineers review as a pull request. Lovable produces real code too, but in a new project that has to be rebuilt inside your product before it can ship. v0 can build on an existing repo through Git Import, and Bolt can import an existing repo and commit to it.
How many users do you need to test a prototype?
Five users per round find most usability problems, according to Nielsen Norman Group. Run three rounds of five, fixing between rounds, rather than one round of fifteen.
Also in the loop.
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