Use case
Private AI knowledge workflows for R&D teams
Evaluate a private, citation-grounded AI workflow over reports, experiments, technical notes, and design records.
Who it is for
Industry R&D managers and technical teams evaluating controlled AI over internal documents.
Why this matters
R&D teams need fast retrieval without weakening IP control, provider governance, or traceability.
What it helps with
Get source-grounded answers from internal technical documents.
Start with a controlled non-confidential corpus before broader rollout.
Choose BYO-key or local/self-hosted deployment when procurement requires it.
How a pilot would use it
- Stand up a controlled workspace and load a starter document set.
- Validate answer quality, citation traceability, and refusal behavior on real questions.
- Review provider mode, support, and security needs before expansion.
Example question
Which test report supports the revised tolerance range?
The revised range is supported by the June reliability report, which records passing results for the narrower tolerance under accelerated cycling.
Trust and controls
- Tenant and workspace access boundaries keep corpora separate.
- Provider ownership and cost responsibility are explicit.
- Platform-managed spend can be capped; BYO/local usage remains under your governance.
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.