A Longevity Institute Shipped Clinical AI, Zero Egress

After an incumbent burned 12 months with nothing usable, the institute's clinical team got three AI products live in-country, with clinicians taking over any patient conversation.

Shipped production AI products

Implementation Time

Aanikh Kler

Lazer Technologies | Co-Founder Surf (acquired) | Canada's Young Entrepreneur of the Year 2023

Lazer Technologies

Connect ↓

The Challenge

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.

What They Built

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.

AI Role

A self-hosted 70B clinical LLM powers the patient-facing assistant, clinician content authoring, and biomarker storytelling, orchestrated through LangGraph. Before any message reaches a patient, three independent guardrail agents must reach consensus, and every output carries evidence-graded citations and SHAP/LIME explainability so clinicians can trace exactly why the AI said what it said.

AI Model

Custom / proprietary

Infrastructure

In-region sovereign cloud and dedicated on-prem H100 GPUs (fully sovereign, zero egress) • 24-endpoint FHIR R4 Precision Health Engine • Microsoft Teams (Azure Communication Services) per-patient care-team channels

Integration Points

24-endpoint FHIR R4 engine • Microsoft Teams group chat per patient (patient + AI assistant + care team) • LangGraph orchestration over a self-hosted 70B clinical LLM

Impact

Zero Data Egress, Hundreds of Controls Satisfied

No patient data leaves in-country infrastructure, and the platform satisfies hundreds of national healthcare security controls, turning the region's hardest regulatory constraint into a deployable architecture.

3 Production AI Products on One Platform

An in-clinic display experience, a clinician authoring CMS, and a patient mobile app all ship from a shared FHIR R4 engine, where the prior vendor's 12-month effort produced nothing usable.

212 Backend Tests, Multiple Security Gaps Closed

Lazer hardened the platform for clinical launch with extensive automated testing and security remediation, with clinician takeover and full audit on every AI interaction.

Technology Utilized

Implementation Complexity

Risk & Compliance

Customer Experience

Best Fit For

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

Aanikh Kler

Lazer Technologies | Co-Founder Surf (acquired) | Canada's Young Entrepreneur of the Year 2023

Lazer Technologies

Award-winning entrepreneur and tech leader helping startups and global brands like Netflix and Amazon drive growth by ethically harnessing consumer data and AI-driven innovation.

Get an intro

Industry:

Healthcare & Life Sciences

Business Function:

Product & Engineering

Operations

Company Size:

Project Cost:

Cost not disclosed

Ownership:

Organization Type:

Private Company

AI Pattern:

Conversational AI (Chatbot / Agent)

AI-Accelerated Custom Software

Value Type:

Risk & Compliance

Customer Experience

AI Model:

Frequently Asked Questions

How did a longevity institute run clinical AI without sending patient data out of the country?

The institute worked with Lazer to self-host a 70B clinical LLM on dedicated in-region H100 GPUs instead of using any hosted API, keeping patient data inside national infrastructure. The result was a clinical AI platform with zero data egress.

What AI tools and models were used to build the sovereign clinical AI platform?

The platform runs a self-hosted 70B Llama-3-based clinical LLM orchestrated with LangGraph, wired to a 24-endpoint FHIR R4 engine. Three independent guardrail agents must reach consensus before any message reaches a patient, and inference runs on dedicated H100 GPUs.

What results did the healthcare AI project deliver?

Lazer shipped three production AI products on one shared FHIR R4 engine: an in-clinic display, a clinician authoring CMS, and a patient mobile app. The platform satisfies hundreds of national healthcare security controls, and Lazer added 212 backend tests while closing multiple security gaps.

How long did the sovereign clinical AI build take?

The prior vendor had spent twelve months without producing usable code before Lazer took over. A specific delivery timeline for the new platform was not disclosed, though the build was scoped to a fixed clinic launch date.

Who is sovereign clinical AI best suited for?

It best fits healthcare and life sciences organizations operating under strict data-residency or sovereignty laws, where hosted AI APIs are legally off the table.