Design systems your AI can read.

We architect tokens, variables and components that developers — and automated build pipelines — execute without a translation step. Semantic naming, Figma↔code parity, documentation written for execution.

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Why systems fail

Token semantics Your AI can't tell secondary text from disabled text. Tokens describe how things look, not what they're for. gray-700#5B6170text-2 text-secondary

Component parity What ships doesn't match what was designed. Figma components resemble code components — they don't map to them. Card_v2_finalButton-FINAL-v3Group 128 Card ↔ Card.tsx

Naming drift Three names for the same thing. Design, code and docs each call it differently — every reader has to guess. secondarymutedgray-2 text-secondary

Source of truth Your system lives in heads and PDFs. There's no machine-readable source of truth a pipeline can execute. brand-guidelines.pdfhandoff-notes.docxSlack threads tokens.json + Code Connect

Your system was written for humans to read.
AI agents are now its heaviest readers — and they fail on all four.

What we build

01 Token architecture in Figma: variables, modes, semantic naming

02 Core component libraries scoped to what actually ships

03 Figma ↔ code parity — components that map, not resemble

04 Documentation a developer or a pipeline can execute without interpretation

05 Governance: what enters the system, and who approves it.

Three ways in

for agencies

Brand → system

You won the brand; we make it executable. One client brand end to end — tokens, components, docs. Repeatable per client, white-label or named.

Talk to us →

for existing systems

System audit

A scored diagnostic of an existing system: findings across six dimensions, quick wins vs structural fixes, and a live test — an agent tries to build a known screen from your system.

Talk to us →

recurring

Standing capacity

A recurring slot in your pipeline: N brands per month.

Talk to us →

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AI-readiness score /100 · before → after

Remaining points: governance — adoption on the client's side.

Done at product scale: we re-architected an enterprise platform's design system to be machine-readable — 180+ components, 800+ tokens in four disciplined tiers, explicit Figma↔code mapping. Every engagement ends with a live consumability test: an agent builds agreed screens from the system, unaided. Proof, not promises.

Design systems your AI can read.
Led by Diego Nogueira ↗