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Figma design systems, engineered for the AI era
A Figma file alone isn’t a system. We help teams turn Figma into structured, machine-readable infrastructure — tokens, variants, metadata — so that developers and AI agents can both build from it with confidence.
What a real Figma design system is
Figma-based design systems are no longer nice-to-haves — they’re essential infrastructure. But a Figma file alone isn’t a system. A working Figma design system is more than a component library. It has:
- Atomic components with consistent variant logic
- Semantic design tokens for spacing, color, and typography
- Accessibility baked in, not bolted on
- The naming conventions and metadata that make Dev Mode — and MCP — actually useful
Structured right, your Figma file doesn’t just guide developers. It generates real code.
Why structure pays — the developer’s side
Without a system, developers get flat designs, inconsistent spacing, and handoffs with no context — guesswork all the way down. With one, components snap into predictable structures, tokens map 1:1 to the codebase, and MCP-connected editors like Cursor and Claude generate code you can actually ship.
95%
In one of TJ Pitre’s demos, a login card built in Figma came out 95% production-ready — tokens and all — after a single pass through an AI editor, using MCP.
How Figma MCP changes the game
MCP adds a machine-readable layer on top of Dev Mode. Instead of a screenshot and a prayer, an AI editor gets your component’s tokens, slots, and structure — and turns them into component code. It works in Cursor or Claude, and it supports headless systems where the design source and the code target live in different stacks.
The path looks like this: Figma → AI → component code, with the system intact.
› “implement the login card from figma” ✓ mcp: tokens, slots, structure extracted ✓ component generated — tokens mapped 1:1 — 95% production-ready after a single pass.
FigmaLint: audit before handoff
FigmaLint audits your components the way a developer — or an AI agent — would read them. It flags:
- Hardcoded values where tokens should be
- Missing accessibility props
- Unclear naming and variant logic
- Structure gaps that break automation
Recommendations land in-editor, with a built-in chat for working through fixes — so quality gets checked before handoff, not after the bug report.
The toolchain we use
- Figma as the source of truth.
- MCP for structured output — tokens, slots, structure.
- Claude or Cursor for AI code generation.
- Story UI for prompt-based layout prototyping in Storybook.
- FigmaLint for audit and QA before anything ships.
This isn’t design-to-code gimmickry — it’s the process we run with mid-market and enterprise teams to shorten cycles and cut dev friction.
Proof: Big Medium
- Client
- Big Medium (Dallas, TX)
- Challenge
- Translate an existing Figma design system into production-grade web components — aligned with tokens, variants, and design logic.
- Approach
- Built on the Altitude Design System foundation, wrapped in React, with MCP and Story UI enabling prompt-based layout generation — PMs laid out pages in natural language against a fully token-compliant system.
- Result
- 40%+ reduction in design-to-code time
Related work: PetSmart Sparky · On The Line with Toast
The handoff checklist
Before you hand off to developers — or to AI — ask:
- Are all tokens mapped and named semantically?
- Are components structured with variants, slots, and props?
- Are hardcoded values avoided?
- Are states clearly defined and grouped?
- Has FigmaLint passed all checks?
We audit and refactor existing Figma systems, build headless component libraries, automate code generation with MCP + AI, and coach designers on system thinking. If your Figma file is working harder than your team, let’s fix that.
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Let’s talk about your Figma system
Audit, refactor, or rebuild — a 30-minute discovery call is the fastest way to find out what your Figma file could be generating.