For funders
See the whole field
Query the live graph of a field you fund and see which claims are well supported, which are contested, and which questions still have no answer. See more than what's in the spotlight.
Catch dropped lines of insight
When a line of work stalls, the graph shows where it stopped: the question that never got an answer, the proposed experiment still waiting for a lab.
Spot bottlenecks and emerging trends
The shape of the record shows where a field is stuck and where momentum is building, years before citation counts catch up.
Recognize the full breadth of contributions
Credit attaches to each claim, question, and piece of evidence a researcher adds. Work that would once have vanished into a co-author line can be recognized and rewarded.
Forecast where a field is going
A provenanced record is one you can compute on. When people or agents draw new conclusions from it, every inference traces back to vetted evidence.
For labs
A shared map across labs
Collaborating labs work from a shared graph of claims, evidence, and requests. That shared map gives them the precision to take on questions too big for any single lab.
Surface your unknown unknowns
Structuring work as claims, evidence, and requests reveals what's already latent in your research: the insights you haven't named and the questions you didn't know you were asking.
Assisted peer review
Most of peer review is untangling what a paper claims and what its evidence shows. In the graph that structure is explicit, so AI can carry a real share of the work.
Granular attribution
Every record is signed by the person who made it. However many labs share a project, each researcher can point to the exact contributions that are theirs.
AI as a trusted collaborator
A graph of claims and evidence is the rules of science in machine-readable form, and agents run far better on it than on cluttered PDFs. They can navigate and add to the record while human contributions stay plainly in evidence.