We build healthcare AI solutions for clinical, operational, and population-health use cases. Risk-prediction models, imaging analysis, ambient documentation, and LLM-powered clinical assistants — built with the audit trail clinicians need when they have to defend a recommendation.
A model with 92% accuracy that can't explain its reasoning is useless in a clinical encounter. The hard part of healthcare AI isn't training the model — it's building the surrounding system (audit logs, version control, drift monitoring, fairness checks, human-in-the-loop UX) that earns clinician trust and survives regulatory review.
Data lineage from EHR through to model input. Model versioning with full reproducibility. Audit logs that capture every input, output, and clinician override. Drift monitoring in production. Human-in-the-loop UX that surfaces model confidence and rationale. The model itself is 30% of the work; the system around it is 70%.
Models in production with documented training data, version control, drift monitoring, and audit trails. Clinician acceptance survey above 4.4/5. Workflow time reductions of 20–40% on the documentation and review-heavy steps.
Risk-prediction models for sepsis, readmission, decompensation, no-show — with calibration and fairness analysis built in.
AI-assisted reads for radiology, pathology, and ophthalmology — with DICOM-native integration and verifier workflows.
LLM-powered ambient documentation that drafts clinical notes from encounter audio — physician-approved before commit.
Retrieval-augmented LLMs grounded in patient records, formulary, and clinical guidelines — with citation and consent controls.
Cohort risk-stratification, attribution, and outcomes-prediction models supporting value-based contracts.
Production drift monitoring, fairness dashboards, and model-version audit trails — the operational discipline regulators ask about.