Industries — Healthcare
Hospitals, health systems, and healthcare technology companies. Where the cost of a wrong prediction is measured in patient outcomes — and the engineering standard has to match.
Healthcare AI fails differently than other AI. A recommendation engine that is wrong loses a click; a triage model that is wrong loses time a patient may not have. That asymmetry shapes everything about how we work in this industry — from the data handling to the validation evidence to the way uncertainty appears on a clinician's screen.
Clinicians are already saturated with alerts. A decision support tool that adds noise gets ignored — or worse, erodes trust in every system that follows it.
We design for specificity first, integrating with the actual clinical workflow rather than interrupting it. Uncertainty is displayed honestly, overrides are one click and always logged, and the system is tuned with clinician feedback until it earns its place on the screen.
Radiology and pathology backlogs mean urgent cases can wait behind routine ones. Triage models help — but a model that quietly fails on a patient subgroup is a safety incident waiting to happen.
Every triage model we deliver includes subgroup performance analysis as a release gate, not an appendix. Cases the model cannot confidently assess are flagged for standard-priority human review rather than silently ranked, and the confidence thresholds are governed clinically, not just statistically.
Bed capacity, ED demand, and staffing decisions are still frequently made on spreadsheets and intuition, while the data to do better sits unused in operational systems.
We build forecasting systems planners actually trust: honest confidence intervals instead of false precision, accuracy tracked and reported continuously, and models that degrade gracefully — and visibly — when conditions shift beyond what they have seen.
Tell us about the model you need, the rules you operate under, and the deadline you are working against. We will tell you honestly whether we can help.