Dedomena provides a platform for data anonymization and synthetic data generation, ensuring compliance with data protection regulations while maintaining data utility. The technology enables businesses to create high-quality, statistically similar datasets for testing, validation, and AI model improvement, significantly reducing project timelines and costs.
Funding
$530K 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 leveraging data for innovation due to privacy regulations, data restrictions, and biases present in real-world datasets. Traditional anonymization techniques often compromise data utility, while data sharing between departments or organizations remains a complex and risky endeavor.
Solution
Dedomena provides a platform for generating synthetic data that mirrors the statistical properties of real data without containing any personally identifiable information. This enables businesses to access high-quality, production-like datasets for testing, validation, and AI model training while ensuring compliance with data protection laws like GDPR. The platform offers tools for data anonymization, synthetic data generation, and pre-built AI models, allowing users to extract insights, enrich information, and securely share data assets. By using synthetic data, organizations can accelerate innovation, improve model performance, reduce risks and costs, and boost collaboration.
Target Audience
Dedomena targets data-driven businesses, data scientists, AI developers, and organizations that require secure and compliant data for innovation, testing, and AI model development.
Features
- Data Anonymization: Advanced techniques that preserve data value while ensuring privacy.
- Synthetic Data Generation: Creation of statistically similar, production-like data copies without identifiable information.
- Pre-built AI Models (Neurons): Ready-to-use models for extracting insights and value from data.
- Secure Data Sharing: Enables compliant data sharing between departments and organizations.
- Bias Reduction: Balances datasets to improve the accuracy of machine learning models.
- Accelerated Testing: Expedites testing and QA cycles.
- Reduced Time-to-Insight: Improves data processes and reduces AI development costs.