Give Biotech Teams AI That Carries Experimental Context Across Every Lab Session

A lab team is six weeks into a protein characterization study. The lead scientist uses AI to help interpret assay results, cross-reference literature, and draft protocol updates — but every session requires re-uploading the experimental summary, re-explaining the hypothesis, and re-establishing what the literature review has already covered. When a collaborating team at another site picks up the work, there's no shared AI context layer. They re-do literature work that's already been done and miss the interim findings the first team logged weeks ago.

40M+ Life Sciences Papers Indexed and Ready — MemoryLake includes built-in access to PubMed, arXiv, and bioRxiv — 40M+ papers available in every AI session without manual import. Cross-reference your experimental results against current literature without leaving your AI workflow.

D1 Engine Parses Lab Data Files Directly — The D1 Vision-Language Model handles complex PDF lab reports, Excel assay outputs, and instrument data files. Structured results are extracted and stored as persistent Fact Memory — no manual transcription of instrument outputs into documents.

Cross-Team Research Context That Persists — Shared team memory with role-based access control gives every lab member and collaborating team access to the same experimental history, protocol decisions, and literature synthesis — regardless of which AI model they're using or where they're located.

1. Connect — Link your AI tools via MCP or REST API. MemoryLake's built-in PubMed, arXiv, and bioRxiv access is available immediately. Connect Dropbox or Google Workspace for lab document and data file integration.
2. Structure — Experimental protocols and design decisions go into Fact Memory with versioning and conflict detection. Literature synthesis sessions go into Conversation Memory. Assay results and instrument data extracted by D1 Engine go into structured Fact Memory with source attribution.
3. Reuse — When any team member — at any site, using any AI model — opens a new session, they get the full experimental history, current literature coverage, and prior results. No re-briefing, no re-upload of prior summaries.

MemoryLake is built for biotech teams, life sciences researchers, and lab teams who conduct multi-week or multi-month experiments that require sustained AI research context across sessions, team members, and collaborating sites. It's particularly valuable for teams doing drug discovery or protein research where experimental data volume is high and literature cross-referencing is continuous, multi-site collaborations where research context needs to persist across organizational boundaries, and labs generating complex instrument data that needs to be parsed and stored as structured memory rather than living in raw files.