Solutions / 03 · Reinforcement learning
Expert judgment, at post-training scale.
Judgment from the people who do the work. Credentialed specialists on retainer across physics, medicine, law, engineering, and finance: preference pairs, rubrics, evaluations, red-team data.
900+
Vetted experts
14
Disciplines
0.87
Median agreement
72h
Panel spin-up
The data engine for post-training.
Credentialed, not crowdsourced
Degrees and licenses verified for every panelist; disciplines matched to your domain.
Calibrated agreement
Gold-task calibration every 50 items; inter-rater agreement shipped per batch.
Rationales attached
Every verdict carries a written justification your reward model can learn from.
Panels on retainer
Standing panels spin up on your rubric in 72 hours and scale with volume.
Access at scale
Real credentials. Not crowdwork.
Every expert is credential-verified, trained on your rubric, and calibrated against gold tasks. Agreement is measured and shipped with the data.
01
Physicists
PhD-level problem construction, derivation checking, and response ranking.
120+ PhDs
02
Physicians
Diagnosis and treatment preference data from licensed, practicing doctors.
60+ MDs
03
Lawyers
Contract, case, and regulatory reasoning scored by practicing attorneys.
80+ JDs
04
Engineers
Code, systems, and safety reviews from senior working engineers.
300+ seniors
05
Accountants
Audit, tax, and disclosure evaluation by certified professionals.
90+ CPAs
What we produce
Preference pairs
Ranked responses with rationales, calibrated against gold tasks.
Rubrics & evals
Domain rubrics authored by experts, then applied at volume.
Expert CoT
Step-by-step reasoning traces written by credentialed specialists.
Red-team data
Adversarial probes and failure documentation in high-stakes domains.
Use case
Verdicts with the why attached.
What a buyer sees before licensing a single comparison.
RESPONSE A · FLAGGED
Sign error at step 4: drops the metric contraction in the second variation.
RESPONSE B · PREFERRED
Valid derivation; elegant use of the Ward identity at step 6.
Preference pairs
Rankings teach what. Rationales teach why.
Every comparison ships with the expert's written reasoning, an error taxonomy, and a calibration score: reward-model-ready.
Measured, not promised
Expert data compounds.
Post-training runs are evaluated before listing: SFT baseline, with preferences, and with rationale-augmented rewards.
Panels in session.
Standing, compensated, calibrated, across 14 disciplines.
01
Physics panel
PhDs ranking derivations and constructing gold problems.
Derivations · Gold problems
02
Physician panel
Licensed doctors scoring diagnosis and treatment plans.
Diagnosis · Treatment plans
03
Legal panel
Practicing attorneys evaluating contract reasoning.
Contracts · Rubric scores
Stand up a panel
Your eval framework, our experts.
Send the domain, rubric, and volume. We assemble a credentialed panel, calibrate it on your gold set, and deliver preference data with measured agreement, typically live within two weeks.
2 wks
Panel live
0.85+
Target agreement
100%
Credential-verified
Per-item
QC'd pricing
Catalog / Reinforcement learning
All verticals ↗Physics PhD preference pairs
Graduate-level problem sets with ranked model responses, error taxonomies, and step-by-step corrections.
Legal reasoning evaluations
Practicing lawyers scoring contract analysis and case reasoning: rubric-based, with written justifications.
Clinical decision RLHF panels
Physician panels producing preference data on diagnosis and treatment plans, built to your eval framework.
Code review preference data
Senior engineers ranking model-written patches: correctness, safety, style, with inline comments.
Accounting & audit evaluations
CPAs scoring model outputs on reconciliation, disclosure, and audit reasoning tasks. QC pass in progress.
Red-team & safety panels
Domain experts probing for confident errors, unsafe advice, and jailbreaks in high-stakes domains.