Ormer Health helps established medical-device companies identify where AI creates genuine product advantage — and builds the working prototype that proves it. From strategic opportunity-mapping through to a regulatory-grade demonstrator, we bring engineering rigour and regulatory foresight to the front end of innovation.
Where does AI create genuine product advantage in your portfolio — and where is it noise?
We work alongside your R&D, clinical, and commercial leads to map the landscape of plausible AI applications against your product strategy. We separate real opportunities from bandwagon-jumping, and rank what's left by clinical value, technical feasibility, and regulatory tractability.
Deliverable · Opportunity Map & ShortlistFrom shortlisted opportunity to specific, buildable product concept.
We translate the opportunity into a defined product concept — functional architecture, data strategy, clinical claim, and regulatory pathway, all considered together rather than in sequence. The result is a concept that's novel, technically credible, and certifiable.
Deliverable · Concept Definition & PathwayA working concept demonstrator that de-risks the full programme.
We build a regulatory-grade prototype that validates the technical assumptions and produces real data against real questions. By the end of this stage, you've replaced a slide-deck idea with something stakeholders can interact with — and a clear-eyed view of what full development will cost.
Deliverable · Concept DemonstratorMost consultancies will sell you a strategy deck or a regulatory file. We do the work between them — the structured front-end that decides whether the strategy is real and whether the file is worth writing.
Engagements are led by founders, not handed to juniors. Our edge is the rare combination of hands-on machine-learning practice, three decades of medical-device engineering, and deep regulatory experience. Most teams have one of these. We routinely bring all three.
A long-standing orthopaedics manufacturer had a problem familiar to many established medical-device businesses. Their trauma product line was high quality, technically sound, and increasingly indistinguishable from competitors. Price was becoming the deciding factor in tenders.
The brief was unusually open: how do we differentiate?
The answer that emerged from a structured opportunity-mapping exercise was the concept of an instrumented intramedullary nail — a familiar orthopaedic implant, but with embedded strain gauges and an accelerometer. The same implant could now:
— Measure the degree of fracture healing through load-bearing analysis
— Detect non-unions earlier than radiographic imaging
— Monitor patient gait for changes indicating soft-tissue complications
The concept won two IET Technology Innovation Awards and reshaped the manufacturer's strategic positioning in the trauma segment. The lesson isn't about strain gauges. It's that in mature product categories, the real AI opportunity is almost never about adding "AI" — it's about identifying the data layer the existing product is already adjacent to, but not yet capturing.
End-to-end development of clinical ML models, with statistical analysis plans and validation studies designed for regulatory submission.
Conformity assessment strategy and technical-file authorship across EU MDR, UK MDR, FDA QSR, and the EU AI Act. Including the two-week AI Act × MDR gap analysis for products already in development.
ISO 13485-aligned QMS with AI Act extensions. Risk management (ISO 14971), software lifecycle (IEC 62304), usability (IEC 62366), cybersecurity (IEC 81001-5-1).
Acting Head of AI, Head of Regulatory, or Head of QA for medical-device companies that need senior expertise without a full-time hire. Typically one to two days per week, six- to twelve-month engagements.
Although Ormer Health's core market is medical-device AI, the EU AI Act's structural similarity to medical-device regulation means our methodology applies across high-risk AI domains — including HR technology, education, machinery, and critical infrastructure. We take selected engagements outside medical devices where the regulatory overlap is strongest.
A model that performs well in a study is a design input, not a product. The step from funded research to something you can lawfully place on the market is where most of the cost sits, and where the mistakes that cannot be undone are made. We work with spin-outs and grant-funded teams through that transition, while the decisions are still cheap to change.
Classification under MDR Rule 11, UK MDR and FDA, the conformity route that follows from it, and the clinical and technical evidence needed to support it. Delivered as a costed and sequenced plan, so the work becomes something you can budget and raise against rather than a risk you carry unpriced.
An ISO 13485 quality system sized for a team of five rather than fifty, with AI Act extensions where they apply. Built around how your team already works, deployed with your people actually using it, and taken through certification audit.
Your engineers and clinicians learn to run design control, risk management and the software lifecycle themselves. The aim is fewer consultants over time, not more. Usually a mix of workshops and standing sessions through to first submission.
Clinical evaluation, risk management file, software documentation under IEC 62304, usability engineering and post-market plans, written to survive assessment. Authored by someone who has assessed technical files on behalf of an EU notified body.
Written for AI and machine learning software intended for the UK, EU and US markets. It sets out what happens at the handover from funded research to product development, and which of the decisions taken during research can still be corrected.
No form, and no email address required.
CrisisScribe is Ormer Health's flagship product: an AI-powered clinical scribing and quality-assurance tool, built specifically for mental-health crisis teams. It captures the conversation, structures the clinical record, and surfaces quality-assurance signals to senior clinicians — without adding to the cognitive load of frontline staff.
Currently in pilot with NHS partners. Built on the same regulatory foundations we apply to client work.
Three decades in medical-device systems engineering, software development, and regulatory affairs. MSc in Computer Science with specialisation in machine learning, computer vision, and pattern recognition. Lead auditor under ISO 13485 with EU Notified Body experience. Currently co-statistician on a multi-centre clinical validation study, developing machine-learning diagnostic models for respiratory disease.
Senior mental-health clinician with frontline crisis-team experience. Clinical lead for CrisisScribe, ensuring that what we build is grounded in the real practice of clinical work, not in idealised process diagrams. Recent academic work on clinical supervision in mental-health crisis teams (Robert Gordon University).
If your medical-device AI project would benefit from a small, senior team that can do both the engineering and the regulation, we'd be glad to hear from you. Most engagements start with a one-hour call. If the fit is right, we'll send a proposal within a week.