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.

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.

  • Cross-domain traceability
    Challenge

    Product information is fragmented across codebases, schematics, firmware, mobile apps and cloud repositories, making an accurate, validated, audit-proof traceability matrix for user safety hard to maintain.

    How we help

    We generate a cross-domain dynamic traceability report that links a specific user-safety requirement to the exact firmware, codebase and cloud integration test case, audit-proof development and compliance de-risking across the product life cycle.

  • Validation documentation
    Challenge

    Validation documentation depends on manual research and entry, so it is hard to stress-test the technical file comprehensively and find gaps before submission.

    How we help

    We run an automated audit on a mock technical file, surfacing critical documentation gaps such as hardware/software life-cycle incompatibilities and auto-generating the missing validation protocols to raise approval success.

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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