Design-to-code, finally done right
Pixframe scans your design system once, then converts any Figma frame to code that visually matches your design, uses your actual token names, and survives your next rebrand - automatically verified before it ships.
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What we actually want is Figma to Claude Code. Why would I take something generated by AI, refine it in Figma, and then spend 3 hours trying to replicate those changes in code only for it to never end up 1:1 with the design?
- A senior developer, publicly, to significant engagement. Not an edge case.
The cost of "close enough"
Every product team using Figma faces the same daily loss. It compounds invisibly - until you calculate it.
4–8hrs
Manual correction time per non-trivial design, / per developer. Every week, on every team.
$1.08M
Annual productivity waste for a 5-developer / team, at market rates. Per year, not per project.
0
Tools ship with token compliance auditing or visual verification. Until now.
01 / CAUSE
Every existing tool reads Figma's node tree - a JSON description of the design. JSON cannot encode how a shadow reads across a background, how spacing breathes in context, or how a gradient composites over imagery. The output is always structurally close and visually wrong.
02 / CAUSE
Existing tools generate generic code - and even tools that read your token names resolve them to raw hex values in the output. var(--primary) becomes #020617. The code looks correct. It breaks silently on the first token update. We tested this.
03 / CAUSE
Every correction cycle requires a new LLM call. A complex design with 40 components and 2–3 correction cycles burns hundreds of thousands of tokens per project. This is economically unsustainable and structurally discourages the iteration that real-world design actually requires.
The architecture
Vision-first rendering, design system grounding, and automated visual verification - working as a single loop.
Layer 01
Ground truth renderer
Export each Figma frame to a pixel-accurate PNG via Figma's export API. The vision model sees what the designer sees - not a JSON abstraction of it. This closes the lossy-data problem immediately.
Layer 02
Design system schema
Scan your design system thoroughly. Create a local JSON schema that includes your color tokens, spacing scale, component variants, and patterns. All generation utilizes this.
Layer 03
Visual verification loop
Semantic audit first: every color, spacing, and radius value checked against your token schema - no raw values through. Then visual diff: render, compare against the original, iterate until it clears.
Output
Semantic code, not div soup
The output uses your real component APIs and token names. < Card variant="elevated" size="md" > - not < div className="rounded-lg shadow-md p-4" >. It survives your next rebrand.
Over time
Institutional memory
Accepted generations signal preferences. The system learns token choices, idiomatic compositions, and drift from defaults. The 50th generation is more accurate than the first.
Economics
Cost curve that inverts
Known patterns become template instantiations - no LLM call at all. The DS schema is a one-time cost. Every other tool's cost is flat or rising. Ours goes down over time as the schema matures.
Competitive landscape
These are not features that competitors have poorly. They are features that do not exist anywhere in the market. That is our product.
| Capability | Lovable | Figma Make | shadcn Studio | Cursor | Bolt / v0 | Pencil.dev | Pixframe |
|---|---|---|---|---|---|---|---|
| Visual truth (renders image) | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ |
| DS schema ingestion | ✗ | partial | ✗ | ✗ | ✗ | partial | ✓ |
| Token contract audit | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ |
| Visual verification loop | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ |
| Institutional memory | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ |
| Semantic component output | generic | partial | vanilla only | manual | generic | generic | your DS |
| Token cost over time | flat | flat | flat | flat | flat | flat | ↓ decreases |
The moat
Every generation is a signal. Over hundreds of interactions, Pixframe learns which token choices your team prefers in specific contexts, which component compositions are idiomatic versus one-off, and where your design system has drifted from the tool's default assumptions.
This creates an institutional memory that compounds. The tool becomes more accurate the longer a team uses it - and switching costs that grow with it.
Pricing
4 hours per week at $150/hr = $2,400/month in recovered time per seat. Our price is a fraction of that.
Starter
$79
/ month per pair
For small teams getting started with design-to-code automation.
1 designer + 1 developer seat
DS schema scan (1 project)
Visual verification loop
shadcn/Tailwind support
Clipboard & PR output
Team
$149
/ month per pair
For teams who need institutional memory and multi-DS support.
Up to 5 seats
DS schema scan (unlimited projects)
Institutional memory layer
Multi-DS support (Radix, custom)
IDE integration (VS Code, Cursor)
Token drift detection
Enterprise
Custom
contact us
For large design teams with proprietary design systems.
Unlimited seats
Private DS ingestion
On-premises schema storage
CI/CD verification pipeline
Fine-tuned model on your history
DS governance & analytics
SEE IT IN ACTION
Watch the visual verification loop close the gap between what was designed and what gets shipped.
Demo 1 - pricing card
Demo 2 - login form
Early access
We're onboarding teams using Next.js + Tailwind + shadcn. Waitlist members get priority access, founder conversations, and early pricing locked in.
No spam. No pitch deck. Just an honest conversation when we're ready.
