
Foundations That Keep Patient Data Safe
Build and test healthcare models instantly using secure evaluation workflows with strong data controls, auditability, and performance safeguards.
Learn how it works →Develop and test healthcare AI systems with full observability and a secure, privacy-preserving data environment.
Get early accessfrom quantiles.sdk import createSuite, runEvaluation, exportPackage
suite = createSuite({
id: 'cardio_readmission',
cohorts: ['cardio_v2'],
tasks: ['risk_prediction'],
metrics: {
discrimination: ['auroc'],
calibration: ['calibration'],
threshold: ['sensitivity', 'specificity', 'ppv', 'npv'],
clinicalUtility: ['net_benefit']
},
judges: [
{
name: 'explanation_quality',
appliesTo: ['risk_explanation'],
type: 'llm_judge',
rubric: [
'grounded_in_patient_data',
'clinically_reasonable',
'appropriate_uncertainty',
'actionable_next_step',
'no_harmful_recommendation'
]
}
],
slices: ['gender', 'race_ethnicity', 'payer_type'],
})Secure data environments, built-in evaluations, and flexible integration for reliable healthcare AI performance.
Build, fine-tune, and benchmark models on your in-house datasets, synthetic data, or custom mixes, with full traceability and dataset versioning built in.
Security & Privacy →Continuously evaluate model performance, accuracy, and drift with integrated metrics and experiment-level observability, enabling transparent, reproducible, and interpretable research for safe operation in healthcare environments.
Evaluations & Benchmarks→Integrate seamlessly with your ML pipelines through our JSON- and FHIR-compatible API, open-source Python SDK, or native PyTorch integrations — and deploy across cloud, hybrid, or on-prem environments with full observability and control.

Build and test healthcare models instantly using secure evaluation workflows with strong data controls, auditability, and performance safeguards.
Learn how it works →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 →Built-in healthcare-specific and industry-standard AI benchmarks for rigorous, unified evaluations.
Designed for healthcare and AI engineering teams, enabling real-time data, seamless integration, and scalable pipelines for training, evals, and everything in between.
Developed with HIPAA and NIST standards in mind, ensuring privacy, security, and transparent lineage, so you can trace every benchmark to its exact model version, training data, and evaluation set.
Features research-validated, healthcare-specific benchmarks, so you can easily measure reliability, safety, and fairness across datasets and deployments.