AI for healthcare that runs inside the hospital.
AI that earns the trust of clinicians, regulators, and patients.
Healthcare AI fails for predictable reasons: it does not respect clinical workflows, it leaks patient data to vendors, and it hallucinates in ways that clinicians cannot quickly verify. We design for the opposite on every engagement.
Our healthcare work runs on the hospital's own servers, trains on the hospital's own de-identified notes, and writes every prediction with an explanation a clinician can audit in seconds. Nothing leaves the building unless the customer explicitly chooses to send it out.
From private documentation assistants to scheduling optimizers and prior-authorization copilots, we build the systems that earn a place in clinical workflows · not the demo graveyard.
Four places to start.
Each of these has shipped in production for a real healthcare customer. Pick the closest match to your situation.
Clinical documentation
Ambient AI that drafts visit notes, discharge summaries, and referral letters in the doctor's own voice · trained on their own past notes, hosted on their own servers, with the medical-code validity check built in.
Smart scheduling
Predict no-shows, optimize clinic capacity, and fill cancellations automatically. The scheduler becomes a recommendation engine rather than a calendar.
Decision support and prior authorization
Surface the relevant guidelines and patient history at the point of care, and pre-fill prior-auth forms so clinicians spend minutes on paperwork rather than hours.
Clinical trial matching
Read every active trial protocol against the EHR, surface candidates for the trial coordinator, and keep eligibility criteria current as protocols evolve.
Three steps. No surprises.
The same shape as every Fornext engagement · from healthcare to banking to restaurants. You see real progress in weeks, not quarters.
Privacy first, scope second
Every engagement begins with a data-handling review: where data lives, who can see it, what regulators expect, what the customer's own governance team will sign off on.
Clinician-in-the-loop pilots
We ship the first working version to a small group of clinicians and measure adoption weekly. If they do not use it, we change it · not the other way around.
Production with audit trails
Every prediction is logged with the inputs and the reasoning. The compliance team can review any decision on demand, and we keep the model trainable from the corrections clinicians make.
The questions healthcare leaders ask us.
These come up on every healthcare discovery call. The answers are real, not sales-deck answers.
Have a different question? Ask usWill patient data leave our network?
No, not unless you decide it should. Our default is on-premise or customer-controlled cloud, with the model and the data both inside your perimeter. We will sign whatever paperwork your team requires.
How do you handle Arabic–English code-switching?
We train on your team's real (de-identified) notes, which captures the way your clinicians actually write. The model adapts to your population rather than imposing a generic English or Arabic model.
Can the model explain its reasoning to a regulator?
Yes. Every output carries an audit trail · which inputs it considered, which guidelines it matched against, and which factors drove the recommendation. Your compliance team can review any decision on demand.
What happens when the model is wrong?
Every correction from a clinician flows back into the training pipeline. The system gets measurably better every week · and the audit trail shows exactly when each improvement happened.
Real-time intelligence on the factory floor · without ripping out what works.
AI that reads like a careful junior associate · without crossing matters.
Real-time decisions · with explanations regulators accept.
Voice agents that pick up the phone · in any language, 24/7.
Talk to us about your healthcare project.
One short call. We'll tell you what we'd do, what it would take, and what it would cost · even if we end up pointing you somewhere else.