01
Pharma R&D & Trial Design
Generate diverse patient populations for trial simulation. Refine inclusion criteria and predict recruitment challenges before investing millions in actual trials. 60% reduction in design time.
Backed by Icelandic Tech Development Fund
Realistic synthetic patients. Zero privacy risk. API-first.
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.
Generate diverse patient populations for trial simulation. Refine inclusion criteria and predict recruitment challenges before investing millions in actual trials. 60% reduction in design time.
Enable research teams to work with realistic patient data without privacy barriers. Large hospital systems report potential annual savings of $2.3M from faster research cycles.
Train and validate medical AI models on unlimited synthetic patient data. Particularly valuable for rare diseases where real data is scarce. 10x more training data available.
Test edge cases, develop features, and ensure HIPAA/GDPR compliance without touching real patient data. Teams see 85% faster QA cycles.
Accelerate research timelines and enable collaboration across institutions. Publish faster with privacy-compliant datasets. 3 months from idea to publication.
Differential privacy guarantees with support for FHIR R4, HL7, and custom formats
Our models learn the joint distribution across patient demographics, diagnoses, medications, procedures and observations. This multimodal approach captures the real clinical correlations that make synthetic data useful.
Generate complete patient records on demand via API. Every record is FHIR R4 validated. Configurable from simple demographics to complex multi-morbidity profiles.
Our models are trained with differential privacy guarantees. The output contains no real patient information and cannot be reverse-engineered to identify individuals.
Safe for public sharing, third-party access and demo environments. No PHI. No re-identification risk.
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.
Proof-of-concept and initial development
Active development teams
Large-scale deployments
Academic and research institutions: contact us for discounted rates
PhDs from Imperial College London with 40+ published papers
CEO & Co-founder
ML researcher with experience at Samsung AI and Evidation Health. Focused on predictive models for digital health monitoring using wearable sensors.
CTO & Co-founder
Computer vision specialist and PhD from Imperial College London. Expert in large-scale synthetic data generation for ML systems.
See how K01 can transform your healthcare data challenges.