Privacy-preserving synthetic patient datasets for ML development, clinical research, and healthcare analytics.
Skip months of data access approvals. Generate privacy-guaranteed patient cohorts in minutes.
Differential privacy guarantees that individual patients cannot be identified, even with auxiliary information
Generate thousands of synthetic patient records in minutes, not months of approval processes
Preserves complex medical relationships, comorbidities, and demographic distributions from real data
Pharmaceutical companies, hospitals, and digital health startups accelerating innovation
Projected reduction in clinical trial design time
Generate diverse patient populations for trial simulation, optimize inclusion criteria, and predict enrollment challenges before investing millions in actual trials.
Potential annual savings from faster research for a large hospital system
Train and validate medical AI models on unlimited synthetic patient data. Perfect for rare diseases where real data is scarce.
More training data available
Faster QA testing cycles
Test edge cases, develop features, and ensure HIPAA compliance without touching real patient data.
From idea to publication with synthetic healthcare data
Accelerate research timelines, enable collaboration across institutions, and publish faster with privacy-compliant datasets.
Differential privacy guarantees with support for FHIR R4, HL7, and custom formats
Our proprietary deep learning models understand complex medical relationships, ensuring synthetic data maintains clinical validity.
# Generate privacy-preserving synthetic cohort
response = k01.generate(
condition="Type 2 Diabetes",
cohort_size=10000,
demographics={"age_range": [45, 75]},
privacy_epsilon=1.0
)
Differential privacy ensures that no individual patient can be identified, even with auxiliary information.
// Privacy-preserving optimization
minimize: KL(P_real || P_synthetic)
subject to: privacy_loss ≤ ε
causal_structure preserved
RESTful APIs, FHIR compatibility, and SDKs for Python, R, and JavaScript. Deploy on-premise or use our cloud.
Data Categories
Formats
Every synthetic dataset is automatically validated for statistical fidelity, privacy compliance, and clinical accuracy.
Built for startups, ready for enterprise
Proof-of-concept and initial development
Active development teams
Large-scale deployments
Universities and research institutions
PhDs from Imperial College London with 40+ published papers
CEO & Co-founder
ML researcher with experience at Samsung AI and Evidation Health. Specialized in predictive models for digital health monitoring using wearable sensors.
CTO & Co-founder
Computer vision specialist from Meta-acquired Scape Technologies. Expert in large-scale synthetic data generation for ML systems.
Join leading healthcare organizations using K01 to accelerate innovation