Purpose-built for Healthcare AI & Medical LLM Teams
Your Model Is Only As Good As The Humans Who Trained It.
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.
Medical LLMs & Foundation ModelsClinical Decision Support AIHealth Chatbots & CopilotsDiagnostic AIDrug Discovery AIDigital TherapeuticsHealthcare Agentic Systems
About Dritiva
About Dritiva
Dritiva is an early-stage healthcare AI operations company focused on bringing qualified human expertise into the development and evaluation of medical AI systems.
We support AI startups, healthcare technology companies, research organizations, and clinical teams with:
Clinical data annotation
RLHF and preference ranking
Safety and red-teaming review
Research participant operations
Clinical workflow feedback
Our goal is to help teams generate more clinically grounded datasets and evaluation workflows while maintaining clear quality and confidentiality processes.
How We Plug Into Your AI Pipeline
You Build. We Supply the Human Signal.
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.
01
📋
Share Your Task Spec
Tell us what your model needs: RLHF pairs, eval rubrics, red-team prompts, annotation schema, or SFT examples
02
🎯
Annotator Matching
We assign credentialed clinicians matched to your specialty — cardiologists, oncologists, pharmacists, nurses, patients
03
✅
Quality & COI Screening
Every annotator is credential-verified, COI-screened, and calibrated on your task guidelines before going live
04
⚙️
Feedback Collection at Scale
Annotation tasks, preference ranking, response scoring, red-team prompting, or adversarial testing — run in parallel across our pool
05
📦
Structured Output Delivery
JSONL, CSV, or API — preference pairs, eval scores, red-team logs, labeled datasets — ready for your training or fine-tuning run
What AI Teams Outsource To Us
The Exact Work Your In-House Team Shouldn't Be Doing
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.
⚡
RLHF Preference Data — Physician-Grade
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.
🔬
SFT Dataset Creation & Medical Annotation
Domain experts write gold-standard clinical examples, annotate medical text, validate synthetic data, and label diagnostic content for supervised fine-tuning pipelines.
🔐
Red-Teaming & Adversarial Safety Testing
Clinicians systematically probe your model with adversarial medical prompts — surfacing hallucinations, dangerous dosage errors, contraindication failures, and bias patterns before your users find them.
📊
Model Evaluation & Clinical Benchmark Scoring
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.
🧪
Real-Patient Edge Cases for Model Robustness
Actual patients across 10 disease areas interact with your product and surface failure modes that synthetic data and internal testing completely miss.
Methodology
How Matching & Review Works
Precision sourcing, not panel spam. Every participant moves through the same four-stage process before touching a live project.
1
Screening
Initial fit and specialty check against project requirements.
2
Credential Review
Verification of licensure, specialty, and relevant experience.
3
Project Training
Written guidelines and calibration on the specific task at hand.
4
Quality Review
Sample-based checks and escalation for uncertain or high-risk cases.
Project-specific onboarding, written guidelines, sample-based quality checks, and escalation for uncertain or high-risk cases.
What We Do
Six Ways AI Teams Outsource To Dritiva
Every service maps to a real gap in your AI development workflow — from the first training run to post-market model monitoring.
⚡
RLHF & Preference Data at Clinical Grade
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.
Preference PairsDPO / PPO DataReward ModelingRLHF
🔬
SFT Dataset Creation & Medical Annotation
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.
SFT ExamplesClinical AnnotationSynthetic Data QANER & Labeling
🔐
Red-Teaming & AI Safety Testing
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.
Real-World Edge CasesUX Failure LogsDisease-Specific PanelsInteraction Data
🛡️
Clinical Trial & Post-Market Research Support
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.
Site FeasibilityPatient Pre-ScreeningPhase IV RecruitmentRWE & HEOR
Who We Serve
If You Are Building AI in Healthcare, We Are Your Feedback Layer
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.
🤖
Medical LLM & Foundation Model Labs
Clinical preference data at scale
SFT datasets across specialties
Physician red-team panels
Benchmark creation & scoring
Safety eval before release
🏥
Clinical Decision Support AI
Specialist output evaluation
Workflow integration testing
EHR interaction annotation
Diagnostic accuracy auditing
Regulatory evidence generation
💬
Health Chatbots & AI Copilots
Patient interaction red-teaming
Empathy & tone evaluation
Medication query stress testing
Multi-turn conversation scoring
Real-user edge case capture
🔬
Drug Discovery & Biopharma AI
KOL panels for hypothesis validation
Clinical trial AI feasibility
Expert annotation of biomedical data
Pharmacovigilance AI testing
Real world evidence support
🩻
Diagnostic & Imaging AI
Radiologist annotation panels
Ground truth label validation
Inter-rater reliability studies
Edge case pathology recruitment
Clinical reader studies
📱
Digital Therapeutics & Remote Care AI
Patient usability & acceptance testing
Caregiver interaction studies
Behavioral data annotation
Chronic disease panel testing
Post-market outcome studies
Annotator Pool
Your Pre-Built Clinical Annotation Workforce
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.
🫀
CardiologistsInterventional & General
🧠
NeurologistsNeuro & Psychiatry
🎗️
OncologistsMedical & Surgical
🩺
EndocrinologistsDiabetes · Thyroid
🏥
Hospital AdministratorsPurchase & Operations
⚗️
Clinical ResearchersCRO · Academic
⚖️
Regulatory ExpertsPV · Medical Affairs
💊
PharmacovigilanceDrug Safety Professionals
Patient Data Layer
Real Patient Behavior Is What Makes Your Model Production-Ready
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.
10
Disease Panels
5,000+
Verified Patient Participants
72hr
Panel Deployment Time
95%
Verification Rate
Diabetes & Endocrinology
Cardiovascular Diseases
Oncology & Cancer
Neurology & Mental Health
Dermatology
Respiratory & Pulmonology
Women's Health
Rare & Orphan Diseases
Gastroenterology
Infectious Diseases
Insights for AI Teams
The Practitioner's Guide to Human Feedback in Healthcare AI
Tactical frameworks and real-world thinking for AI product teams, ML engineers, and clinical AI founders navigating the human-in-the-loop challenge.
Hallucinated drug dosages, contraindication blindness, demographic bias in triage — a taxonomy of healthcare AI failure from structured red-team sessions.
Why 1,000 physician-written examples often outperform 100,000 crowdsourced ones for clinical instruction fine-tuning — and how to spec the task correctly.
The failure modes that only emerge when actual patients with real health literacy gaps, comorbidities, and emotional states interact with your AI product.
Dritiva is building a multidisciplinary leadership team across healthcare operations, research, AI quality, and strategic partnerships.
Clinical Network
Clinical Association & Expert Panel
Dritiva collaborates with licensed healthcare professionals and subject-matter experts who may contribute to consultation, workflow review, domain feedback, and project-specific guidance.
Important: Clinical experts participate based on the requirements of specific engagements. Listing an expert does not imply full-time employment, regulatory endorsement, or responsibility for all Dritiva projects.
Clinical Advisors
Dr
Dr. [Full Name]
Internal Medicine
Mumbai, India
Provides domain feedback for medical content review and evaluation workflows.
Dr
Dr. [Full Name]
Cardiology
Delhi, India
Supports consultation on cardiovascular content and clinical reasoning review.
Dr
Dr. [Full Name]
Endocrinology
Bengaluru, India
Contributes expertise for diabetes and metabolic health annotation tasks.
Dr
Dr. [Full Name]
Radiology
Hyderabad, India
Advises on imaging-related terminology and workflow considerations.
Expert Panel
Dr. [Name] — Emergency MedicineDr. [Name] — PediatricsDr. [Name] — NeurologyDr. [Name] — PsychiatryDr. [Name] — OncologyDr. [Name] — Public HealthDr. [Name] — Clinical ResearchDr. [Name] — Medical Documentation
Expert participation is engagement-specific and may include consultation, guideline review, quality feedback, or educational input depending on the scope of work.
Example
Illustrative Engagement Example
A representative walkthrough of how a typical engagement runs, start to finish.
4 weeks
Engagement
12
Clinicians
2,400
Rankings
Scenario
A healthcare AI startup needed clinician preference rankings for model-generated answers across multiple internal-medicine topics.
Support Provided
Reviewer onboarding
Preference-ranking guidelines
Quality review workflow
Weekly delivery batches
Outcome
The client received structured rankings and reviewer feedback that supported internal evaluation and model-refinement decisions.
Illustrative example for process explanation.
Update
Recent Company Update
Dritiva recently announced a collaboration with SI-Insights to support participant recruitment and data-collection activities for a multi-country AI research engagement.
Everything healthcare AI teams ask before their first project with us.
Dritiva is your outsourced human feedback and AI evaluation layer. We deploy verified clinicians, patients, and domain experts to generate RLHF preference pairs, build SFT datasets, red-team clinical AI outputs, and run model evaluations — formatted and delivered directly into your training pipeline. We work with medical LLM teams, clinical decision support developers, health chatbot builders, and biopharma AI teams that need clinical-grade human signal without building an internal annotation operation.
Share your model's task type and target specialty. We select credential-verified clinicians matching that specialty, apply COI screening, and run a calibration sprint. Annotators then generate preference pairs, error labels, and ranked outputs in DPO/PPO-compatible JSONL format. A calibrated cohort is typically live within 48 hours of scoping a project.
Yes — this is what Dritiva is built for. Our annotator pool includes 10,000+ credentialed physicians, nurses, pharmacists, regulatory experts, and patient advocates. Unlike general annotation platforms, every annotator is specialty-matched, credential-verified, and task-calibrated to your AI use case before data collection begins.
Standard deployment is 48 hours from project scoping to active annotation — covering annotator selection, specialty matching, COI screening, NDA execution, and task calibration. For complex multi-specialty projects or FDA-evidence-grade data collection, we recommend a 1-week calibration sprint before full-scale production.
We maintain verified patient and caregiver panels across 10 disease areas: Diabetes & Endocrinology, Cardiovascular Diseases, Oncology & Cancer, Neurology & Mental Health, Dermatology, Respiratory & Pulmonology, Women's Health, Rare & Orphan Diseases, Gastroenterology, and Infectious Diseases. Every participant is identity-verified and condition-confirmed before any project begins.
Dritiva applies confidentiality and data-handling practices based on the requirements of each engagement. Access is limited to approved project participants, and workflow expectations are defined contractually. Mutual confidentiality agreements can be established before project discussions or data sharing, and BAA discussions are available for engagements involving protected health information. Relevant research and quality practices may be informed by applicable healthcare and clinical research standards depending on the project scope — clients remain solely responsible for their own regulatory strategy and submissions.
Trust
Privacy, Security & Confidentiality
How we handle data and confidentiality across engagements.
Project-specific data handling
Dritiva applies confidentiality and data-handling practices based on the requirements of each engagement. Access is limited to approved project participants, and workflow expectations are defined contractually.
NDA-ready engagements
Mutual confidentiality agreements can be established before project discussions or data sharing.
BAA discussions available
For engagements involving protected health information, Business Associate Agreement discussions are available where applicable.
Standards-informed workflows
Relevant research and quality practices may be informed by applicable healthcare and clinical research standards depending on the project scope.
Your Next Training Run Needs Better Human Signal.
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 our team.
We're a lean, high-impact team sitting at the intersection of clinical medicine and AI. If you want to shape how the next generation of healthcare AI is trained, evaluated, and made safe — we want to hear from you.
Senior Role
Engagement Manager
Remote · IndiaFull-Time5–8 yrs exp.
Own client engagements end-to-end — from first scoping call to delivery sign-off. You'll be the primary point of contact for healthcare AI companies, translating their model development challenges into structured human feedback programs.
What You'll Do
Lead discovery conversations with ML teams, clinical AI leads, and regulatory affairs at client organisations to scope RLHF, annotation, red-teaming, and evaluation programs
Design engagement structures: annotator profile, task taxonomy, calibration approach, delivery timeline, and quality thresholds — in coordination with the delivery team
Own project P&L, resource allocation, and milestone tracking across 3–6 simultaneous client engagements
Manage senior client relationships, escalation resolution, and expansion conversations
Translate clinical and regulatory requirements (FDA SaMD, HIPAA, ICH-GCP) into practical engagement constraints for annotator and delivery teams
Contribute to Dritiva's methodology — documenting what works across engagements and building repeatable playbooks
What You Bring
5–8 years in consulting, professional services, or client-facing roles in healthcare, life sciences, or technology
Demonstrated ability to manage complex, multi-stakeholder projects in regulated environments
Familiarity with AI/ML concepts — you don't need to write code, but you need to discuss RLHF, fine-tuning, and model evaluation credibly with ML engineers
Strong written and verbal communication; comfortable presenting to C-suite and senior technical leads
Experience in a startup or high-growth environment preferred; ability to operate without structure
Background in healthcare, pharma, biopharma, or medical technology strongly preferred
Why This Role
Direct exposure to the most consequential AI deployment challenge of the decade — healthcare
Speak directly with our leadership team; no management layers between you and decisions
Opportunity to grow into a leadership role as Dritiva scales its client portfolio
Work directly on the design and delivery of human feedback programs for healthcare AI clients. You'll sit between the client team and the annotator pool — structuring tasks, calibrating annotators, monitoring quality, and surfacing insights that improve model behaviour.
What You'll Do
Design annotator briefs and task specifications for RLHF preference collection, SFT dataset creation, model evaluation rubrics, and red-teaming sessions across clinical specialties
Run calibration and onboarding sessions with physician and patient annotators; monitor inter-rater reliability and flag calibration failures
Conduct quality reviews on delivered annotation batches — reviewing preference pairs, rubric scores, and red-team findings for consistency, clinical accuracy, and guideline compliance
Synthesise annotator findings into actionable client reports: failure mode summaries, dataset quality assessments, benchmark gap analyses
Support the Engagement Manager on client-facing deliverables, status updates, and scoping for follow-on programs
Maintain project documentation — data dictionaries, annotation schemas, QC logs — to Dritiva's methodology standards
What You Bring
2–5 years of experience in consulting, research operations, clinical research, or data/AI project delivery
Understanding of how AI/ML models are built and evaluated — exposure to annotation tools, RLHF concepts, or NLP evaluation frameworks is a strong plus
Domain familiarity in healthcare, pharmaceuticals, or life sciences; ability to read and interpret clinical content credibly
Detail-oriented with strong analytical and written communication skills
Comfortable managing ambiguity and iterating quickly in a startup environment
Experience with structured data collection, survey design, or qualitative research methods preferred
Why This Role
Hands-on exposure to the full lifecycle of healthcare AI training — from task design to model-ready dataset delivery
Rapid skill development across clinical domains, AI methodology, and regulatory frameworks (FDA, HIPAA, ICH-GCP)
The entry point into Dritiva's delivery team. You'll support the design, coordination, and quality control of human feedback programs for healthcare AI clients — learning the full stack of clinical AI evaluation from the ground up, fast.
What You'll Do
Assist in drafting annotator briefs, task instruction documents, and calibration materials under the guidance of Consultants and the Engagement Manager
Coordinate annotator scheduling, onboarding logistics, and session documentation for physician and patient panels
Perform first-pass quality checks on annotation batches — checking for completeness, schema adherence, and obvious calibration errors before senior QC review
Maintain project trackers, data logs, and delivery dashboards to ensure engagements stay on timeline
Research clinical guidelines, regulatory frameworks, and domain knowledge relevant to active client engagements (e.g., specialty-specific standard of care, FDA SaMD guidance updates)
Support preparation of client-facing reports, slide decks, and delivery summaries
What You Bring
0–2 years of experience; strong candidates from healthcare, life sciences, public health, or related fields welcome to apply at any experience level
Genuine curiosity about AI and how large language models are built and evaluated — no coding required, but intellectual comfort with technical concepts is essential
Strong attention to detail; ability to follow complex structured processes accurately and flag inconsistencies
Clear written communication; able to produce structured, professional documents from scratch
Highly organised; comfortable managing multiple parallel workstreams with competing deadlines
Degree in medicine, pharmacy, nursing, public health, biotechnology, life sciences, or a related field preferred — or a non-healthcare degree paired with demonstrable AI/data interest
Why This Role
Rare opportunity to enter the healthcare AI training space at the ground floor of a firm building the category
Learn directly from our leadership and senior team — steep learning curve, real ownership from day one
Structured growth path toward Consultant and beyond within 12–18 months