Keep Every Customer Research Insight, Across Every AI Session
Customer research generates signal that is expensive to collect and easy to lose. When your AI session ends, so does the synthesis you built up across hours of interview analysis. MemoryLake gives research teams a persistent memory layer that retains interview records, validated insights, and reusable research frameworks — so your AI keeps getting smarter about your customers over time, not just within a single session.
Keep Every Customer Research Insight, Across Every AI Session
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The Memory Problem
You spend three weeks conducting customer interviews. You feed transcripts to your AI one by one, building a picture of patterns across participants. Then you switch tools, hand off to a colleague, or start a new phase of research — and that accumulated synthesis is gone. The next person starts with raw transcripts, not the layered understanding you built. Every research cycle reinvents the same wheel.
What MemoryLake Does Differently
Permanent interview records
Conversation Memory stores every interview session, synthesis pass, and annotation as a permanent, searchable record. You can query across dozens of interviews in natural language: "What did enterprise users say about onboarding friction?" retrieves the relevant excerpts instantly.
Validated, conflict-checked insights
Fact Memory stores your validated research findings with version history and conflict detection. When a new interview contradicts an established insight, MemoryLake flags the conflict rather than silently overwriting it. Your insights accumulate with integrity.
Reusable research frameworks
Skill Memory stores your interview guides, synthesis methods, and analysis frameworks as reusable workflows. A new team member can load your established research process in seconds — same rigor, no ramp-up time.
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How It Works
- Connect — Integrate MemoryLake with your AI tools via MCP or REST API. Import existing research documents from Google Drive, Dropbox, or your team's storage using native integrations.
- Structure — Interview transcripts go into Conversation Memory. Validated insights go into Fact Memory. Research methodologies and interview guides go into Skill Memory. Each type is optimized for how that information gets used.
- Reuse — When a new research cycle starts, your AI already knows your validated insights, your methodology, and your historical findings. Every new interview builds on everything you've already learned.
Before & After
| Without MemoryLake | With MemoryLake | |
|---|---|---|
| Returning to research after a break | Re-read all notes, rebuild mental model before prompting AI | Query Conversation Memory for instant synthesis recap |
| Handing off to a colleague | Export notes, write handoff docs, explain context verbally | Colleague queries the shared memory directly — no handoff doc needed |
| Contradictory interview findings | Manually track conflicts in spreadsheets or sticky notes | Fact Memory flags conflicts automatically when new data contradicts existing insights |
| Running the same research process again | Reconstruct your interview guide and analysis method from scratch | Load it from Skill Memory — exact methodology, ready to run |
Built For
UX researchers, product researchers, and market researchers who conduct ongoing customer interviews and need their AI to retain the full body of findings — not just the most recent session. Also useful for research operations teams that need to standardize methodology across multiple researchers and ensure institutional knowledge doesn't leave with individual team members.
Try this use case on your own account
A research team keeps its customer interview notes in shared memory, so its assistant can answer "what did Dana say about the onboarding checklist?". Two of eight participants withdraw consent, and the team does what it should: deletes their notes in every project and checks that search finds none of their words. Then someone asks the assistant about them. MemoryRouter attaches by topic, so a question about Dana brings back another participant's notes on the checklist, and the model puts those words in her mouth; when nothing is attached at all, it answers "Yes, Dana Reyes … can be quoted". A record of the deletion that holds none of what they said fixes it for the people it names: a consent register, or a short notice under each deleted file's name. A name nobody ever interviewed still gets someone else's words.
What you will see
- Eight interview notes imported into three projects; the two who withdraw are deleted from all three with
proj doc deleteandlib delete, andsearch --types documentfor their names and their own phrases finds 0 left, in every run - What each project keeps in their place: nothing, the team's consent register (who withdrew and when, do not quote), or a notice uploaded under each deleted file's own name; a checker (
router messages) shows MemoryRouter attaching other participants' notes, or nothing, to a question about someone with no notes - Eight questions, 3 answers each through
router chat --boundary(gpt-4o-mini), over 8 runs: about the two who withdrew, notes deleted 0 of 96 right (48 "yes, quote them", 26 someone else's words, 22 invented), with the register 89 of 96, with the notices 90 of 96, and every remaining miss a call with nothing attached; a name never interviewed 0 of 72 in all three; the controls 207 of 207 - Plus
whoandstudysubcommands and a local web UI that marks whose notes each wrong answer borrowed from
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/consent-withdrawal-memory export MEMORYLAKE_API_KEY=sk-… python3 demo.py # or: python3 web/server.py
Watch it run · real, unedited recordings
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Frequently asked questions
Can multiple researchers access the same memory?
Can multiple researchers access the same memory?
Yes. MemoryLake supports shared team workspaces with role-based access control. You decide who can read, write, or administer each memory. Researchers can contribute to a shared pool of findings while maintaining appropriate access boundaries.
How do I import existing research documents?
How do I import existing research documents?
MemoryLake integrates directly with Google Workspace, Dropbox, and common storage systems. You can also import documents directly — MemoryLake's D1 Engine handles complex PDF and Excel files, extracting structured information automatically.
Does MemoryLake work with my current AI tools?
Does MemoryLake work with my current AI tools?
Yes. MemoryLake works with ChatGPT, Claude, Gemini, Perplexity, and any model accessible via API. If your team uses different AI tools, the memory layer is shared — the same research context is available regardless of which model a researcher prefers.