Clearbox AI generates high-quality, privacy-compliant synthetic datasets to accelerate AI development and innovation. This data replaces sensitive real-world information, enabling organizations to overcome data scarcity and improve machine learning model performance. Their solutions ensure GDPR compliance while preserving data utility for analytics and research.
Funding
$400K 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
Many organizations struggle with data governance challenges related to AI model development, including limited access to sensitive data, data retention policies, and insufficient data quality. Traditional anonymization techniques can degrade data utility, while the cost of data collection and labeling for AI projects remains high.
Solution
Clearbox AI provides a human-centric AI dataset platform that leverages synthetic data generation to address these challenges. The platform enables organizations to unlock sensitive data, improve model performance, and streamline software testing by generating high-quality, artificial data that reflects the statistical properties of real-world datasets. Clearbox AI's Enterprise Solution offers a fully dockerized environment that can be installed on-premise or in the cloud, allowing users to generate synthetic data from structured data sources such as relational databases and data warehouses. The platform also includes data connectors for seamless integration with existing infrastructure, as well as data profiling and preparation tools to enhance the quality of synthetic datasets.
Target Audience
Clearbox AI targets data scientists, analysts, data engineers, innovation managers, compliance officers, and data protection officers who need to overcome data limitations, improve AI model performance, and ensure data privacy.
Features
- Synthetic data generation for tabular, relational, time-series, geo, and sequential datasets
- Data connectors for relational databases (mySQL, PostgreSQL, Oracle, Microsoft SQL Server) and data warehouses (BigQuery, RedShift, Snowflake, Databricks)
- Data profiling and preparation tools for dataset documentation and data testing
- Automated machine learning approach for finding the best generative architecture for each task
- Data cloning to generate proxy datasets with the same statistical properties as the original
- Data augmentation to rebalance datasets by increasing the instances of a minority class
- Quality reports containing utility metrics and privacy metrics
- Python SDK for integrating synthetic data into existing processes
- Web-based frontend for interacting with the Enterprise Solution