GDPR-safe synthetic data for AI training
Structured health records, clinical transcripts, and imaging metadata — generated, annotated, and delivered at scale.
Train faster, safer, and at scale
Purpose-built synthetic data that maintains statistical fidelity while eliminating privacy risks.
Privacy by Design
All synthetic data generated using privacy-preserving techniques. No real patient data is used, ensuring compliance with GDPR, HIPAA, and other healthcare privacy regulations.
Rich Annotations
Labels baked in from day one: outcomes, diagnoses, urgency tags, and clinical context. Purpose-built for training high-performance healthcare AI models.
Scalable Delivery
From demo sets to 5M+ records, delivered securely via EU cloud infrastructure. Enterprise-grade security with full audit trails and compliance reporting.
Synthetic data that mirrors reality
Statistically equivalent to real healthcare data, but with zero privacy risk.
Structured Health Records
Complete patient profiles with demographics, symptoms, vitals, and outcomes
Clinical Transcripts
Realistic doctor-patient conversations with medical terminology, and medical conditions descriptions
Imaging Metadata
DICOM headers, scan parameters, and diagnostic findings, including body parts and indications
From requirements to deployment
Streamlined process for acquiring privacy-safe synthetic data at any scale.
Define your dataset
Specify your AI training requirements, data schema, and compliance needs. Our team works with you to define the exact structure and annotations required.
We generate synthetic data at scale
We generate fully synthetic data using probabilistic sampling, programmatic templates, and modality-specific rules. Clinical relationships and correlations are preserved and validated with internal statistical checks.
Secure delivery via EU cloud portal
Access your datasets through our enterprise-grade portal with full audit trails, compliance reporting, and secure download capabilities.
All data synthetic. No PHI. GDPR-compliant by design.
All outputs are synthetic. Free-text is scanned for potential PII patterns (regex by default; spaCy optional). Default policy: scan-only; real-risk hits trigger regeneration or redaction. Re-identification risk is evaluated using uniqueness and k-anonymity proxies and summarized in the Evidence Pack, alongside the PII real_risk_summary.
Evidence Pack & Governance
Each dataset ships with a transparent Evidence Pack and the safeguards teams need to move quickly.
Artifacts included
- schema.yaml and data-dictionary.csv for schema, normalization, and context.
- code-validation.json for ICD-10, CPT, LOINC, and other terminology checks.
- QA and re-identification summaries plus optional FHIR-lite NDJSON exports.
PII handling
Free-text fields are scanned for potential PII via regex (default) or spaCy NER. The default policy is scan-only—we regenerate or redact only when scans flag real-risk hits.
Every PII report contains a real_risk_summary, and governance notes ship with each Data Card for audit-ready context.