Betterdata provides state-of-the-art synthetic data generation infrastructure for enterprise AI and data intelligence applications. The platform utilizes multi-model engines, including Tabular Foundation Models and GANs, to create high-quality, privacy-preserving datasets. This enables organizations in regulated industries to accelerate AI/ML training, system testing, and secure data sharing while maintaining compliance.
Funding
$2.4M 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.
EFFTPAFounders
Product
Problem
Organizations face challenges in sharing and utilizing sensitive production data due to increasing data protection regulations. Traditional anonymization techniques are often insufficient, leaving data vulnerable to re-identification attacks and hindering data-driven innovation. This results in slower product development cycles, restricted data collaborations, and increased compliance costs.
Solution
Betterdata provides a programmatic synthetic data platform that transforms sensitive production data into privacy-preserving, highly realistic synthetic data. This approach enables faster and safer data access for product development, testing, and collaboration across teams and organizations. By replacing real data with artificially created data, Betterdata ensures compliance with data protection laws, reduces the risk of re-identification, and accelerates data-driven innovation. The platform facilitates the identification and removal of biases in data, leading to fairer AI models and improved data quality.
Target Audience
Betterdata targets organizations in financial services, telecommunications, healthcare, and retail that require secure and compliant data sharing for product development, data collaboration, and machine learning.
Features
- Generation of programmable synthetic data that mimics the statistical properties of real data
- Privacy-by-design approach to eliminate privacy risks associated with sensitive data
- Bias detection and mitigation tools to ensure fairness in AI models
- Data privacy verification to screen synthetic data for potential threats
- Intelligent data augmentation to improve machine learning performance
- API and web application interface for easy integration and deployment
- On-premise deployment option for enhanced data security