AI Infrastructure & MLOps for Healthcare

Healthcare AI fails quietly in ways that matter: a prior authorization model that drifts as payer rules change, a summarization tool that starts dropping a medication when an EHR template updates, a clinical classifier that degrades on a population it was never evaluated against. Under HIPAA, you also cannot debug by exporting PHI to a vendor dashboard. MLOps closes that gap. We build evaluation, observability, and CI/CD that keep clinical AI accurate and safe over time, with human-in-the-loop checkpoints and PHI-safe monitoring that runs entirely inside your environment, so quality is measured continuously without ever moving protected health information out of your control.

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How we deliver it

AI Infrastructure & MLOps, built for healthcare

We build clinical evaluation sets reviewed by your clinicians, with golden references for the outputs that affect care, so every model change is scored for clinical accuracy before release.

We run PHI-safe monitoring inside your environment: drift, latency, and error tracking that surfaces aggregate quality signals without exposing patient records.

We wire human-in-the-loop review into the pipeline, capturing clinician overrides as labeled feedback that strengthens the next eval.

Every model change passes a CI/CD eval gate tuned to clinical thresholds, so updates cannot regress on safety-critical outputs.

Where it pays off in healthcare

Prior authorization monitoring

Track approval and denial accuracy as payer rules shift, with alerts when drift starts pushing decisions outside clinically reviewed thresholds.

EHR-change drift detection

Catch the moment an Epic or Cerner template update degrades extraction or summarization quality before it reaches a clinician.

Clinician override loop

Capture every human-in-the-loop correction as labeled data, turning real clinical judgment into the evaluation set for the next release.

PHI-safe observability

Monitor quality, latency, and cost on aggregate signals inside your environment, so HIPAA-protected records never leave your control.

Healthcare clients keep clinical AI inside reviewed safety thresholds release after release, often cutting prior authorization turnaround time while giving compliance a continuous, PHI-safe record of model quality.

Healthcare AI, answered

Monitoring runs inside your environment and reports on aggregate signals like accuracy rates, drift, and latency rather than individual records. When a case needs deeper review, it stays within your HIPAA boundary and your access controls, never in an external dashboard.

Clinician review is a built-in checkpoint, not an afterthought. Overrides and corrections are captured as labeled feedback, which both protects patients in the moment and feeds the evaluation set, so the next model version is measured against real clinical judgment.

Production monitoring tracks extraction and summarization quality continuously, so a template change that degrades output triggers an alert rather than silently reaching a clinician. The eval gate then verifies any fix before it ships.