Mathematically Private
Synthetic Healthcare Data

Generate HIPAA-compliant synthetic data in hours. Backed by ε-differential privacy with provable privacy guarantees.

Age distribution retention in synthetic cardiac dataset with differential privacy noise
HIPAA Compliant
SOC 2 Type II
FHIR R4 Native
FDA Ready

The High Cost of Data Friction

$360B
Unrealized annual savings
18 mo
Avg. data sharing delay
60%
AI projects fail due to data access

Engineered for Enterprise Healthcare

01

Accelerate AI without privacy risk

Deploy machine learning models faster with synthetic data that maintains statistical accuracy while ensuring mathematical privacy guarantees.

02

Enable collaboration at scale

Share data across departments, with research partners, and vendors—without exposing real patient information or triggering compliance reviews.

03

Meet regulatory requirements

Built for healthcare compliance from day one. HIPAA-aligned with audit-ready documentation that satisfies the most stringent security requirements.