Keep Every Customer Research Insight, Across Every AI Session

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.

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.

1. 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.
2. 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.
3. 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.

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.