For teams building medical AI
Verified physicians to train, align and evaluate your medical AI
Generic annotators don't have clinical judgment. In-house physicians don't scale. DataLaps puts credential-verified, bilingual MDs in your pipeline — for annotation, RLHF, red-teaming and model evaluation — with double-blind consensus as the proof.
One physician network, five ways to use it
What verified physicians can do for your model
Clinical annotation & labeling
Specialty-matched MDs label your data in parallel; double-blind consensus gives you a defensible inter-annotator agreement metric, not one person's guess.
- Imaging, clinical NLP, EHR, transcripts
- Bilingual EN + ES coverage
- Per-item agreement attached to every label
RLHF & preference data
Physicians rank and critique model outputs with real clinical criteria — the preference and reward data a medical model needs before it ships.
- Response ranking and error-spotting by MDs
- Rationale captured, not just thumbs up/down
- Reference answers written by physicians
Red-teaming & adversarial evaluation
MDs probe your model for clinically unsafe answers — the failures generic raters can't recognize — so you find them before your users do.
- Adversarial prompts from real clinical edge cases
- Severity-tagged findings with clinical rationale
- Bilingual coverage for EN + ES failure modes
Model evaluation & benchmark
A physician panel scores your model's outputs against independent clinical judgment — an evaluation set you can show advisors and buyers.
- Independent verdicts, then consensus
- Chance-corrected agreement, not raw match rates
- PII-free, shareable evaluation report
Consensus datasets · Made to order
Prefer to order rather than build? Get consensus-labeled clinical datasets with the agreement level attached to every row — produced for your cases and delivered as an audit-ready file.
- Independent MD verdicts reconciled by consensus
- Confidence interval + chance-corrected agreement
- CSV / JSONL with a datasheet and integrity hash
Why physician-grade
The alternative to generic data vendors
Generic annotation vendors
- ✕Crowdworkers with no medical license
- ✕Self-reported résumés, unverifiable
- ✕Single-annotator labels, no agreement metric
- ✕Can't recognize a clinically unsafe answer
DataLaps
- Licensed MDs, credentials human-reviewed
- Registry cross-checks where registries allow
- Double-blind consensus with a defensible metric
- Real clinical judgment on every batch
The mechanism
How the consensus works
Verified physicians
Every annotator is a licensed MD, credentials checked by a human — and cross-checked against official registries where available.
Double-blind consensus
Multiple physicians label independently; agreement is measured and disagreements are adjudicated, not averaged away.
Defensible deliverable
You receive labeled data plus a statistical agreement metric — the evidence clinical advisors and auditors look for.
This is what you receive
Not a raw spreadsheet — a verifiable deliverable with a folio, a confidence interval and full traceability. See the exact format on a synthetic, PII-free sample.
Start a pilot
Tell us what your model needs
Run a measured pilot — send a sample sprint, keep the data and the results.