Generate privacy-preserving, high-fidelity synthetic datasets at scale to accelerate model development, testing, and analytics without exposing real user data.
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ARC v2 synthesizes structured, tabular, and image data with configurable privacy guarantees and fidelity controls — enabling safe data sharing and robust model training.
Learn moreARC v2 delivers tailored synthetic datasets across multiple domains with privacy controls, data validation, and seamless integration into ML workflows.
Produce realistic, statistically representative datasets that preserve analytic value while protecting privacy.
Granular privacy and compliance controls including differential privacy, k-anonymity tuning, and audit logs.
Synthesize tabular, time-series, and image data with domain-aware models and schema preservation.
Expand your training datasets with synthetic examples to reduce overfitting and improve model robustness.
APIs, SDKs, and connectors to plug synthetic data directly into training and analytics pipelines.
Scalable deployments, role-based access, and security certifications for enterprise production use.
ARC v2 balances utility and privacy so teams can accelerate ML development, share data safely, and meet compliance requirements.
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