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NEWLLM-as-a-judge Evaluations

The fast, safe foundation to evaluate and monitor healthcare AI

Develop and test healthcare AI systems with full observability and a secure, privacy-preserving data environment.

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from quantiles.sdk import createSuite, runEvaluation, exportPackage

suite = createSuite({
  id: 'cardio_readmission',
  cohorts: ['synthetic_cardio_v2'],
  tasks: ['risk_prediction'],
  metrics: ['auroc', 'brier', 'calibration'],
  slices: ['sex', 'race_ethnicity', 'payer_type'],
})
Terminal · quantiles eval
Built by technology and healthcare experts from
  • Stanford Medicine
  • Microsoft
  • Genentech
  • OpenAI
  • Biomarin
  • First Databank
  • PayPal
  • UNM Health
  • Stanford Medicine
  • Microsoft
  • Genentech
  • OpenAI
  • Biomarin
  • First Databank
  • PayPal
  • UNM Health
Evaluation Infrastructure

A unified observability platform to evaluate and optimize AI systems powering healthcare applications.

Secure data environments, built-in evaluations, and flexible integration for reliable healthcare AI performance.

Secure Data Environment

Build, fine-tune, and benchmark models on your in-house datasets, synthetic data, or custom mixes, with full traceability and dataset versioning built in.

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Evaluate & Refine

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.

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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.

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Build and test healthcare models instantly using secure evaluation workflows with strong data controls, auditability, and performance safeguards.

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From small-scale prototypes to production-ready models, Quantiles evaluates and monitors healthcare AI applications to ensure they perform safely, fairly, and reliably.

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Built to adapt and scale with your ML pipelines, from prototype to production.

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Purpose-built for Healthcare

Built-in healthcare-specific and industry-standard AI benchmarks for rigorous, unified evaluations.

AI-native Infrastructure

Designed for healthcare and AI engineering teams, enabling real-time data, seamless integration, and scalable pipelines for training, evals, and everything in between.

Privacy & Compliance at Every Step

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.

Observability & Evaluation

Features research-validated, healthcare-specific benchmarks, so you can easily measure reliability, safety, and fairness across datasets and deployments.