Five Questions to Ask Before You White-Label Genetic Testing
The checklist a lab owner, hospital administrator or clinic director should put to any white-label genomics vendor — including us.
Once you've decided that building an interpretation platform in-house doesn't make sense, the harder problem starts: the vendors all sound alike. Every deck says AI-powered, every site says evidence-based, and the differences that will actually determine your risk and your margins are invisible in a pitch. These five questions make them visible. Put them to every vendor you evaluate — including us.
1. Where does each interpretation trace to?
A genomic report is a chain of claims, and your brand sits at the end of it. If a statement in the report can't be walked back to the evidence behind it, you are signing your name to something you cannot defend when a clinician — or a lawyer — asks. A good answer names the evidence base, explains how it is curated and updated, and shows you citations inside an actual report. A red flag is "proprietary algorithm" offered as the final answer, or an evidence base scraped in bulk from public databases with no curation step between the literature and your client's report.
How we answer it: every interpretation on our platform traces to a curated evidence base, and the citations are in the report where your clinicians can check them.
2. Who signs off before a report ships?
Ask literally: before a report reaches my client, does a qualified human read it — and is their sign-off recorded? A pure-algorithm pipeline quietly shifts all interpretive risk onto whoever receives the report, which in a white-label arrangement means you. A good answer describes a named review step, who is qualified to perform it, and what happens when a reviewer disagrees with the machine. A red flag is "fully automated" presented as a feature, or the vaguer "our scientists validate the models" — model QA is not report review.
How we answer it: every report is reviewed and signed off by a qualified human. Decision-support for the treating professional, not a verdict from a black box.
3. What happens to my clients' data — and who owns the relationship?
Genetic data sits in the most protected category of nearly every privacy regime — special-category data under GDPR, and in India, obligations under the DPDP Act. Ask for the vendor's data-processing terms in writing: where data lives, how deletion requests are honored, and what "we may use data to improve our products" actually licenses them to do. Then ask the commercial version of the same question: does the vendor run any channel that touches consumers directly? If so, your client list is their prospect list. A good answer is contractual on both counts. A red flag is vagueness on either.
How we answer it: we are GDPR compliant, and we run no consumer store — your clients are never our customers. The white-label structure is the point: the brand on the report, and the relationship behind it, are yours, and both commitments are in the contract.
4. What does the vendor's marketing actually claim?
This is the question buyers skip, and it is the one with the longest tail. Read the vendor's public copy the way a regulator would. In the EU, marketing language helps define a product's intended purpose — diagnostic phrasing in a brochure can reclassify the product itself. And a vendor that overclaims is not taking a risk on your behalf; it is handing the risk to you, because your name is on the report your clients hold. A good answer is boringly precise: decision-support framing, consistent across website, deck and contract, with certifications stated exactly — name, scope, status. A red flag is diagnostic language in the marketing while the contract quietly disclaims it, or certification name-dropping with the specifics missing.
A vendor's overclaim never stays the vendor's problem. Your brand is on the report.
How we answer it: decision-support, not diagnosis — and we state our certifications in full: ISO 9001:2015 certified, pursuing ISO 13485, GDPR compliant. The precision is deliberate. In this category, precision is the product.
5. What are the real unit economics?
The headline per-report price is the start of the pricing conversation, not the end of it. Ask what surrounds it: setup fees, minimum volume commitments, what happens to pricing at your actual volumes rather than the brochure's, and — most telling — what it costs to leave. A good answer is a per-report number you can model, and exit terms under which your accumulated report data walks out with you. A red flag is "custom pricing" that never resolves into a number, or a contract where the price of leaving is losing your own history.
How we answer it: from roughly $3–5 per interpreted report, on top of the wet-lab you already run — and we'll model it against your real volumes, not ours.
If you're evaluating vendors, book a 30-minute demo and bring all five questions. We'll answer them against your actual test menu and volumes.