Give Healthcare Researchers AI That Retains Clinical Context Across the Full Study Lifecycle

A clinical researcher is three months into a systematic review. They use AI to help synthesize literature, identify gaps, and draft sections — but every session starts from scratch. They re-upload prior summaries, re-establish the PICO framework, and re-explain the inclusion criteria before doing any new work. When a junior researcher joins mid-study and uses a different AI model, there's no shared research context to draw from. Six months of AI-assisted literature work lives in closed chat sessions nobody can search.

40M+ Medical Papers Built In, No Upload Required — MemoryLake includes indexed access to PubMed, arXiv, and bioRxiv so AI sessions draw on current literature without manual import. Search across 500K+ clinical trials and 2M+ FDA and DrugBank drug records as part of your standard research workflow.

Conflict Detection for Contradictory Findings — Fact Memory stores synthesized research findings with source attribution and built-in conflict detection. When a new study contradicts a prior finding you've logged, MemoryLake flags the discrepancy — giving you an explicit record of where the evidence is in tension rather than silently overwriting it.

Research Continuity Across the Full Study Lifecycle — Conversation Memory makes every AI-assisted research session permanently searchable. Retrieve the literature synthesis from month two, the protocol decision rationale from the IRB prep, or the interim analysis discussion — any time, in any future session.

1. Connect — Link your AI tools (Claude, ChatGPT, Gemini, or any model via API endpoint) via MCP or REST API. MemoryLake's built-in medical datasets are available immediately — no data import required for PubMed, clinical trials, or FDA records.
2. Structure — Study protocol decisions and design rationale go into Fact Memory with versioning. Literature synthesis sessions go into Conversation Memory. Key findings and evidence summaries go into Fact Memory with conflict detection and source attribution.
3. Reuse — When you or a colleague opens a new AI session at any point in the study lifecycle, the full prior research context is immediately available — protocol history, synthesized literature, flagged contradictions, and open questions.

MemoryLake is built for healthcare researchers, clinical researchers, and medical scientists who conduct extended studies, systematic reviews, or clinical trials — and need AI research context to persist across months, team members, and model switches. It's particularly useful for teams doing systematic reviews where literature volume exceeds what any context window can hold, multi-site studies where research context needs to be shared across investigator teams, and researchers whose work requires documented provenance and audit trails for regulatory or publication purposes.