Give Design Studios Design System Memory That AI Tools Actually Read
Design systems live carefully maintained in Figma — and entirely invisible to the AI tools designers use. Every design AI session re-explains the system from scratch. MemoryLake gives design studios design system memory that every AI tool reads.
Give Design Studios Design System Memory That AI Tools Actually Read
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The problem: design system rules don't reach AI tools
Your design system defines tokens, components, spacing rules, type ramps. The AI design tools you use produce output that ignores all of it. Designers spend post-AI time aligning output to the system the AI should have followed.
How MemoryLake captures design system memory
Design system skill memory
Tokens, components, rules, banned patterns.
Per-client design system variants
Each client's system scoped separately.
Reflection memory for system evolution
How the system has changed.
Cross-tool retrieval
Figma, image AI, design copilots unified.
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How it works for design studio system memory
- Connect — Import design system docs and authorize design tools.
- Structure — Tokens, components, rules become typed memory.
- Reuse — Every AI tool retrieves the system before generating.
Before vs. after: design system AI memory
| Without MemoryLake | With MemoryLake | |
|---|---|---|
| AI output follows system | Rarely | By default |
| Cross-tool system consistency | Variable | Memory-enforced |
| System updates reach AI | Manual | Immediate |
| New designer onboarding | Slow | Memory-loaded |
Who this is for
Design studios and in-house design teams maintaining design systems at 5-50 person scale — where system fidelity in AI-generated output matters.
Try this use case on your own account
Kiteline Studio keeps its design system in two layers: the studio's own system, and one project per client holding only what that client changes. An AI tool that reads the client alone has no rule for anything the client didn't change; one that reads both gets two values for every token the client did change, and nothing says which wins. Read layered, the client wins token by token.
What you will see
- Every token is one fact with
fact add, andfact update --metadatarecords its value, version, who decided it, when and why: 12 tokens and 2 banned patterns on the studio project, 5 overrides and a drop-shadow ban for Orchard Bank, 3 overrides for Pell & Finch - Read the way an AI tool would:
searchon Orchard Bank's project alone has no spacing token at all, andsearch --projects <studio>,<client>returns both corner radii with the studio's 8px ranked above Orchard's 0px. 14 of 14 overridden lookups across 7 runs - Layered:
fact listover both projects, the client's override wins per token. Yesterday's three AI drafts are reviewed line by line, each finding citing the layer, version and date, and Orchard Bank's green on a Pell & Finch card is named as Orchard's - The studio changes
space.4with onefact update: Orchard Bank inherits 20px, Pell & Finch keeps its 12px override, a draft that passed yesterday fails today, andfact tracekeeps both versions. Plus a local web UI
You need: a MemoryLake API key · the memorylake CLI · Python 3.9+
Run it
git clone https://github.com/memorylake-ai/memorylake-usecases.git cd memorylake-usecases/design-system-memory export MEMORYLAKE_API_KEY=sk-… python3 demo.py # or: python3 web/server.py
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Frequently asked questions
Figma integration?
Figma integration?
Yes — direct connector.
Multi-client design systems?
Multi-client design systems?
Per-client namespaces.
Free tier?
Free tier?
Yes — for small design teams.