AI Integration

Machine-readable context

AI tools are only as good as the context they receive. Classical design systems deliver pixels and Markdown — we deliver structured tokens, semantic components, and machine-readable patterns. Generation becomes reproducible, audit becomes reliable, compliance becomes documentable.

Work packages

Machine-readable Foundation

DTCG Token Architecture · Component API & Taxonomy · Build Pipeline

Knowledge-Base Architecture

Information Architecture & Content Model · Multi-Audience Rendering · Versioning & Provenance

Content Production

Component Documentation · Pattern & Decision Records (ADRs) · Brand Voice & Tone Codification

AI Access Layer

Retrieval Architecture · MCP Server / Tool Interface · System Prompts & Skills

Brand Compliance Layer (Guardrails)

Rule Formalization · Validation Engine · Pre- and Post-Checks in the Agent Flow · CI/CD Integration · Escalation & Exception Workflow

Tooling Integration

Figma Integration · IDE Integration · Design-to-Code Agent Workflow

Questions

Questions AI Integration answers.

  • Context

    Do AI tools see your design system as a structured schema — or only as a screenshot?

  • Generation

    Which AI outputs would you let through to production today without human review?

  • Audit Trail

    Can you prove which AI generated and approved which artefact?

  • AI Act

    Are high-risk AI applications in your product classified, documented, and signed off?

  • Data Sovereignty

    Which training and context data feed your internal AI — and who owns them?

  • Brand Consistency

    Does AI generate brand-aligned output for you — or default to a generic stack?

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