🏥
Wavn for Healthcare

AI your legal team will sign off on.

HIPAA makes most mainstream AI a liability. Teams work around it unsafely or avoid AI entirely. Wavn is built so patient-adjacent data never persists on a model provider's servers.

HIPAA makes every consumer AI tool a liability the moment patient-adjacent data is involved. So teams either work around it unsafely, or skip AI altogether. Both are worse than a tool built for the constraint.

How Wavn helps healthcare

Built for the way you decide.

Built for the data constraint

Patient-adjacent work needs AI that respects strict handling rules. Wavn's architecture keeps data isolated from model providers — documentable for your compliance and legal review.

A record for every decision

Mark each conclusion as a decision, action, or open question. Clinical and operational reasoning is preserved with its evidence — not lost in a chat history.

Both sides of the evidence

The Perspective Engine shows supporting and opposing findings side by side with sources, so a recommendation is weighed against the counter-evidence before it's made.

No OpenAI

No OpenAI, no consumer terms

With no OpenAI in the stack, there's no consumer Terms of Service governing sensitive material. Independence is enforced at the architecture level, with an on-premises option for Studio.

A day in Wavn

A team weighs a protocol change.

A hospital operations team is evaluating a change to a care pathway. The evidence is mixed, and the stakes are high. Here's how that review looks in Wavn.

01
They frame the proposed change on the canvas with the operational constraints.
02
Perplexity surfaces the relevant literature; Gemini structures the trade-offs. All cited.
03
They run Rift — one engine argues for the change, one against. A patient-safety risk surfaces.
04
They mark the decision with the open risk attached. The review trail is preserved for governance.

Illustrative scenario. Wavn is invite-only and pre-launch; no real client data depicted.

Care pathway review — Protocol 4B
We're considering moving step 3 earlier in the pathway to cut length-of-stay. Argue against it — what's the patient-safety risk we might be missing?
Gemini · Analysis
The efficiency case is real, but step 3 early shifts a monitoring window. The risk: a subset of patients shows the relevant complication exactly in the hours you'd be compressing. Length-of-stay drops; so might your detection rate for that subset.
Supports
Moving step 3 earlier cuts average length-of-stay and frees capacity.
Disputes
Compresses a monitoring window where a patient subset shows complications.
Decision: Pilot the change with enhanced monitoring for the at-risk subset before rollout.
← See all the fields Wavn is built for