Ingestion overview

How external systems flow into preset AI's canonical model.

Why ingestion matters

Most teams are not starting from zero. They begin from:

• Figma variables and component styles
• existing codebases
• Storybook libraries
• third-party ecosystems (for example, shadcn/ui, Carbon)

preset AI ingestion should accelerate standardization without forcing a full rebuild.

Ingestion principle

External sources are inputs, not the long-term source of truth.

external source → normalization → canonical model → governance + distribution

Primary ingestion pathways

Figma

• variables, styles, and components imported and mapped to tokens and presets
• drift detected via comparison workflows

Codebase / GitHub

• scan component usage and infer patterns and preset candidates
• detect drift and adoption gaps

Storybook

• extract component APIs and usage examples
• map to preset AI artifacts and the enforcement model

Ecosystem / template starts

• map existing conventions to the semantic preset AI taxonomy
• identify where direct import vs. guided migration is safer

What ingestion must preserve

• source provenance
• confidence of mappings
• explicit unresolved items requiring human review
• safe change boundaries before apply

Governance checkpoints

Before imported artifacts become canonical defaults:

• naming normalization
• token / preset semantic quality checks
• pattern and policy alignment
• proposal + validation + approval workflow where needed

See Importing design systems for the user-facing import flow and API overview for the underlying surfaces.