Build rigorous model evaluations with qualified people.
Darwin can recruit domain-aligned evaluators, calibrate them to a rubric, run blinded overlap and adjudication, verify acceptance criteria, and settle only accepted work.
Example deals
Start from a proven structure, then let Darwin clarify the exact terms with you.
Rank model answers against a rubric
Recruit calibrated evaluators to rank 2,000 model answers against this rubric. Use blinded overlap, capture the reason for each judgment, adjudicate disagreements, and report agreement quality.
Evaluation rubric.pdfStart this goalBuild a domain benchmark
Find qualified domain contributors to write difficult benchmark tasks, pay for a calibration set first, require independent review, and secure the rights to the accepted work.
Start this goalGrade agent trajectories
Recruit experienced reviewers to grade coding-agent trajectories for correctness, efficiency, tool use, and recovery. Require reproducible evidence for every failed grade.
Start this goalCollect preference data
Run a blinded comparison of two models on realistic tasks with qualified reviewers. Capture preferences, confidence, rationale, and subgroup differences without revealing model identity.
Start this goalRed-team an AI feature
Find independent testers with relevant product and safety experience. Define the allowed scope, require reproducible findings, triage severity, and verify fixes against the original cases.
Start this goalEvaluate multilingual quality
Recruit native reviewers across the target languages, calibrate hard terms and cultural criteria, then compare accuracy, usefulness, tone, and failure patterns by market.
Start this goalCreate a regression suite
Turn recent production failures into a versioned regression set with clear expected behavior, risk tiers, scoring, and reviewer guidance, then validate it against the current model.
Start this goalRun an expert adjudication panel
Assemble independent experts to review disputed evaluation items, disclose conflicts, compare reasoning, and return a documented final judgment with remaining uncertainty.
Start this goal