Modeinspect
The AI-native way to design production-grade UI.
In their words
The AI-native way to design production-grade UI.
Mode is the AI design canvas for shaping product UI directly in your codebase.
Design in code on a visual canvas
Mode turns your product’s codebase into editable canvas frames. Refine real UI with visual controls, experiment with AI then share or hand off for review.
EXPLORE
Start from a product screen or blank frame and explore directions with Al.
DESIGN CONTROLS
Prototype your interface on a canvas with familiar layout and style controls.
BUILD
Build any design into a live prototype with your real design system, tokens, and components.
SHARE AND REVIEW
Share the live prototype for feedback, then send engineering a PR with pixel-perfect changes.
Key Features
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1:1 Component Sync: Directly use your existing design system components, variants, and states within the visual canvas.
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Token Enforcement: Automatically applies your design tokens to maintain brand consistency without manual intervention.
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Code-First Design: Changes made on the canvas generate clean, type-safe pull requests, eliminating the need for separate design specs and documentation.
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AI Assistance: Utilize AI to explore design variants, refine styles, or adjust content while staying within your production environment.
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Live Data Prototypes: Prototype using real product data to ensure designs handle edge cases and dynamic states correctly.
Fact file
Web app- Web
- Delivery cycle
- 22 days saved
- Security standard
- SOC 2 Type II
What is Modeinspect?
Design in code on a visual canvas
Existing product codebases turn directly into editable canvas frames through Modeinspect, eliminating the gap between static mockups and live software. Instead of rebuilding interfaces in isolation, design engineers work on top of the real product using actual components, variants, and design tokens. The interface synchronizes 1:1 with the underlying repository so that visual styling, spacing, and brand colors remain strictly enforced without manual handoff documents or redline specs.
Prototyping and design QA in one workflow
Working on the canvas allows teams to build interactive prototypes driven by real product data, handling dynamic edge cases like empty states, long names, and various error conditions across mobile, tablet, and desktop breakpoints. When visual drift occurs during design QA, any pull request opens inside the canvas to be fixed, polished, and reviewed in real time. AI assistance helps explore style variations and copy changes while staying completely bound to the production environment and design system limits.
Pull requests ready for engineering review
Every layout adjustment, component placement, and style edit made on the canvas translates into clean, type-scoped code changes. Rather than generating noisy UI debt or parallel styling systems, the platform constructs targeted pull requests that match repository conventions and coding styles. Engineers receive readable diffs containing type-safe props and events, allowing teams to move from initial design exploration to a merged pull request without traditional handoff chains.