Aetion Generate utilizes generative AI to produce privacy-preserving synthetic datasets from real-world and clinical data, enabling organizations to share and analyze sensitive information without compromising privacy. This technology addresses the challenge of accessing and utilizing valuable data while mitigating re-identification risks and enhancing dataset completeness for improved insights.
Funding
$110M 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 face challenges in sharing and analyzing sensitive real-world and clinical data due to privacy concerns and the risk of re-identification. Traditional de-identification methods often remove or obfuscate valuable attributes, limiting the utility of the data for research and insights.
Solution
Aetion Generate leverages generative AI to create privacy-preserving synthetic datasets that closely mimic the statistical properties of real-world and clinical data. This technology enables organizations to share and analyze sensitive information without compromising patient privacy or regulatory compliance. By generating synthetic data, Aetion Generate enhances dataset completeness, addresses data bias, and augments data to overcome size limitations, ultimately accelerating research, improving insights, and driving business value. The platform offers tools for risk assessment, data transformation, and synthetic data generation, ensuring secure and compliant data sharing across and beyond the enterprise.
Target Audience
Aetion Generate is designed for biopharmaceutical companies, MedTech organizations, data providers, security teams, and R&D teams seeking to leverage real-world data while adhering to stringent privacy regulations.
Features
- Generates synthetic datasets from RWD, RCT, and other sources using generative AI
- Automates privacy risk assessments and applies statistically defensible de-identification techniques
- Assesses privacy risk of standalone or linked data and informs expert determinations
- Deploys in cloud or on-premise environments with auto-scaling capabilities
- Offers three access options: Generate UI, Python Software Development Kit (SDK), and APIs
- Provides audit-ready outputs and jurisdiction-specific compliance
- Enables data augmentation and modeling for data-limited populations