Research Teams Need Shared Memory, Not Just Shared Documents
Individual researchers on the same team use different AI tools, reach different conclusions, and rarely have a structured way to reconcile what they've found. Knowledge fragments. Discoveries don't propagate. MemoryLake is the shared memory layer that keeps a research team's collective knowledge coherent.
Research Teams Need Shared Memory, Not Just Shared Documents
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The Memory Problem
A research team of six people might use ChatGPT, Claude, Perplexity, and Gemini across different projects and workstreams. Each researcher's AI context is private to their sessions. When two researchers independently work through overlapping territory, there's no system to detect contradictions in their conclusions or share what one has established with the rest of the team. The knowledge lives in individual AI sessions that disappear at session end.
What MemoryLake Does Differently
40M+ Research Papers Built In
MemoryLake ships with a built-in dataset of over 40 million academic papers from PubMed, arXiv, and bioRxiv. Researchers query the corpus directly from any AI session — no separate database access, no manual uploads.
Shared Fact Memory With Conflict Detection
When one researcher establishes a finding as a verified Fact, the whole team can see it. When a second researcher's session produces a contradictory finding, MemoryLake flags the conflict automatically before it spreads through the team's work.
Role-Based Access for Sensitive Research
Not all research is visible to the full team. Define access controls that reflect your actual organizational structure: principal investigators, associates, collaborators, and external reviewers can each have scoped access.
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How It Works
- Connect — Each team member links their AI tools to MemoryLake. The shared memory layer is visible to all members with appropriate access permissions.
- Structure — Research findings go into Fact Memory. Literature review threads go into Conversation Memory. Recurring methodologies and experimental protocols go into Skill Memory.
- Reuse — A new researcher joining the team starts with access to the team's full accumulated knowledge base — structured, versioned, conflict-checked memory that reflects what the team has actually established.
Before & After
| Without MemoryLake | With MemoryLake | |
|---|---|---|
| Knowledge sharing | Scattered across individual AI sessions | Shared Fact Memory visible to the whole team |
| Contradictory findings | Discovered late or not at all | Flagged automatically at the moment of conflict |
| New team member onboarding | Hours of briefings and document reading | Shared memory loaded from day one |
| Literature access | Separate database subscriptions | 40M+ papers built into the memory layer |
Built For
Academic research groups, corporate R&D teams, market research firms, policy research organizations, and any team where multiple people are using AI tools to generate and evaluate knowledge that needs to stay coherent across the group. MemoryLake is equally applicable to small lab groups and enterprise research organizations with hundreds of contributors. The MemoryLake-D1 Engine handles research documents that LLMs typically struggle with: multi-column PDFs, visually complex reports, and structured data exports from lab instruments or statistical tools.
Try this use case on your own account
A research team saves its result charts, a value printed on every bar, into shared memory. Saved as a PNG, a chart is indexed as a vision summary, and that is what MemoryRouter attaches: two or three of the printed values, the ones the summary mentions. Wrap the very same PNG in a one-page PDF — no redraw, no data file — and the chart becomes a figure whose printed labels are read value by value. MemoryRouter attaches them and the answers are right.
What you will see
- Two heat-pump survey figures (20 printed values) kept three ways, one project and one boundary each: PNG screenshots, the same PNG files wrapped in a PDF by a standard-library function in the demo, and unlabelled charts wrapped the same way; the research assistant is
router chat --boundary(gpt-4o-mini, temperature 0) search --types documentshows what the index read off each chart: the figure text was empty for every PNG and held every printed value for every wrapped PDF (20 of 20 imports each)- Six questions, 3 answers each, over 10 runs: PNG 27 of 180 right (the rest “not given” or half answers), the same PNGs in a PDF 175 of 180, unlabelled charts in a PDF 0 of 180 — with invented rankings and, twice, values the index estimated from bar heights (
~60for 31.6) - A checker call (Claude copies out what MemoryRouter attached) shows the asked values were attached 60 of 60 times for the PDF and 0 of 60 for the PNG, and separates the PDF's 5 misses as “attached and still wrong”. Plus a
figuresubcommand and 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/research-figure-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
How does conflict detection work in practice for research teams?
How does conflict detection work in practice for research teams?
When a Fact is added to the shared memory layer, MemoryLake checks it against existing Facts in the same domain. If there's a semantic contradiction — two research sessions reaching opposite conclusions — the system flags it and notifies the team. A researcher can then review the two findings, investigate further, and either update one Fact or create a versioned branch.
Does MemoryLake work with specialized research AI tools?
Does MemoryLake work with specialized research AI tools?
MemoryLake works with any AI tool via REST API or MCP integration, as well as with specific integrations for ChatGPT, Claude, Gemini, Grok, Perplexity, and others. If your research team uses specialized domain AI tools with API access, those can be integrated as well.
What compliance certifications does MemoryLake hold for handling research data?
What compliance certifications does MemoryLake hold for handling research data?
MemoryLake holds ISO 27001, SOC 2 Type II, GDPR, and CCPA certifications. All data is encrypted with AES-256 and end-to-end encryption. Your research data is not used for model training.