Healthcare AI, on the record.

AI in healthcare - where the cost of being wrong is a person, not a refund.

Healthcare is the industry where AI has the highest theoretical upside and the longest list of failed deployments. The clinical and administrative pressures are real: workforce shortages, multilingual patient populations, intake bottlenecks, documentation burden, and a long tail of back-office work that pulls clinicians away from patients. AI can absorb a meaningful share of this work - but only if the system is designed for an environment where being wrong has clinical, legal, and human consequences.

The non-negotiables are not features. PII redacted before tokens leave the VPC. EMR integration that respects clinical workflow, not a parallel chart that nobody reads. Multilingual coverage that recognizes Hebrew, Arabic, Russian, and English natively - not English with translation latency. Voice agents that meet HIPAA-class controls over SIP. Signed audit trails on every state change, because the next review will be by a regulator, an internal compliance officer, or a patient’s attorney - and the system has to defend itself in that conversation.

The cultural work is at least as important. Clinicians have seen the pitches. They have also seen the systems that promised to reduce documentation burden and ended up adding to it. Programs survive when they are built with the people who will run them - clinicians, intake staff, IT, security - and when the deployment is staged tightly enough that the team can course-correct before the system is live in thirty hospitals. We have shipped that work, with the Ministry of Health and a wider network of public hospitals, and we build assuming the system will be reviewed by people for whom safety is the only feature that matters.

30 government hospitals, one patient platform.

Built with the Ministry of Health on the IL Landing Zone, end-to-end signed audit, live in 30+ hospitals.