VEIL.AI provides the BONSAI platform, an anonymization and synthetic data engine that converts raw health records into high‑utility, privacy‑preserving datasets. It offers record‑level anonymization, variable‑level privacy tuning, and automatic risk‑and‑quality reporting to meet GDPR, HIPAA, and other regulations, enabling safe AI/ML training, research, and data sharing for pharma, biotech, and healthcare organizations.
Funding
$1.3M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Organizations in healthcare, pharma, and research need to use large volumes of sensitive patient data for AI development, clinical studies, and data sharing, but strict regulations such as GDPR, the EU AI Act, and the European Health Data Space limit access and expose them to privacy‑risk and compliance penalties.
Solution
VEIL.AI offers the BONSAI platform, a next‑generation anonymization and synthetic data engine that transforms raw health records into high‑utility, privacy‑preserving datasets. The system operates in three phases—configuration of privacy and quality criteria, automated record‑level anonymization (or synthesis), and automated risk‑and‑quality reporting—allowing users to retain statistical properties while eliminating personal identifiers. It supports both static files and continuous data streams, integrates with common data models (e.g., OMOP) and cloud or on‑premise environments, and produces compliance‑ready reports that satisfy GDPR, HIPAA, EHDS, and related standards. The resulting anonymized data can be safely used for AI/ML training, real‑world evidence generation, cross‑border collaboration, and secondary analysis without exposing individuals.
Target Audience
Primary customers are pharmaceutical and biotech companies, research hospitals, universities, and health data authorities that need to share or analyze patient‑level health data while meeting strict privacy regulations.
Features
- Record‑level anonymization that preserves granular data for rare‑disease cohorts and small sample sizes
- Variable‑level privacy and quality tuning, accelerating processing by orders of magnitude
- Automatic re‑identification risk analysis and data‑quality comparison reports
- Support for continuous data streams and longitudinal health device feeds
- Synthetic data generation that mirrors original statistical distributions for AI training
- Compatibility with DataFrames, OMOP CDM, and Snowflake native app for seamless pipeline integration
- Deployable as SaaS or customer‑hosted (cloud/on‑premises) to meet diverse security requirements
- Built‑in validation tools ensuring regulatory compliance (GDPR, HIPAA, EU AI Act, EHDS)