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Build, test, and benchmark faster

Streamline AI model testing and benchmarking with transparent, reproducible evaluations across synthetic and real-world datasets.

Unify data, model, and benchmarks in one evaluation flow

Unify data, models, and evaluations in a transparent and auditable pipeline that accelerates healthcare AI development, deployment, and monitoring.

  1. DatasetEasily start with Quantiles’ synthetic patient data or securely connect your datasets while preserving data residency and preventing raw-data exposure.
    Patient DataMix
    NameIDConditions
    Jeffrey Byrd76825Asthma
    Dylan Clark33624Diabetes, Hypertension
  2. AI ModelExecute and version models in controlled environments for consistent, reproducible inference and training.
  3. EvaluationsEvaluate performance across datasets and model iterations with full lineage and benchmark comparability.
    MODEL: CodeBlue
    BenchmarkPrompt APrompt B
    Hash7f82d90db9e05a4c
    Accuracy0.860.93
Benchmarks

Configurable Evaluation Framework

Create benchmarks effortlessly from built-in, custom, or hybrid evaluations, designed to match your research and product goals.

Benchmarks

Your benchmarks have been added

Performance

Run the full benchmark suite and compute each primary metric...

Accuracy

Compare model outputs to ground truth using task-specific scorer...

Latency

Measures time to first byte and total completion per request...

Evaluations
Reproducible Evaluations with Full Lineage

Each evaluation is fully traceable, capturing dataset versions, model configurations, parameters, and metrics in one place. Compare runs across datasets, reproduce experiments, and verify benchmark outcomes with end-to-end lineage tracking from data to model to results.