biomarkr is a health intelligence platform built to help people understand their blood test results over time. This page explains what we are, what we are not, and the principles that govern how the platform works.
biomarkr was built after years of trying to make sense of repeated blood tests during a long personal health journey. Test after test, the results came back — numbers, ranges, flags — but the picture never became clear.
Eventually things became clearer. But the experience made one thing obvious: the hardest part of blood testing is not getting the results. It is understanding what they mean across time, across your body systems, and in the context of how you actually feel.
Private blood testing in the UK is growing fast. The interpretation infrastructure around it is not keeping pace. Patients often leave appointments with numbers but little sense of direction. Clinicians spend time re-explaining things a clear report could communicate once.
biomarkr exists to close that gap — not to replace clinicians, but to give patients and their doctors a longitudinal picture that makes those conversations more informed and more useful.
These are not aspirations. They are constraints that govern product design, output framing, and what the platform will and will not do.
These boundaries are not caveats added at the end. They are the design brief.
Every report passes through automated layers before it reaches a clinician for review. These deterministic systems run separately from the AI layer.
The core design principle is straightforward: facts first, AI second. biomarkr extracts and structures every biomarker — name, value, unit, reference range, previous result, and trend direction — before interpretation is generated. It does not generate final interpretation directly from raw PDF text; the document is first converted into structured biomarker data.
Once a draft is generated, automated controls check that the narrative is grounded in the panel data, free of alarmist or diagnostic language, internally consistent, and appropriately caveated.
In the clinic workflow, the output is then reviewed and approved by the clinician before it reaches the patient. Nothing is delivered autonomously. The human-in-the-loop is structural, not optional.
The automated acceptance gate runs 28 test scenarios — across four patient profiles and seven test timelines — before every release. All 28 must pass.
The pseudonymous data model is a deliberate architectural choice to minimise the sensitivity of what biomarkr processes.
In the clinic workflow, biomarkr never receives patient names, NHS numbers, date of birth, or contact details. Your clinic assigns a pseudonymous ID, and biomarkr processes only that ID alongside blood test data.
The mapping between a patient's identity and their pseudonymous ID lives entirely within your clinic's systems. biomarkr cannot reverse it.
For patients using the direct companion app, biomarkr holds account credentials and uploaded test data under UK GDPR. A full privacy policy is available on request.
biomarkr is positioned as a health intelligence and interpretation tool — not a medical device. Staying inside that line is central to how the platform is built.
In the UK, software intended to diagnose, prevent, monitor, treat, or alleviate a medical condition may qualify as Software as a Medical Device and fall under MHRA regulation. biomarkr is currently designed and positioned to remain outside diagnostic or autonomous clinical decision-making use cases.
Every new feature is evaluated against these boundaries. If a feature moves the product toward autonomous clinical decision-making, it is not built in that form.
In the clinic workflow, clinician review is the mechanism that keeps the product in its intended lane. The AI generates a draft. A qualified clinician reviews and approves it.
We are monitoring developments in UK health AI regulation and will engage with regulatory guidance as the platform evolves. We are not currently seeking SaMD certification and will not make regulatory claims beyond what has been formally established.
We believe trust is built by being precise about what a tool is for — and honest about where it falls short. These are not edge cases. They are structural limitations users and clinicians should understand.
If you have questions about how biomarkr handles data, how the safety architecture works, or how to evaluate the platform for clinical use, we are happy to talk through it properly.