For AI labs
Expert evaluation data, without the noise
Generalist crowds plateau exactly where frontier models need the most help. Kamapathy gives your lab on-demand access to a vetted bench of practicing domain experts — for evaluation, preference data, demonstrations, and red-teaming.
labs@kamapathy.com — tell us what you are evaluating and we will follow up with next steps.
Why Kamapathy
Built for high-stakes training data
When a wrong label can mislead a frontier model, the bar for who produces it — and how it is checked — has to be higher.
Vetted domain experts
Physicians, engineers, lawyers, mathematicians, and more — each identity-verified, credential-reviewed, and assessed in their domain before they see a single task.
Calibrated quality
Rubric-based reviews, per-submission scoring, and revision loops keep signal high and rework low. Weak submissions are rejected or sent back, not passed through.
Full auditability
Every claim, submission, and review decision is audit-logged. You always know who produced each datum, who reviewed it, and how it scored.
Confidential by default
Project data is scoped to the experts assigned to it, under NDA, with role-based access controls across the entire pipeline.
What we support
Data your researchers actually asked for
Every engagement is scoped around your rubric and research goals — not forced into a one-size-fits-all task template.
Model evaluation & benchmarking
Structured expert grading of model outputs against your rubrics — correctness, safety, reasoning quality, and domain-specific failure modes.
Preference & ranking data
Pairwise comparisons and full rankings from experts who can actually tell a subtly wrong answer from a right one.
Expert demonstrations
Gold-standard reference answers, proofs, diagnoses, and code written by practitioners — the supervision data that generalist crowds cannot produce.
Domain red-teaming
Targeted probing for unsafe, incorrect, or non-compliant behavior in high-stakes domains like medicine, law, and cybersecurity.
Expert coverage across
Working together
How an engagement runs
From first call to delivered data, with QA and provenance built into every step.
- 01
Scope the project
Define domains, task types, rubrics, and volume with our team. We help translate your research goals into reviewable task specs.
- 02
Match the experts
We staff from the vetted pool by domain, seniority, and assessment performance — not by whoever clicks first.
- 03
Calibrate, then run
A pilot batch aligns experts and reviewers on your rubric before production volume, with continuous QA review throughout.
- 04
Deliver with provenance
You receive structured data with reviewer scores, feedback, and a complete audit trail for every item.
Tell us what you are evaluating
Share your domains, task types, and rough volume. We will come back with a proposed expert bench and a pilot plan.