Organizations face challenges in acquiring and utilizing real-world data due to privacy regulations, data scarcity, and the need for secure data sharing. Traditional data anonymization techniques often compromise data utility, hindering AI model training, software testing, and collaborative research.

Crowdruption offers a synthetic data platform that generates privacy-compliant data mirroring real-world datasets without exposing sensitive information. The platform utilizes Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) to create high-quality synthetic data that retains the statistical properties, nuances, and complexities of real data. This enables organizations to enhance AI/ML model development by upscaling rare patterns, mitigating biases, and injecting domain knowledge. The generated data facilitates secure data sharing, improves software testing by creating realistic replicas of customer data, and supports AI explainability by providing an audit trail for transparent AI decisions.

Synthetic data generation using Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs)

Differential privacy to ensure privacy-friendly and GDPR-compliant data

Data augmentation capabilities to expand small datasets and balance minority class representation

Simulation of data by altering distributions and filling in missing data points

Facilitation of data sharing and collaboration among organizations

Enhancement of AI/ML model development by addressing data scarcity and bias

Streamlining of software testing by creating realistic, privacy-compliant replicas of customer data

Improvement of AI/ML fairness and explainability by providing an audit trail for transparent AI decisions

The primary target audience includes organizations in finance, healthcare, retail, IoT, and robotics that require privacy-compliant data for AI/ML model training, software testing, and secure data sharing.