Quantiles provides a unified observability platform for healthcare AI models, offering secure, privacy‑preserving evaluation, drift detection, and reproducibility tracking through a JSON/FHIR API, Python SDK, and PyTorch connectors. The service delivers real‑time monitoring via a dashboard, CLI, and version‑tracked runs, supporting cloud, hybrid, and on‑prem deployments to meet compliance and data residency requirements.
Funding
Funding not disclosed
Founders
Product
Problem
Healthcare AI models often lack transparent, continuous evaluation and monitoring, making it difficult to ensure accuracy, detect drift, and maintain compliance with patient privacy regulations. Without a unified observability framework, developers must piece together disparate tools, leading to inconsistent benchmarking and increased risk of unsafe model behavior in clinical settings.
Solution
Quantiles offers a unified observability platform that enables developers to evaluate, monitor, and optimize AI models used in healthcare applications. The service provides secure, privacy‑preserving data environments where patient data can be processed without exposing raw information. Built‑in metrics cover accuracy, drift detection, and reproducibility, while experiment‑level tracking ensures results are transparent and reproducible. Integration is achieved through a JSON‑ and FHIR‑compatible API, an open‑source Python SDK, and native PyTorch connectors, allowing seamless embedding into existing ML pipelines. The platform supports cloud, hybrid, and on‑prem deployments, giving teams full control over data residency and compliance. A web dashboard, CLI, and version‑tracked evaluation runs provide continuous insight into model performance throughout development and production.
Target Audience
Primary customers are machine‑learning engineers, data scientists, and AI product teams building clinical decision support or diagnostic models, as well as healthcare organizations that require compliant monitoring of AI systems.
Features
- Secure, privacy‑preserving data environment with federated evaluation capabilities
- Built‑in healthcare‑specific benchmarks and standard AI metrics for accuracy, drift, and reproducibility
- One‑line integration via JSON/FHIR API, open‑source Python SDK, and native PyTorch connectors
- Dashboard, API, and CLI access for real‑time monitoring and automated workflow integration
- Version‑tracked evaluation runs and custom benchmark creation, including LLM judge evaluations
- Enterprise‑grade security features such as RBAC with granular permissions and audit/compliance reporting
- Scalable usage limits with pay‑as‑you‑go pricing for additional storage and sample processing