Dritiva is your outsourced human feedback partner for healthcare AI. We deploy 10,000+ verified clinicians, patients, and domain experts to generate RLHF data, run model evaluations, red-team clinical outputs, and build the annotation pipelines your AI team can't do in-house.
We work with AI teams building in
Most AI teams hit the same wall: you need high-quality domain feedback at scale, but hiring and managing clinical annotators in-house is slow and expensive. We are that layer — already built, already verified.
Tell us what your model needs: RLHF pairs, eval rubrics, red-team prompts, annotation schema, or SFT examples
We assign credentialed clinicians matched to your specialty — cardiologists, oncologists, pharmacists, nurses, patients
Every annotator is credential-verified, COI-screened, and calibrated on your task guidelines before going live
Annotation tasks, preference ranking, response scoring, red-team prompting, or adversarial testing — run in parallel across our pool
JSONL, CSV, or API — preference pairs, eval scores, red-team logs, labeled datasets — ready for your training or fine-tuning run
Recruiting, vetting, and managing clinical annotators is a full-time operation. We've already built it. You plug in your task — we return structured human signal your model can train on.
Board-certified clinicians compare model response pairs and generate ranked preference data for DPO, PPO, or reward model training — in your format, at your volume.
Domain experts write gold-standard clinical examples, annotate medical text, validate synthetic data, and label diagnostic content for supervised fine-tuning pipelines.
Clinicians systematically probe your model with adversarial medical prompts — surfacing hallucinations, dangerous dosage errors, contraindication failures, and bias patterns before your users find them.
Run structured evals where specialists score your model on accuracy, safety, clinical appropriateness, and reasoning quality — giving you eval data you can defend to stakeholders and regulators.
Actual patients across 10 disease areas interact with your product and surface failure modes that synthetic data and internal testing completely miss.
Precision sourcing, not panel spam. Our proprietary scoring engine surfaces the highest-quality participants for every engagement.
NLP-based semantic matching across 25+ medical specialties ensures we don't just match keywords — we match expertise depth.
Years of clinical practice, research publications, and procedure volume are log-normalized to reward quality over quantity.
Every participant carries a performance score from prior projects — rewarding reliability, insight quality, and responsiveness.
Real-time availability windows and timezone alignment ensure projects launch without scheduling friction.
Automated COI detection flags industry affiliations, competitor relationships, and regulatory conflicts before every engagement.
Every service maps to a real gap in your AI development workflow — from the first training run to post-market model monitoring.
We run preference ranking sessions with physicians, nurses, and pharmacists on your model's outputs. They compare response pairs, rank by clinical accuracy, flag harmful content, and generate the reward signal your training pipeline needs — delivered as JSONL or CSV.
Domain experts write high-quality clinical instruction-response pairs, annotate medical records and literature, validate AI-generated content, and build the labeled datasets your supervised fine-tuning runs demand. From clinical notes to drug monographs.
Clinicians run structured adversarial probing on your model — generating edge-case prompts, testing drug-drug interactions, checking diagnostic reasoning errors, and identifying demographic bias patterns. You get a red-team report and a curated failure dataset.
We deploy specialist panels to score your model on custom eval rubrics — clinical correctness, safety, empathy, reasoning quality, and regulatory alignment. Results feed directly into your model card, pre-deployment checklist, or FDA/CE submission evidence.
Real patients across 10 disease areas interact with your AI product and expose the edge cases your synthetic test sets miss — rare symptom combinations, health literacy gaps, medication confusion, emotional distress responses. Delivered as structured usability and interaction logs.
For AI companies whose products are entering regulatory pathways or supporting clinical research — we provide investigator identification, patient pre-screening across 10 disease panels, pharmacovigilance cohort recruitment, and Phase IV real-world evidence generation.
From foundation model labs to clinical AI startups to pharma companies deploying AI-assisted drug development — every team that builds in healthcare needs domain-verified human signal.
Building a medical annotation team from scratch takes months and burns runway. Ours is already assembled, credentialed, and task-calibrated — across 25+ specialties and 10 disease areas. You assign a task, we deploy the right people within 48 hours.
Synthetic data can't replicate how a Type 2 diabetic with low health literacy actually interacts with your chatbot — or how a caregiver managing a rare disease child misreads your AI's output. We put real patients in front of your model and capture structured failure data across 10 disease panels.
Tactical frameworks and real-world thinking for AI product teams, ML engineers, and clinical AI founders navigating the human-in-the-loop challenge.
What to tell physicians before a preference-ranking session — rubric design, calibration tasks, and disagreement handling for medical outputs.
Read Article →Hallucinated drug dosages, contraindication blindness, demographic bias in triage — a taxonomy of healthcare AI failure from structured red-team sessions.
Read Article →The problem with MedQA and USMLE-style evals for clinical AI: what they miss, and how expert-scored rubric evals produce more defensible results.
Read Article →Why 1,000 physician-written examples often outperform 100,000 crowdsourced ones for clinical instruction fine-tuning — and how to spec the task correctly.
Read Article →The human evaluation evidence the FDA expects for AI/ML-based Software as a Medical Device — and how to generate it systematically.
Read Article →The failure modes that only emerge when actual patients with real health literacy gaps, comorbidities, and emotional states interact with your AI product.
Read Article →Everything healthcare AI teams ask before their first project with us.
Tell us your model, your task type, and your target specialty. We'll scope a pilot and have annotators calibrated within 48 hours. No procurement process — talk directly to the co-founder.