Embedded solutions and SaMD built for compliance. Relevant’s senior teams deliver custom software for medical devices engineered to IEC 62304, FDA, and CE standards.
Medical devices have unique functional requirements, regulatory constraints, and integration dependencies. We build custom software for medical devices from the ground up. It’s designed for your hardware environment, clinical use case, and certification the product requires.
Software as a Medical Device is subject to strict regulatory scrutiny. Our end-to-end SaMD development services cover product definition, risk classification, architecture, development, and validation. All are aligned with the FDA, MDR, and IMDRF frameworks.
Real-time performance, hardware reliability, and safety-critical logic require a different engineering discipline than standard software. Our engineers build firmware and low-level software for medical devices where failure is not an option.
Thorough V&V documentation is what gets medical device software past regulators. Our team covers functional testing, risk-based test planning, and IEC 62304-compliant documentation. Independent verification for new and legacy systems is part of the same quality assurance process we run on every project.
Architecture decisions made early determine if your product scales, certifies, and updates cleanly. We deliver software design for medical devices with an IEC 62304 lifecycle structure, a modular architecture, and built-in regulatory traceability.
Outdated architectures create certification gaps that slow product development. We modernize legacy medical device software without interrupting how the device works, upgrading architecture, documentation, and compliance posture in parallel. Most legacy software modernization projects stall on missing docs, and we rebuild those as we go.
Connected medical devices bring security, interoperability, and data-integrity requirements that standard embedded development doesn’t address. We handle connectivity protocols, cybersecurity controls, and cloud integration alongside device-side firmware. If your device streams data to the cloud, that’s where IoMT software development comes in.
Good code doesn’t pass regulators without the right documentation. We produce and maintain Software Requirements Specification, Software Design Specification, risk management files, and traceability matrices for FDA 510(k), CE marking, and ISO 13485 submissions.
Regulatory frameworks for medical device software are detailed, non-negotiable, and change with product classification. We build IEC 62304 lifecycle structure and ISO 14971 risk management into the architecture from day one. Since February 2026, the FDA’s QMSR aligns 21 CFR Part 820 with ISO 13485, so your quality documentation can follow one structure for US and EU submissions.
Delays in medical device software certification stem from incomplete documentation, poor traceability, or V&V gaps discovered late. We structure development so that regulatory artifacts, such as SRS, SDS, risk files, and traceability matrices, are built alongside the code.
In medical device software, a bug is a patient-safety issue. Our engineers apply rigorous hazard analysis, failure mode assessment, and safety architecture design to ensure the system behaves correctly under all operating conditions.
Older medical device software often lacks the architecture documentation and compliance traceability required for modern regulatory submissions. Modernizing these systems without disrupting device operation demands a structured approach.
Medical devices operating in clinical environments can’t tolerate latency, memory overflows, or timing failures. Our embedded software engineers build firmware with deterministic behavior, hardware abstraction layers, and real-time OS integration suited to the device’s clinical use case.
Connected medical devices are high-value targets. Under section 524B of the FD&C Act, the FDA expects cyber devices to come with threat modeling, an SBOM, and a plan for post-market vulnerability monitoring. MDR asks for similar evidence. We build secure boot, encrypted communication, and these artifacts into the engineering process.
Medical device software doesn’t operate in isolation. Connecting devices to EHR platforms, hospital networks, and clinical data systems requires HL7 FHIR integration, a solid interoperability architecture, and security controls that match the clinical environment.
Updating medical device software post-certification triggers re-validation requirements that teams underestimate. We build change management processes and modular architectures that make post-market updates traceable, testable, and compliant with ongoing regulatory obligations.
Machine learning models trained on imaging, sensor, and clinical data help medical devices detect conditions earlier and more accurately. Alongside this, it reduces diagnostic error and supports clinicians with evidence-based recommendations at the point of care.
AI models analyze operational data from medical devices in real time, identifying anomalies and predicting failures before they affect clinical use. Maintenance becomes proactive, and device uptime improves measurably.
Deep learning pipelines process X-ray, MRI, CT, and pathology images to spot patterns that are hard to see in clinical practice. We build and validate these models the same way we approach AI in healthcare in general: accuracy and safety thresholds come first, because SaMD classification depends on them.
Unstructured clinical notes, device logs, and patient records contain information that databases miss. NLP models extract, classify, and route that data, which allows you to turn noise into signals that care teams and device manufacturers can act on.
Connected devices generating continuous physiological data need smart filtering to separate meaningful alerts from noise. AI models trained on patient-specific baselines catch deterioration early without flooding care teams. That’s the core of any remote patient monitoring platform: alerts that matter.
Medical devices serving diverse patient populations benefit from software that adjusts to individual physiology, usage patterns, and clinical context. We develop adaptive AI algorithms that optimize device behavior per patient. Our team makes this within the validated operating envelope required for regulatory approval.
Training AI models on sensitive medical device data without centralizing patient records is a regulatory and ethical requirement. Federated learning architectures let models improve across distributed data sources while keeping PHI on-device and HIPAA-compliant.
We built Össur’s app that turns 3D scans and order forms into prosthetic orders in minutes. That’s the kind of device-adjacent work our engineers do, with clinical workflows and regulated data in mind.
SRS, SDS, risk files, and traceability matrices grow with the code. Your QA lead won’t have to rebuild them a month before submission.
Embedded code, the cloud backend, and the companion app are built by one team. No one has to translate requirements between vendors, and nothing gets lost at the handoff.
The engineers who start your project are still here when you reach certification. The people who wrote your risk file can answer the auditor’s questions about it.
Medical devices rarely work alone. We connect them to EHRs, hospital networks, and clinical systems, and keep patient data safe with HIPAA-compliant architecture.
Safety-critical architecture, embedded constraints, and regulatory traceability aren’t junior-level work. Every team we field is senior-heavy, with experience in regulated healthcare environments.
As this is a regulated discipline, our process is built to deliver compliant, safety-critical software on time. It also includes documentation, traceability, and clinical alignment at every stage.
We define regulatory pathway, software classification under IEC 62304, and risk levels before architecture work begins. These decisions shape the depth of documentation, testing requirements, and the validation scope for everything that follows.
Architecture, system requirements, and IEC 62304-compliant lifecycle structure are locked in before development starts. Our team covers safety constraints, hardware integration, real-time requirements, and the traceability that regulators will audit.
Senior engineers build in iterations with regulatory artifacts, where risk management files, traceability matrices, and verification records are updated continuously. This way, your documentation stays up to date with the codebase.
Functional testing, risk-based test planning, and independent V&V run against the Software Requirements Specification. For SaMD, clinical validation is documented to meet FDA and MDR standards. No submission moves forward without an auditable test record.
We support submissions for FDA 510(k), CE marking, and ISO 13485. Technical documentation, regulatory queries, and post-approval change control included. As your device evolves, we ensure every update remains traceable, validated, and compliant.
Our process is built around IEC 62304 for the software lifecycle, ISO 14971 for risk management, and ISO 13485 for quality management. In the US, that maps to the FDA’s QMSR (21 CFR Part 820, in force since February 2026). In the EU, it maps to MDR or IVDR. 21 CFR Part 11 applies when your system handles electronic records and signatures. Which standards apply depends on your device’s regulatory pathway and software safety class.
IEC 62304 sorts medical device software into three classes based on the harm a software failure could cause. Class A: no injury or damage to health is possible. Class B: non-serious injury is possible. Class C: death or serious injury is possible. The class sets how much documentation, design detail, and testing you need. Class C requires detailed design down to the unit level and the most rigorous verification. We help you set the class early, because getting it wrong means either over-engineering or redoing work before submission.
We address it at the architecture level: threat modeling, secure boot, encrypted communication, role-based access controls, and post-market vulnerability monitoring. For cyber devices, we also prepare the SBOM and the cybersecurity documentation the FDA expects under section 524B, plus the evidence MDR asks for in connected and IoMT devices.
Yes, it’s a standard part of our work. We use HL7 FHIR and custom APIs to connect device software with Epic, Cerner, Athenahealth, and other EHR systems. We also make sure data integrity and access controls match the clinical environment’s security requirements.
We produce and maintain the full regulatory documentation package, including the Software Requirements Specification, Software Design Specification, risk management files, traceability matrices, V&V test records, and cybersecurity documentation. All artifacts are structured to support FDA 510(k), De Novo, or PMA submissions, depending on device classification.
Yes. We cover the full connected stack: device-side firmware, a cloud backend, and companion mobile apps. Security, interoperability, and regulatory compliance are built into every layer, including data transmission protocols and cloud infrastructure.
Yes. We develop and validate AI and ML models for diagnostic support, predictive monitoring, computer vision, and adaptive device behavior. For SaMD in the US, model development follows the FDA’s Total Product Lifecycle approach and predetermined change control plan requirements. For the EU, AI-based medical devices also fall under the EU AI Act’s high-risk requirements on top of MDR, so we plan data governance and model documentation for both from the start.
Testing is planned from the start, based on the IEC 62304 safety class and the ISO 14971 risk assessment. It covers functional verification, integration testing, system validation and, for SaMD, clinical performance validation. Every activity ends up in auditable records aligned with your regulatory pathway.
SaMD is software that performs a medical function independently of hardware. These can be diagnostic apps, clinical decision support tools, AI-powered monitoring platforms, etc. Development for SaMD follows a stricter regulatory path than embedded device software: risk classification under IMDRF and IEC 62304, clinical validation requirements, and FDA or MDR pre-market review, depending on the intended use and risk level. Our team handles the full SaMD development lifecycle, from product definition and risk classification through architecture, development, V&V, and regulatory submission.
It depends on the software safety class, the regulatory pathway (510(k), De Novo, PMA, or CE marking), whether there’s embedded firmware, and how much documentation already exists. A Class A companion app and a Class C embedded system are very different projects. We usually start with a short scoping phase, then give you an estimate with timelines and team composition.
Timelines depend on the same factors: device class, regulatory pathway, and system complexity. Plan for regulatory work too, not only coding. Building the SRS, risk files, and traceability alongside development, instead of after it, is what keeps most projects on schedule.
You can hand us the full project, from regulatory scoping to post-market support, or add our engineers to your in-house team. If you already run a QMS, a dedicated development team can work inside your processes and tools.
If regulatory gaps are slowing your device to market, we’re here to help.
Healthcare
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