Health providers hold some of the most tightly regulated data in New Zealand. A private AI instance lets clinical and administrative teams search approved protocols, summarise case context and draft responses — while patient information stays inside your own environment, aligned with the Health Information Privacy Code, and a clinician owns every decision that carries consequence.
Health information carries specific handling rules under the Privacy Act 2020 and the Health Information Privacy Code. Routing it to a vendor-hosted, multi-tenant model is often incompatible with those obligations — which is exactly the obstacle a private, in-environment deployment removes.
A wrong answer in a clinical setting can carry real harm. We build for support, not autonomy: a human clinician owns every consequential decision, with escalation and boundaries designed in from the start rather than bolted on.
Care systems can’t afford to break when a public model is deprecated overnight, or to depend on where a vendor happens to store data. A private instance gives you control over the model version, the update schedule and where data physically resides.
Instant, accurate answers from your approved protocols, guidelines and policies — without the documents ever leaving your infrastructure.
Summarise case context and draft first-pass responses to lift administrative load, with clinicians reviewing anything that carries consequence.
Consistent answers to routine, non-clinical questions, with anything sensitive or ambiguous escalated to a person.
Yes — if the architecture is right. The obstacle is not AI itself but where patient information is processed. A private AI instance runs inside infrastructure the provider controls, so patient data never reaches a shared external model. That removes the main sovereignty barrier while keeping a clinician accountable for every consequential decision.
It’s designed to be. Because patient information stays inside your own environment and never reaches a shared external model, a private deployment removes the main sovereignty obstacle. We define exactly what the AI can access and do before go-live, and every consequential decision stays with a clinician. Where a specific use case doesn’t fit, we’ll tell you honestly.
No — and it shouldn’t. We build for clinical support: knowledge search, summarisation and drafting, with a human clinician owning every decision that carries consequence. Those boundaries are agreed during deployment and enforced by design.
We scope integration during the AI Readiness Workshop and build inside your existing security and governance rather than around it. The point of a private instance is that it lives where your controls already are.
Book a free discovery call. No preparation required — just tell us what you’re trying to solve.
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