Foundations That Keep Patient Data Safe
Build and test healthcare models instantly with synthetic datasets and secure, on-prem evaluations designed for privacy, performance, and realism.
Learn how it works →High-fidelity healthcare data for AI research, training, and testing
Build and test AI models faster and more reliably with realistic, privacy-preserving patient data.
Better models and AI systems, faster
Quantiles synthetic data lets you build healthcare AI with realism, privacy, and speed, without changing your existing stack.
- Cut data acquisition and compliance overhead while enabling rapid iteration cycles across AI research and production.
- Generate patient cohorts with controlled demographics, clinical variables, and outcome distributions for richer model training and evaluation.
- Create custom datamixes with synthetic and real-world data to stress-test models, simulate edge cases, and improve model safety and robustness, while ensuring sensitive data never leaves your system.
Evals That Scale
From small-scale prototypes to production-ready models, Quantiles evaluates and monitors healthcare AI applications to ensure they perform safely, fairly, and reliably.
Explore Evaluation Tools →Start benchmarking with one line of code
Build and validate healthcare AI systems securely and reproducibly using benchmark-validated synthetic patient data optimized for privacy, accuracy, and cross-environment performance.
Data
Patient data that mirrors clinical records
Synthetic datasets replicate the statistical structure of real EHRs, including codes, labs, medications, and longitudinal patient journeys, and are validated against real-world datasets like MIMIC to ensure fidelity and downstream utility.
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PHI-Free and Compliant
All patient records are synthesized de novo to eliminate re-identification risk and ensure HIPAA- and NIST-aligned data for building and testing healthcare AI systems.
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One-line Integration
Load eval-ready synthetic datasets using the open source SDK or CLI and integrate them seamlessly into existing ML pipelines.
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