MedTech · Development & Approval

Personalized Solutions for MedTech Development & Approval

We help MedTech teams move from device development to approval with structured, compliant and scalable organizational, digital and technical solutions.

30–50% weniger Vorbereitungszeit für Design Reviews AI Traceability Graph · Remote patient monitoring MedTech

01 · the phase

Development and approval is where traceability gaps, documentation overhead and submission rework decide whether a device ships on time. We know these bottlenecks, connecting requirements, design changes, risks and verification evidence across hardware, firmware, app and cloud, and we prove which one is worth moving with a 4-week PoC.

02 · what we hear

Key challenges, and how AI-driven transformation helps.

  • Domänenübergreifende Traceability
    Challenge

    Eine Anforderung liegt im Anforderungssystem, die Firmware, die sie umsetzt, im Repo, der Test, der sie belegt, in einem Spreadsheet, und das Risiko, das sie mitigiert, in einer separaten Datei. Die Traceability-Matrix zu bauen heisst: eine Person gleicht vor jedem Design Review vier Quellen von Hand ab.

    How we help

    Wir erzeugen einen domänenübergreifenden, dynamischen Traceability-Report, der eine konkrete Anwendersicherheits-Anforderung mit dem exakten Firmware-, Codebase- und Cloud-Integrations-Testfall verknüpft, audit-sichere Entwicklung und Compliance-De-Risking über den Produktlebenszyklus.

  • Validierungsdokumentation
    Challenge

    Niemand weiss, ob das technische Dossier vollständig ist, bis ein Prüfer sagt, dass es das nicht ist. Es richtig zu prüfen heisst, es von vorne bis hinten zu lesen, also wird es genau dann geprüft, wenn keine Zeit mehr bleibt, das Gefundene zu beheben.

    How we help

    Wir führen ein automatisiertes Audit auf einem Mock-Technical-File aus, legen kritische Dokumentationslücken offen, etwa Inkompatibilitäten zwischen Hardware- und Software-Lebenszyklen, und generieren die fehlenden Validierungsprotokolle automatisch, um die Zulassungswahrscheinlichkeit zu erhöhen.

03 · use cases

What a 4-week PoC could target.

End-to-end traceability graph

Connect needs, requirements, changes, risks, tests and evidence.

We define the traceability data model and naming standards, align teams on what "done" means, specify the integrations and governance, and deliver an MVP scope and rollout plan, connecting traceability across hardware, firmware, app, cloud and analytics.

What good looks like

  • End-to-end traceability across disciplines
  • Confidence going into design reviews
  • An MVP scope and rollout plan

Automated design-review packs

Standardized, audit-ready traceability packs on demand.

We automate generation of standardized traceability packs for design reviews, the system suggests links and flags missing or inconsistent traceability before the review starts.

What good looks like

  • Design-review preparation reduced by 30–50%
  • Audit-ready packs on demand
  • Gaps flagged before reviews

Verification & validation orchestration

Link V&V tests to requirements and risks so gaps surface early.

We connect verification and validation activities to the requirements and risks they cover, making coverage visible and surfacing gaps early instead of late.

What good looks like

  • Clear requirement-to-evidence coverage
  • Earlier detection of verification gaps
  • Less late rework and fewer change loops

Clinical evidence packaging

Structure clinical evidence for MDR/FDA submission.

We structure and organize clinical evidence so it maps cleanly to submission requirements and is ready to assemble.

What good looks like

  • Submission-aligned evidence structure
  • Faster assembly
  • Fewer late coverage gaps

Submission readiness assessment

A gap check on what your tech file needs next.

We assess your tech file and traceability against submission requirements and return a prioritized gap list with a clear path to readiness.

What good looks like

  • A prioritized readiness gap list
  • Clear next steps to submission
  • Reduced submission risk

04 · proof

Work, re-framed for this phase.

AI Traceability Graph · Remote patient monitoring MedTech

End-to-end traceability across hardware, firmware, app, cloud and analytics, design-review prep down 30–50%. The exact spine an MDR/FDA submission depends on.

Background
A medtech company in remote patient monitoring could not trace changes across hardware, firmware, the mobile app, cloud services and analytics consistently. It was often unclear which requirement a change addressed, which risk it mitigated and which tests provided evidence, slowing design reviews and creating late coverage gaps.
Solution
An AI Traceability Graph connecting user needs, requirements, design changes, risks, tests, defects and evidence across the tools the teams already use. The system suggests links, flags missing or inconsistent traceability, and generates a standardized traceability pack for design reviews.
Results · 12 mo. after engagement
Design-review preparation time reduced by 30% to 50% through automated traceability packs. Earlier detection of verification gaps. Clear end-to-end traceability from requirement to change to test evidence across hardware, firmware, app, cloud and analytics.
inite's role
inite defined the traceability data model and naming standards, aligned teams on what 'done' means from a traceability perspective, specified the integrations and governance, and delivered an MVP scope and rollout plan.

“Before, we lost time proving what a change was for and where the evidence lived. With inite's approach and the traceability graph, we can answer those questions fast, align across disciplines, and go into reviews with confidence.”

Head of R&D · Remote Patient Monitoring MedTech Company

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