Founder & CEO

Affriee DareleYvana

Founder & Chief Executive Officer, Harmony Verify

Building the verification infrastructure for healthcare AI — ensuring that every AI-generated medical output is safe, accurate and trustworthy before it reaches a patient.

Clinical AI assurance Verification infrastructure Patient safety

The story

Why Harmony exists.

I've spent years at the intersection of healthcare and technology. I watched AI systems being deployed into clinical environments at unprecedented speed — and I saw what happened when speed outpaced verification.

A hallucinated dosage. A missed red-flag symptom. A treatment recommendation that ignored a contraindication. Each one is a patient at risk. And yet there was no independent infrastructure to catch these errors before they reached providers and patients.

Harmony exists to close that gap. We connect AI companies with board-certified medical experts who review, verify and certify AI-generated outputs — creating a trust layer that doesn't slow down innovation, but ensures it's safe.

Correction issued REC-0001

The case that does not make the news General medicine

An AI-drafted plan reads fluently, cites nothing that is obviously wrong, and contains one number that should not be there.

Somebody has to be looking. That is the whole company — the person whose job it is to look, and the record proving they did.

What we are for

Mission and long-term vision.

Mission

To ensure every AI-generated medical output is reviewed by qualified human experts before it reaches a patient — making healthcare AI safe, accurate and trustworthy at scale.

Long-term vision

To become the trusted verification infrastructure for all of healthcare AI — a standard that institutions, regulators and patients can rely on, combining advanced AI with expert human oversight.

Why this, why now

Why I built this, and why it should matter to you.

Why I chose this problem

Because the failure is invisible until it isn't. A model that is wrong in an obvious way gets caught on the first demo. A model that is wrong in a plausible way — fluent, confident, formatted like every correct answer it has ever produced — gets deployed, trusted, and acted on.

I kept meeting teams building genuinely useful clinical tools who had no honest way to answer the only question that mattered: how do you know this output is right? Internal evaluation is a company grading its own exam. Nobody in medicine accepts that anywhere else, and there was no reason to accept it here — except that the alternative did not exist yet.

So the choice was to keep waiting for someone else to build it, or build it. I am not a large incumbent, and that turns out to be the point: verification only means something when the party doing it has no stake in the answer being yes.

Why it is crucial

Clinical AI is crossing from suggestion to dependence. When a tool drafts the note, the note gets signed. When it flags the risk score, the score enters the chart. The clinician is still accountable — but their attention is now spread across output that arrives faster than it can be independently checked.

Meanwhile the people who decide whether a product ships into a hospital — clinical governance committees, safety officers, regulators — are being asked to approve systems on evidence the vendor produced. That is a bottleneck for good products and an open door for bad ones. Both are bad outcomes.

Independent verification fixes the asymmetry. Not by slowing anyone down, but by making "it was checked, by this named clinician, against this evidence, on this date" a thing that exists.

If you build healthcare AI

You get past the governance committee with independent evidence instead of a promise, and you find out where your system is weakest before a customer does.

If you are a clinician

You get a second set of qualified eyes on the output you are being asked to trust, and a record you can point to when someone asks how you knew.

If you are a patient

You will never see this layer, and that is the intention. The point is that the mistake was caught upstream, before it became your appointment.

AI will become infrastructure in medicine. Trust cannot be the optional part.

Affriee Darele Yvana Founder & CEO, Harmony Verify

Timeline

The journey.

Early career
Clinical foundation

Built deep expertise at the intersection of healthcare and technology, witnessing firsthand the gap between AI deployment speed and verification.
The insight
Identifying the gap

Recognised that no independent infrastructure existed to verify AI-generated medical outputs before they reached patients and providers.
Founding
Creating Harmony

Founded Harmony to build the verification layer healthcare AI desperately needed — combining board-certified experts with rigorous, auditable workflows.
Growth
Building the network

Building a global network of medical specialists across more than twenty specialties, into a verification infrastructure that scales with demand rather than against it.
Today
Scaling impact

Leading Harmony toward becoming the trusted verification standard for healthcare AI — the layer institutions, regulators and patients can rely on.

Work with us

Send me your hardest cases.

The most useful conversation is not a deck. Route a real sample of what your system produces and we will come back with a measured baseline and the errors we found.

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