Use case
Private AI for unpublished research documents
Use AI over sensitive lab material without treating public chatbots as the default destination for unpublished work.
Who it is for
Labs evaluating AI for pre-publication results, proposals, internal protocols, or restricted collaborations.
Why this matters
Public AI tools are convenient, but unpublished research needs a system where data-control, source grounding, and deployment choice are explicit.
What it helps with
Reduce the temptation to paste sensitive documents into public chat tools.
Choose managed, BYO-key, or local/self-hosted operation based on the lab's review process.
Keep answers tied to internal evidence rather than open-web context.
How a pilot would use it
- Start with a non-confidential or approved pilot corpus.
- Validate citation quality, refusal behavior, and access boundaries.
- Move to BYO-key or local/self-hosted deployment for stricter data-control requirements.
Example question
What did our unpublished validation note conclude about the revised assay?
The note concluded that the revised assay reduced background signal after incubation was shortened, while preserving the same buffer composition. It did not evaluate long-term stability.
Trust and controls
- Private by design does not mean upload everything immediately; scope the first corpus deliberately.
- Sensitive document text is not written to default logs.
- Security posture and known limitations should be reviewed before sensitive uploads.
Current Early Access limits and provider-mode disclosures are documented on the known limitations page.
Start with a focused lab corpus
The strongest pilot starts with documents your lab already trusts: protocols, core papers, theses, project notes, and recurring questions.