For teams building medical AI
Physician-in-the-loop validation,
from data to model
Five ways to put verified bilingual clinical judgment into your pipeline. Same physician network, one clinical standard — pick the one that solves your problem.
Request a demoConsensus datasets
Single-annotator labels are impossible to defend when a regulator asks "how do you know this is correct?"
Every case is labeled independently by multiple bilingual MDs, then reconciled by statistical consensus. You get an audit-ready dataset with the agreement level attached to every label.
How it works
- Double-blind: annotators never see each other's verdicts
- Disagreements surfaced and adjudicated, not averaged away
- Delivered as CSV with per-item consensus and confidence
Verified physician workforce
Building an in-house clinical annotation team takes months and generic labeling vendors don't have real medical judgment.
Deploy dedicated pods of verified bilingual MDs for annotation, RLHF, clinical NLP and transcription review — trained on your guidelines and ready in days.
How it works
- Bilingual MDs across Latin America, C1+ English
- Part-time (10–20h) or full-time (40h) capacity
- Physician-in-the-loop review on every batch
Clinical RLHF & preference data
A clinical LLM that sounds confident but is subtly wrong is a liability. Off-the-shelf raters can't catch medical errors.
Physicians rank and critique model outputs with real clinical criteria — building the preference and reward data that makes a medical model safe to ship.
How it works
- Response ranking and error-spotting by MDs
- Bilingual coverage for EN + ES clinical use cases
- Rationale captured, not just a thumbs up/down
Validation analytics (no PII)
You need to prove your training data is reliable — but you can't expose patient data or annotator identities to do it.
Aggregated, PII-free analytics on annotator quality and inter-rater agreement — the evidence your data is trustworthy, safe to share with buyers and auditors.
How it works
- Chance-corrected agreement (not raw match rates)
- Reliability broken down by clinical axis
- Aggregated workforce metrics with zero PII
Credential-verified talent
Self-reported résumés are unverifiable. One unlicensed "MD" in your pipeline is a compliance and reputational risk.
Every physician's license is verified against national medical registries with document review and anti-fraud checks — so "MD" actually means MD.
How it works
- License checked against national registries (14 countries)
- Document + identity review by a human, not just OCR
- One-license-per-account anti-fraud enforcement
Run a measured pilot
Send us a sample sprint and we'll benchmark cost and accuracy against your current workflow — you keep the data and the results.