Domain Fellows
Practitioners who use Raycaster on real or de-identified work, review outputs the way a peer would, and help define submission-grade acceptance conditions.
AI Fellows program →Experts · Fellows · Partners
Raycaster’s evaluation records are only as good as the domain judgment inside them. Fellows, practitioners, and specialist partners turn consequential workflows into tasks, rubrics, and verdicts—while protecting confidential material.
How Raycaster works for you
This is not a generic annotation marketplace. Contribution means capturing real professional work, specifying what “good” looks like, and adjudicating whether an agent actually completed it.
Roles
Practitioners who use Raycaster on real or de-identified work, review outputs the way a peer would, and help define submission-grade acceptance conditions.
AI Fellows program →Students in regulated and technical programs who learn AI-native professional practice while contributing structured review and example packets.
Campus Fellows program →Specialist shops and domain networks that supply representative workflows, adjudication, and QC at program scale—with clear confidentiality boundaries.
Partner intake →People who translate tacit standards into observable criteria, resolve grader disagreements, and keep holdouts honest.
Contribute judgment →The loop
Representative tasks, source packets, edge cases, and the decisions a competent practitioner would actually make.
Files, tools, and state become a reproducible workspace—usable for evaluation, training episodes, and regression.
Rubrics, graders, and expert review turn tacit standards into evidence another team can inspect and defend.
Selected traces can educate the market. Sensitive packets, recipes, and partner identities stay gated by design.
Confidentiality
Fellows programs use placeholders and sanitized examples—not your employer’s live dossier.
Partner identities, raw packets, and holdouts stay qualified or NDA unless you explicitly approve.
Participation can improve public benchmarks and your own private evaluation / deployment readiness.
Choose a path
Experts often do both: supply judgment for the ecosystem and build a private eval for their own team.
Private evaluation before you deploy—clear tasks, scoring criteria, and inspectable runs for your work.
Request an evaluation 02Expert data and realistic work environments for training and evaluating agents on hard professional tasks.
Talk about a project 03Practitioners and specialist firms help shape evaluations from real work—without becoming a labeling gig.
Explore contributionStart
After intake you’ll get a contribution guide: what packets look like, how review works, and which track—Fellows, campus, partner, or adjudication—fits.