CuraOps AI is an open-source, synthetic-data-driven MLOps QA framework. We enable hospitals and health-tech teams to validate, monitor, and deploy clinical AI models with zero PHI exposure.
Hospitals want to deploy AI, but critical roadblocks prevent safe adoption.
Testing AI models on real patient data violates HIPAA and exposes institutions to massive legal liability.
AI accuracy degrades over time as patient demographics change, leading to dangerous clinical hallucinations.
Legacy EHR systems and modern AI endpoints frequently fail to communicate, causing critical data pipeline breaks.
A standardized, privacy-first testing pipeline that allows health-tech teams to validate clinical AI models using 100% synthetic data.
Generates realistic, de-identified patient histories using Synthea™. Guarantees zero PHI exposure during the entire QA phase.
Pre-configured Great Expectations validation suites enforce medical boundary checks (e.g., heart rate 40-180 bpm) before data hits the model.
Integrates MLflow and automated pytest suites to detect model drift, validate API latency (<200ms), and ensure continuous reliability.
An end-to-end, cloud-agnostic MLOps pipeline optimized for healthcare compliance.
Synthea™ generates HL7 FHIR synthetic patient records.
Great Expectations enforces clinical safety boundaries.
FastAPI tests the AI model's predictions and latency.
MLflow tracks distributions and alerts on model decay.
CuraOps AI directly addresses the "fundamental lack of engineering approach" to synthetic data identified in recent peer-reviewed literature (Pileggi & Liu, 2026). By providing an open-source, pre-production QA framework, we accelerate safe AI adoption, reduce clinical risk, and ensure strict HIPAA compliance for hospitals nationwide.
CuraOps AI is architected by a seasoned QE Lead with 10+ years of experience in API and mobile testing, holding an MS in Information Systems and a Post-Graduate degree in Artificial Intelligence. Currently transitioning into Azure AI/MLOps Architecture, [Your Name] built this framework to bridge the gap between rigorous software quality assurance and the high-stakes demands of clinical AI deployment.
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