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% de temps de préparation des revues de conception en moins 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.

  • Traçabilité inter-domaines
    Challenge

    Une exigence vit dans le système d'exigences, le firmware qui l'implémente dans le dépôt, le test qui la prouve dans un tableur, et le risque qu'elle couvre dans un fichier à part. Construire la matrice de traçabilité, c'est une personne qui réconcilie quatre sources à la main avant chaque revue de conception.

    How we help

    Nous générons un rapport de traçabilité dynamique inter-domaines qui relie une exigence précise de sécurité utilisateur au cas de test exact de firmware, base de code et intégration cloud, développement à l'épreuve d'audit et réduction du risque de conformité sur le cycle de vie du produit.

  • Documentation de validation
    Challenge

    Personne ne sait si le dossier technique est complet tant qu'un évaluateur n'a pas dit le contraire. Le vérifier correctement suppose de le lire de bout en bout, si bien qu'il est vérifié précisément au moment où il ne reste plus de temps pour corriger ce qui remonte.

    How we help

    Nous menons un audit automatisé sur un dossier technique fictif, révélant les lacunes documentaires critiques telles que les incompatibilités de cycle de vie matériel/logiciel et générant automatiquement les protocoles de validation manquants pour augmenter le succès d'approbation.

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

Réserver un appel

Choisissez l’échange qui correspond à votre situation. Les deux sont gratuits et durent 30 minutes. Pas de deck, pas de pitch.