The Trust Rails of Modern Healthcare
Cryptographic proof that a health service actually happened — before the claim is paid.
A 30–90 day pilot with African HMOs and health insurtechs. Live in days, not months.
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Recognition
Winner, Startup Pitch Competition 2026
AI for Good Innovation Factory 2027 — exhibitor and Startup Accelerator Programme
InnovateX competition winner
Selected for Claude for Startups
The Widening Trust Gap
1 in 10*
claim denials in the U.S. in 2023 were due to a lack of prior authorization or referral.
15-20%*
Of healthcare funds are lost due to fraud, waste and abuse.
Fraudulent claims and relentless disputes have destroyed the bridge of trust between HMOs, health insurtechs, and their partner pharmacies & hospitals.
Quantify the Financial Leakage:
The True Cost of Micro-Fraud
Relatively small amounts of fraud could culminate into bigger financial woes. See the impact yourself.
Daily Loss
₦600,000
Annual Loss
₦216,000,000
*Calculated based on 360 days
How StripppleCare Works
StripppleCare is the verification layer between the visit and the claim. We confirm the patient was there, that they approved the service, and that they agreed the bill — before it reaches the payer. Fewer disputes, because there is less left to dispute.
Four proofs. One certificate.
Every patient visit generates four independent checkpoints, chained into a single tamper-evident record.
Proof of Presence
The patient is verified as physically at the facility — confirmed by a one-time code and the terminal's own location.
Proof of Authorisation
For high-value procedures, the patient approves the specific service and amount before it is delivered.
Proof of Occurrence
The billing officer confirms what was actually delivered, sealed against the itemised bill.
Proof of Consent
The patient reviews their own bill, on their own device, and confirms it before the claim is submitted.
Each proof produces a unique digital fingerprint, sealed together into a single verifiable record. The HMO, a regulator or an auditor can confirm that record independently — the verification does not depend on our word for it.
What the payer actually receives.
Every completed visit issues a Trust Certificate — a signed record carrying the four proof fingerprints and a Trust Grade.
Auditors review only what genuinely needs reviewing. The grade comes from a deterministic, rule-based engine scoring three independent dimensions — identity, transaction, integrity. Every decision is explainable and auditable.
Built for how healthcare actually works.
Works with any patient
Verification reaches the patient through whatever they can use — a code in the patient app, SMS, or email. Patients with a smartphone use the app. Patients with a basic handset and no data use SMS. Nobody is excluded by the device in their pocket.
Minimal integration to start
A pilot needs no integration at all — staff work in a browser tab alongside their existing systems. Deeper integration is available as a deployment matures, but it is never a precondition.
Live in days
No procurement cycle. No integration project. No new hardware.
Integration, in stages.
Integration deepens as a deployment matures. It is never a requirement to begin.
Start
No integration. Staff use StripppleCare in a browser tab. The HMO supplies enrollee and tariff data at onboarding. Live in days.
Connect
Eligibility and tariff data sync directly with the HMO's systems instead of being uploaded. Read-only.
Integrate
The hospital's management system communicates with StripppleCare directly, removing manual steps for staff.
Embed
StripppleCare runs inside the hospital's existing system. Staff never leave the interface they already know.
Who this is for.
Today
HMOs and health insurtechs
Verified claims before payment. Fewer disputes, faster adjudication, less leakage.
As we expand
Government health schemes
Proof that scheme funds reached real patients who received real services.
Donors and implementing organisations
Point-of-care evidence of programme delivery, independently verifiable.
Lenders and development finance
Verified encounter data as a basis for underwriting health providers.
The data layer comes before the model.
Most health AI in Africa trains on unverified claims data. Garbage in, garbage out.
Our verification engine is deterministic and rule-based today — explainable, auditable, defensible in a dispute. What it produces is something that doesn't currently exist: a cryptographically verified health claims dataset.
That dataset is what makes anomaly detection trustworthy later. We're building the ground truth first, because a model is only as good as what it learns from.
Research
Founder's research accepted at ICEGOV 2026, the International Conference on Theory and Practice of Electronic Governance, organised by United Nations University (UNU-EGOV).
Academic affiliation: Miva Open University.
Get in touch
Ready to transform your healthcare trust infrastructure?