Why SciencePilot
AI should strengthen scientific judgment — not hide it.
SciencePilot is designed around discrete research jobs: planning experiments, checking calculations, improving manuscripts, synthesizing literature, shaping grants, and anticipating peer review.
Every workflow keeps the researcher in control. Inputs remain visible, outputs are editable, assumptions are surfaced, and evidence-sensitive tasks explicitly preserve numerical data and citations.
Four principles behind every tool.
Inputs stay visible
Nothing is hidden behind the model. What you put in is always in view alongside what comes out.
Outputs stay editable
Every result is a starting draft you own and refine — never a locked black box.
Assumptions are surfaced
Evidence-sensitive tasks explicitly preserve numerical data and citations.
Provider-agnostic
Choose a commercial model, an institutional gateway, or a local deployment — no rebuild required.
Ready to work with a verification layer?
Spin up a private workspace and keep your research yours.