Krit

Krit is the consistency layer for AI-generated product design. Live at krit.design. It's a local, agent-native infinite canvas: you tell your agent what to design, and it draws real screens onto the canvas — prompt, refine, ship as live code. The bet isn't on generation. It's on what happens after the first screen.

## Problem

Two giants now own "describe a UI, get a UI" — Google Stitch and Anthropic's Claude Design. They're free, they have distribution, they stream onto a canvas live. Competing on the first screen is a lost game; everyone wins that now.

The thing one-shot tools demonstrably fail is the 50th screen. Prompt "add a settings page" five times and you get five inconsistent results — different spacing, different button, different voice. Generation went commodity. Consistency didn't. That gap is the whole product.

## The thesis — generation is free, consistency compounds

→ Don't compete on the first screen. Compete on the 50th screen matching the first.

Giants are structurally weak here because they optimize one-shot demo wow and reset per-project memory every session. A perfect model called statelessly per screen still drifts — consistency is a systems problem, not a taste problem. No model upgrade fixes a stateless architecture.

So Krit leads with the outcome — your brand, every screen — and lets the category sit underneath: the consistency layer for AI design.

## The non-obvious call — the pipe vs. the moat

The mechanism is server-side enforcement: per-project tokens (color, type, radii, shadow, motion, grammar) injected into every sandboxed iframe, agent-independent. The AI generates; the canvas enforces. That's the pipe — real and giant-resistant as positioning, but copyable in a sprint, and it only fixes rules, not taste.

The moat is the judgment data flowing through the pipe. Every accept, reject, and override on a generated screen, every usability finding, every per-project visual language — that's labeled taste data a horizontal giant never collects at byte granularity. Giants compound on model capability and reach. Krit compounds on judgment. Bet the company on the second axis.

The sharp realization auditing the code: that signal was already being computed on every write and then deleted — only an integer count survived to analytics. The durable moat was one schema field and two writes away from starting to collect. A plumbing problem, not an invention problem.

## Why this gets stronger as models improve

Most "AI tool" theses decay as base models get better. This one inverts:

→ Anti-regression. The model's loss rewards the average, so the average AI look gets more pervasive and recognizable as models improve. Opinionated deviation appreciates. The curated register is worth more, not less. → Private brand is data the frontier never sees. The model knows "good" generically; it never sees this founder's 40 prior screens and rejected options. That's a compounding switching cost.

## Beyond the mockup

Krit doesn't stop at a static frame. The same canvas carries an idea from prompt to shipped artifact — interaction, motion, video, and a usability read, all in one place.

Interactive and responsive — not a picture of a screen. What lands on the canvas is live code that responds, reflows, and behaves like the real product.

Prompt the motion. Animation is part of the design, not an afterthought bolted on in another tool. Describe the motion in words and Krit animates the actual elements — including SVG.

Storyboard to video. String scenes into a storyboard and export the whole thing as a real launch reel — finished video out of the same canvas you designed in.

Usability, before users. Run a usability pass and five AI users walk your flow, then tell you exactly where they got stuck — structured findings, not vibes.

Local and vendor-neutral, so none of this is locked to a single model funnel — hand it off as code in whatever your stack speaks. The high-end interactivity and motion are uncontested at the ceiling; the discipline is to own that ceiling and never pitch the basics, which reach parity fast.

## Close

The wedge is the technical solo founder already coding in Cursor or Claude Code, shipping a real multi-screen product without a designer — exactly the person who feels longitudinal-consistency pain, and exactly the user whose accept/reject signal feeds the flywheel. Wedge, moat, and demo are one loop: serving the user generates the data, the data closes the taste gap, and the closed gap is the demo.

The whole pitch fits in fifteen seconds — same prompt, five times, in a one-shot tool versus in Krit. One drifts. One stays on-brand. The giants can't reproduce it, because the architecture is the thing that fails it.

Live at krit.design.