AI-native shouldn't mean you bolted on AI. It should mean you can keep it safe as it gets stronger.
The companies that last in AI-enabled care won't be the ones that integrated AI fastest. They'll be the ones built so it stays safe and governed no matter how powerful it gets. That assumption is our foundation, not a feature.
SOC 2 Type II
HIPAA Compliant
FHIR-native
Patent PendingThe scarce thing isn't another model. It's the layer that can govern them.
You already feel it. AI is getting more capable faster than anyone planned for, and the bottleneck in care isn't the intelligence, it's whether a hospital can trust it near a patient. As models get stronger, the scarce thing isn't another model. It's the layer that can govern them, keep them safe, and make them deployable in a regulated setting. That layer is where the durable value sits, because it gets more necessary, not less, as AI improves. So the real question for anything you build, your device, your IP, your programs, is whether it sits on a foundation built to govern AI from the start. Build on that, and a stronger model makes you better. Build without it, and a stronger model makes you more exposed.
Not AI as a feature. AI as a force you design to govern.
Built on the assumption.
Built on the assumption that the AI has to be kept safe and usable for the value to hold, no matter how strong it gets. That assumption is our starting point.
Safety as the foundation, not a feature added later.
The envelope endures.
The model is swappable; you will run a better one next year and the year after. What endures is the governed envelope it runs inside.
Models change. The envelope holds.
Bounded authority, by design.
The bounded authority, the policy, the audit. We made that envelope the foundation, so the intelligence can keep improving without the safety having to be rebuilt each time.
Bounded authority is what makes 'agentic' deployable.
The intelligence keeps improving.
Because the safety is structural, you get the upside of stronger AI without re-earning the trust each time a model improves.
A stronger model makes you better, not more exposed.
The line the FDA draws, built into the platform.
The FDA draws a hard line between general wellness and clinical use, and which side you're on turns on intended use and the claims you make. The 2026 General Wellness guidance is specific, and we built that line directly into the platform: an organization defines a compliance policy with two lanes, and every AI output is checked against the lane it's allowed to operate in. The agent can only produce what its lane permits, because the regulatory boundary is enforced on every output, not left to hope.
The policy we built against: FDA, General Wellness: Policy for Low Risk Devices.
A wellness-lane product can encourage someone to see a professional and show general benchmarks, but it cannot issue diagnostic alerts, trigger clinical interventions, or recommend treatment without becoming a regulated device. Wellness-lane output that drifts toward a clinical claim is caught and rewritten or blocked.
The lane for regulated, clinically consequential work. Clinical-lane output that needs a human is surfaced for review, and the clinically consequential decisions surface to your clinical team rather than being made autonomously.
PHI-safe model routing.
PHI REQUEST
Heart Rate: 106 bpm
Patient ID: 4287
HIPAA Tier
BAA-Covered Model Providers
HIPAA-compliant
NON PHI REQUEST
Summarize ECG Trends
General Tier
Broader AI Models
No Patient Data
PHI requests
PHI REQUEST
Heart Rate: 106 bpm
Patient ID: 4287
HIPAA Tier
BAA-Covered Model Providers
HIPAA-compliant
Non-PHI requests
NON PHI REQUEST
Summarize ECG Trends
General Tier
Broader AI Models
No Patient Data
Compliance gates. Bounded authority.
Governance layer
PHI-Safe Model RoutingHuman-in-the-Loop ReviewLLM Cost and Usage TransparencyCompliance GatesAgent Types with Scoped AuthorityFull Audit TrailMeets all criteria. Output delivered as-is.
Clinical claims rewritten to wellness-appropriate language. Both versions preserved.
PHI leak or harmful content. Surfaced for human review.
The audit trail is the thing that lets a hospital say yes.
Every run logs what data was seen, which model ran, what was produced, which gate it passed, whether a human approved it, and what action followed. Structural, not bolted on.
Audit Trail · Patient_01
ECG stream · episode metadata · patient context
source: ring_0000 · BLE · 250Hz
episode: ep_0000 · 13:30–14:00
patient: pt_0000 · PHI tagged
hash: 0000x00 · verified
anyBio-clinical-v2 · HIPAA tier · BAA-covered
14:08:01“Resting tachycardia detected. HR sustained above 100 bpm...”
14:09:01PHI check · scope · confidence · decision: Human Review
14:10:07dr_JohnDoe · flagged for review · 14:10
14:14:01FHIR synced · EHR updated · episode closed
14:31:01Your model can be the best in its class and still never reach a patient if there's no governed place to run it.
We are that place, and it stays current as models improve.
Your team can say yes to AI because legal and security are reviewing one policy, not auditing every tool.
The governance is the same on every agent, so approval is about the policy, not the project.
See the governance in practice.
We'll walk through the compliance policy, the wellness and clinical lanes, the gates, and the audit trail, and show how an agent runs inside the envelope on a real program.
