Clinical verification infrastructure

Ship clinical AI you can defend.

Harmony Verify is the verification layer between your model and the clinician using it. Board-certified reviewers check AI-generated clinical output against source evidence and return a signed record — so your customers, your regulators and your legal team can all see exactly how a recommendation was reached.

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Built for CDS vendors Specialty-matched reviewers Signed audit record

Awaiting review REC-4471-CARD

AI draft · Discharge summary Cardiology

72 y/o male, post-MI, discharged on dual antiplatelet therapy. Continue clopidogrel 75 mg daily for 3 months, then reassess bleeding risk.

Patient is concurrently prescribed omeprazole for reflux. Reviewer note: omeprazole reduces clopidogrel activation via CYP2C19 — switch to pantoprazole. DAPT duration should be 12 months per current guidance.

Output received via API 00:00
Routed — interventional cardiology 00:04
Evidence checked, correction issued 01:22
Signed record returned 01:29

Harmony Verify Clinical verification infrastructure — built so an AI-generated clinical output is something a professional can rely on and an institution can defend.

Reviewers: Credentialed, licence-verified clinicians matched to the specialty of the case
Evidence: Every judgement cites the guideline, label or literature it rests on
Record: Every reviewed output returns a timestamped, attributable audit record
Privacy: HIPAA-aligned architecture, BAA available, PHI minimised by default

The problem

A confident answer is not a correct one.

Clinical language models are fluent by construction. They produce dosing, coding and triage recommendations in the register of an experienced physician whether or not the underlying reasoning holds — and the failures that matter most are the ones that read perfectly.

01 — Fluency

Errors that read as expertise

A fabricated citation, an inverted contraindication or a plausible-but-wrong dose carries the same confident tone as a correct one. Clinicians under time pressure have no signal to separate them.

02 — Accountability

No one can say who decided

When an AI-assisted recommendation is questioned months later, "the model produced it" is not an answer a health system, a payer or a court will accept. Someone has to be accountable, by name.

03 — Procurement

Deals stall at clinical governance

Your product survives the demo and then meets the safety committee. Without evidence of how outputs are validated and by whom, pilots stretch into quarters and contracts stay unsigned.

How it works

Four steps from model output to defensible record.

Harmony Verify runs alongside your product. You choose which outputs to route — all of them, a sampled share, or only those your own confidence scoring flags.

Submit

Your application posts the AI output, the clinical context it was generated from and the question being asked. One API call. PHI can be tokenised or redacted before it ever reaches us.

Route

The case is matched to a licence-verified clinician in the relevant specialty, with conflict-of-interest and workload checks applied. Urgency tiers determine turnaround.

Verify

The reviewer works through a structured rubric — factual accuracy, guideline concordance, safety, completeness, appropriate hedging — and links each judgement to the source that supports it.

Return

You receive a verdict, any corrections, the supporting citations and a signed audit record — through the API and in your dashboard. Disagreements escalate to a second reviewer.

Read the platform detail

What you get

Verification, and the paperwork to prove it.

A record that survives scrutiny

Each verification returns an immutable record: the original output, the reviewer's credentials and licence state, the rubric scores, the corrections, the cited sources and the timestamps. Exportable for your quality file, your customers' governance reviews and your own incident investigations.

Attributable to a named, credentialed reviewer
Evidence links to guideline, drug label or literature
Immutable, timestamped and exportable

Verified REC-9013-ONC

Fields contained in a verification record
Reviewer	Board-certified, medical oncology · licence verified
Rubric	Accuracy · Guideline concordance · Safety · Completeness
Evidence	2 guideline citations · 1 drug label section
Outcome	Verified with one clarifying amendment
Turnaround	Within the tier committed for this case

Signal back into your model

Verification is only worth the cost if it makes the next output better. Corrections are returned as structured data — failure category, severity, clinical domain, the specific span that was wrong — so your team can see where the model degrades and feed that into evaluation sets, prompt changes and retraining.

Failure taxonomy by category and severity
Error rates segmented by specialty and task type
Reviewer agreement to keep the reviewers honest too

Failure taxonomy — illustrative

Illustrative failure categories returned with corrections
Category	Example
Unsupported claim	Assertion with no source in the provided context
Guideline drift	Recommendation against current society guidance
Interaction missed	Co-prescribed agent not accounted for
Overconfidence	Definitive phrasing where the evidence is uncertain
Omission	Material contraindication or follow-up left out

Product preview

Built for those who cannot afford to be wrong.

A verification command centre designed for clarity under pressure.

app.harmonyverify.com / dashboard

Active verifications

3 pending

Discharge summary · Cardiology · REC-4471

Potential interaction flagged between clopidogrel and omeprazole. Expert review in progress.

72%

Triage guidance · Emergency medicine · REC-2208

Escalation to emergency assessment added. Signed record returned to the submitting system.

Verified

Treatment recommendation · Nephrology · REC-5540

Dose not adjusted for eGFR 28 — correction issued with label citation.

Corrected

Who we build for

Teams putting AI in front of clinicians.

We work with the companies building the tools, not with individual clinicians. Your product stays your product — Harmony Verify is infrastructure underneath it.

Segment 01

Clinical decision support vendors

Diagnostic assistance, treatment recommendation, risk stratification. Verification gives your safety committee reviewers something concrete to evaluate, and gives you evidence when a recommendation is challenged.

Segment 02

Ambient documentation & coding

Notes, summaries and codes generated from encounters. Sampled clinician review measures the error rate you are actually shipping, rather than the one your benchmark suggests.

Segment 03

Patient-facing health AI

Triage, symptom guidance and medication questions, where the reader has no clinical training and no way to catch an error. Higher stakes, tighter review criteria.

Segment 04

Health system & payer AI teams

Internal models for utilisation, prior authorisation and care management, where an independent clinical check is the difference between a pilot and a deployment.

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Why we exist

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

Affriee Darele Yvana Founder & CEO, Harmony Verify

Harmony Verify started from a straightforward observation: the healthcare AI market is racing to make models more capable while the mechanism for establishing that any given output is correct has barely moved.

Capability without verification does not reach the bedside — it stalls in procurement, in safety review, in the quiet decision by a clinician not to trust the tool. We are building the layer that closes that gap, starting where the cost of error is highest.

About Harmony Verify

Early access

Put a clinician behind every output.

We are onboarding a small number of clinical AI teams as design partners. Tell us what you are building and what you need to prove about it.

Request access Review our trust posture