Give Designers AI Memory of Every Client's Feedback Patterns and Preferences
Design clients leave feedback in different tools and formats — Figma comments, email threads, Loom recordings. Each client's preferences emerge across rounds. AI design tools see one round at a time. MemoryLake gives designers feedback memory per client.
Give Designers AI Memory of Every Client's Feedback Patterns and Preferences
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The problem: client design feedback doesn't accumulate as memory
Client A always pushes for tighter type. Client B prefers more white space. Client C reacts negatively to specific color combinations. The pattern across rounds is real — and it lives only in the senior designer's head, not the AI's.
How MemoryLake captures design feedback memory
Per-client feedback reflection memory
Recurring preferences surface.
Round event memory
Approval history per client.
Skill memory for client review style
How each client gives feedback.
Cross-tool retrieval
Figma, email, Loom unified.
Free forever · No credit card required
How it works for design feedback memory
- Connect — Authorize tools where client feedback lives.
- Structure — Each piece of feedback becomes typed memory linked to client.
- Reuse — AI design tools retrieve client preferences before generating.
Before vs. after: design feedback AI memory
| Without MemoryLake | With MemoryLake | |
|---|---|---|
| Anticipate client feedback | Manual recall | Reflection memory |
| Cross-round preference tracking | Tribal | Captured |
| New designer learning client | Months | Memory access |
| AI tool client-awareness | Generic | Memory-loaded |
Who this is for
Design studios juggling multiple clients where understanding each client's feedback style is a real productivity factor.
Try this use case on your own account
A studio note says Harbor & Pine wants tight type: approved with tight type in 6 of 7 review rounds. The count is right, but once the note was written every comp only had tight type, and the client approved the only option on the table. The one round where they were shown both, they picked airy. A preference count that includes the note's own output measures how faithfully the studio followed the note, not what the client likes.
What you will see
- 16 review rounds from Figma comments, email threads and Loom transcripts pinned word for word, each with what the comp showed on that point and how the client answered (asked, picked, approved) in metadata; plus 3 studio notes with the counts they cite
- The quick read searches the brief and defaults to the client's note: all three notes rank #1 and all three say "default to it"
- The memory read counts only rounds where the client had a choice. Approving a comp that only had the note's option, after the note was written, is an echo: Harbor & Pine is 1 for, 1 against, 5 echoes (show both); Vela Outdoor's no teal + orange is 1 for, 4 echoes (unconfirmed); Lumen Health's white space, 4 asked unprompted, is the control
- Both notes are rewritten from the rounds with
fact update --text(old text kept in metadata,fact traceshows both versions), and the same quick read then agrees 3 of 3. Plusask,roundsand 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-feedback-memory export MEMORYLAKE_API_KEY=sk-… python3 demo.py # or: python3 web/server.py
Watch it run · real, unedited recordings
Related use cases
Frequently asked questions
Integrations?
Integrations?
Figma, Loom, Gmail, Slack, custom — all supported.
Privacy?
Privacy?
AES-256 E2E with per-client isolation.
Free tier?
Free tier?
Yes — for small design studios.