Researchers don't lack information. They lack a clear path through what the field has published, what their lab has tried, and what is worth doing next.
The evidence exists. The path forward is scattered.
Published papers
persists
Google Drive
persists
GitHub repos
persists
Zotero / PDFs
persists
— offboarding → onboarding —
Lab notebooks
lost
Meeting notes
lost
Failed experiments
lost
Why methods changed
lost
Important context leaves with the person, not with the project.
The result: every new project starts by reconstructing the past instead of building from it.
NeuriCo gives the next researcher a way to build forward, not start over.
NeuriCo
Public research literature
What has the field already done?
Plug in the sources labs already use for discovery.
Semantic Scholar
papers
OpenAlex
citations
PubMed + arXiv
methods
PDFs + Zotero
context
NeuriCo connects public literature with private lab memory.
Not another place to put documents. The intelligence layer over Drive, GitHub, Zotero, Notion, protocols, notes, and experiment logs.
Source-backed research memory for what was tried, what failed, why decisions changed, and what gaps are worth pursuing next.
Public
→
Private lab memory
NeuriCo
Research memory workspace
Turns scattered artifacts into structured knowledge around hypotheses, methods, failures, decisions, and next studies.
what was tried
what failed
why it changed
what to test next
Google Drive stores your research. AI tools(Notion, NotebookLM) chat with it. NeuriCo remembers what it meant.
click once to reveal both knowledge streams
Private lab memory
What has our lab already tried?
Plug in the sources that already hold lab context.
Google Drive
drafts
GitHub
code + commits
Notion + protocols
decisions
Logs + notes
failures
Slack texts
handoffs
NeuriCo
Idea in. Context back.
Public Sources
Semantic Scholar
PubMed
arXiv
Zotero library
Lab Memory
Google Drive
GitHub / protocols
Notion notebooks
Experiment logs
Analyzing 847 records across both streams…
HYPOTHESIS
"Can adding shade help plants survive drought?"
Guidance mode
Try to reproduce related results
Public dataset
NOAA drought + plant stress data
→
Internal document
Greenhouse appendix.csv from last year's trial
Done
📄 Literature Summary
12 papers on shade and water stress
Light shade often reduces water loss
Gap: little work on young plants outdoors
Done
🔬 Similar Field Studies
Tomato and bean studies used partial shade
Best results came during midday heat
Too much shade reduced plant growth
Running
🏛 Lab History
Scanning old greenhouse notes…
Matching: drought, shade, seedling survival
Found 2 similar student projects
Done
⚠ Past Failures
Spring 2023: shade cloth tore in strong wind
Fall 2022: no full-sun control group
Notes say plants were already too dry at start
Done
🎯 Design Risks
Missing: equal watering across all groups
Missing: full-sun plants for comparison
Risk: shade may slow growth, not help survival
Open Gap
💡 Suggested Next Steps
Design A: full sun vs light shade vs heavy shade
Measure soil moisture and plant survival daily
Read first: greenhouse shade trial notes
NeuriCo
Why now. Why this. Why me.
Why now
Research output is exploding. AI is making it noisier.
Researchers need source-backed memory, not more generic AI text.
Why this
NeuriCo turns scattered knowledge into a guided research path.
Built for university labs, undergraduate research programs, and R&D teams where handoffs and hidden context slow the next experiment.
Why me
I have lived both sides of the research handoff.
I have inherited scattered projects, and now I'm trying to pass on my own work without losing the context behind it.
It is not magic memory. It is structured continuity.