Modeinspect

Modeinspect is a design canvas with your codebase and agents built in, positioned as a production-grade AI design tool that runs in your codebase. It is built for design engineers and for teams that want to design high-fidelity product features directly on the real thing rather than in a separate mockup tool. Teams connect their codebase, open an existing screen, and design with their own components, tokens, live data, states, and breakpoints. The canvas is deliberately framed as a means to an end: as the company puts it, the canvas is not the destination, the product is. Design on a real canvas that sits on top of the real product, with all the freedom of a design tool and none of the throwaway mockups. The problem Modeinspect addresses is stated plainly: most software is designed twice, a picture first and then again in code, with intent drifting in between. The traditional path runs through what the product calls the handoff chain, where design passes work down a chain and then waits for it to come back. Mockups are rebuilt in Figma away from the real product, specs and redlines document every state, engineers reinterpret the design in code, and every change restarts the whole loop. Modeinspect describes that old way as taking 45+ days from design to ship, and contrasts it with a canvas-to-PR loop it says runs about 10 days — roughly 4.5× faster. The argument is that design should not be detached from the thing it is designing, because the moment intent is copied into a picture, it begins to drift. The core promise is unified, collaborative design in code. Everything is in one place: your codebase, the canvas, and coding agents are integrated out of the box, with no MCP servers, no localhost, and no devops glue required. A live product can be pulled onto the canvas, letting you capture any element of your live product, pixel perfect and fully editable, so work starts from where things actually are. Rather than prompting for every adjustment, Modeinspect emphasizes controls, not prompts: a padding change should not take a paragraph, so you edit anything, on canvas or in code, with the visual controls you already know. Your changes then build back as canvas to code in one shot, producing clean, scoped diffs with your design system enforced. The canvas is also built for collaboration, with no localhost to share and no branches to wrangle, so you can send a link, collect comments, and open the PR in one click. Modeinspect is built for design engineers, and its component story is deliberately literal. Components are 1:1: you drop in the actual components your product ships, with every variant and every state intact, rather than a redrawn look-alike that quietly drifts from the real thing. Tokens are enforced, so every color, space, and text style comes straight from your library, and everything you place is automatically on-brand — nothing off-system can sneak in. Breakpoints are native: mobile, tablet, and desktop are laid out side by side and each one reflows live, instead of relying on one frozen frame you just hope survives on a phone. And capture to canvas means that when you spot something in the real product you want to rework, you can pull it straight onto the canvas pixel-exact and fully live, and start from where things actually are rather than from an approximation. Dynamic states and real data are treated as first-class parts of the design rather than afterthoughts. Hover, focus, error, empty, loading, and success states are shaped on the real component, so a design never falls apart the moment someone actually uses it. Real data and real flows mean designing on top of live data and genuine journeys — long names, empty states, and the messy edge cases — so your work holds up in the wild and not just in a tidy mockup. AI exploration uses the latest AI models to explore variants, restyle a section, adjust copy, or apply a design direction while you stay in control, which keeps the AI in a supporting role instead of taking over. And because every move you make on the canvas becomes the real product as you make it, there are no redlines, no spec docs, and no waiting on a rebuild: what you design is what ships. A large part of the product's approach is the quality of the code that comes out the other side. Mode reads your file layout, components, tokens, conventions, and existing logic, then writes within them, so PRs land scoped, type-safe, and ready for engineering review. Diffs are scoped and clean, letting engineers review focused changes rather than rewritten surface area or noisy AI churn. Your design system is enforced throughout: Mode pulls from your component library and design tokens, with no hardcoded colors, no magic numbers, and no throwaway components. There is no generated UI debt, because changes reuse your components, tokens, utilities, and styling system instead of creating a parallel design system. And changes are type-safe, with props, state, events, and data shape checked against the product instead of guessed from a mockup. This is the methodology that separates Modeinspect from AI app builders that generate new surface area alongside the one you already maintain. The stated benefits are framed as measurable rather than aspirational. Modeinspect says production-grade is not a tagline but the metric, and it highlights one customer story in which a team merged design and engineering into the same loop, saving 22 days on the delivery cycle, with zero engineering handoffs and design QA removed. Prelude's Chief Product & Design Officer, Quentin Le Bras, is quoted saying his designers explore on the actual codebase, with real data, and open the PR themselves, going from idea to a merged PR without a handoff in between. A Product Design Manager at Kiwi.com describes the tool integrating seamlessly with their codebase and design system, which is exactly what they had been looking for in AI design tools, enabling iteration on top of an already complex product. A Principal Product Manager at Moss calls it the first AI tool that respects their design system 1:1, allowing the team to create production-like prototypes and making the whole team faster. A UX Designer at NCCER highlights the ability to make changes in real time using their design system and immediately push those changes to code for senior developers to review and merge. The product organizes this into three workflows that share one production loop, all inside your real codebase at production fidelity. Prototyping covers prototypes that feel like the product, built with real data, dynamic states, breakpoints, and interactions — so instead of pitching with mockups, teams pitch with the thing itself. Design QA happens all in one loop: compare canvas to live build pixel-by-pixel, spot drift, fix it, and keep moving, with no round-trips through Figma. Shipping PRs covers pushing minor visual changes or new components as merge-ready PRs, with context, screenshots, and a clean diff. Concrete scenarios follow from these: reworking a screen you spotted in production by capturing it to the canvas, exploring variant directions with AI while keeping control of the final decision, validating a layout against long names and empty states using live data, checking a live build against the canvas for visual drift, and sending a link to stakeholders for comments before opening the pull request. Modeinspect is aimed at design engineers and at teams where design and engineering work in the same loop. The marketing site notes that the product is optimized for larger screens, which fits a workflow built around a canvas, a codebase, and side-by-side breakpoints. The company reports being loved by design engineers at Kiwi, Moss, Apify, e2b, Prelude, NCCER, and Deepnote. Pricing is listed at three monthly tiers: $0/mo, $24/mo, and $48/mo, so there is a free entry point alongside paid plans. Sign-in and the working canvas live at app.modeinspect.com, and the site offers an option to email yourself a link for later. In summary, Modeinspect's primary value proposition is that design stops being a picture that must be rebuilt and becomes the production loop itself. By putting an AI design canvas on top of your real codebase — with your components, tokens, live data, states, and breakpoints — and by writing changes back as scoped, type-safe, merge-ready diffs, it removes the handoff in between and lets teams go from an idea to a merged PR in a single pass.