A Longevity Institute Shipped Clinical AI, Zero Egress

A precision longevity institute targeting high-net-worth patients faced a constraint that broke every conventional AI architecture: a national data-residency law forbids sending patient data outside the country's infrastructure. That ruled out every hosted foundation-model API on the market. An incumbent vendor had already burned 12 months on the problem and produced no code or artifacts the institute's team could build on. With a clinic launch date fixed, the institute needed clinical-grade AI that regulators, clinicians, and compliance officers could all sign off on.

Lazer replaced the incumbent vendor and built three production AI products on a shared platform: an in-clinic 75-inch display experience with live biomarker swapping, a clinician CMS with AI-assisted authoring, and a patient mobile app. All three are wired to a 24-endpoint FHIR R4 Precision Health Engine with SHAP/LIME explainability and evidence-graded citations treated as first-class API contracts, not afterthoughts.

The architecture's most novel pattern is a Microsoft Teams group chat per patient containing the patient, the AI assistant, and the entire care team. Clinicians have live visibility into every AI conversation, can take over mid-thread, and no AI message ever bypasses the audit trail. Orchestration runs on LangGraph over a self-hosted 70B Llama-3-based clinical LLM that performs on par with GPT-4 on USMLE benchmarks, with a 3-agent guardrail consensus required before any response reaches a patient. Inference runs in-region on dedicated H100 GPUs. Along the way, Lazer added 212 backend tests and closed multiple security gaps left from prior work.

Lazer began by replacing an incumbent vendor that had spent twelve months without producing usable code. Because a national data-residency law forbade sending patient data to any hosted foundation-model API, the team ruled out every commercial API and committed to a self-hosted approach. They stood up a 70B Llama-3-based clinical LLM on dedicated in-region H100 GPUs, then orchestrated it through LangGraph so that three independent guardrail agents had to reach consensus before any message reached a patient. Rather than treat compliance as an afterthought, they made evidence-graded citations and SHAP/LIME explainability first-class API contracts. They built a 24-endpoint FHIR R4 Precision Health Engine as the shared backbone for all three products, and designed a per-patient Microsoft Teams channel that placed the patient, the AI assistant, and the full care team in one thread, so clinicians could watch and take over mid-conversation. Along the way they added 212 backend tests and closed security gaps left by the prior vendor, hardening the platform ahead of the fixed clinic launch date.

Healthcare and life sciences organizations operating under strict data-residency or sovereignty laws where hosted AI APIs are legally off the table.