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MedTech client

AI Traceability Graph

30–50% less design-review prep

Background

A remote-patient-monitoring and at-home diagnostics company could not trace changes consistently across hardware, firmware, mobile app, cloud, and analytics. Which requirement a change addressed, which risk it mitigated, which tests provided the evidence: often unclear. Design reviews slowed; coverage gaps surfaced late.

Solution

Personalized Solutions to pinpoint the traceability breakpoints, then an AI Traceability Graph connecting user needs, requirements, design changes, risks, tests, defects, and evidence across the tools the teams already use, auto-suggesting links, flagging inconsistencies, and generating standardised traceability packs for design reviews.

Results

Design-review preparation time down 30–50% through automated traceability packs. Verification gaps caught earlier: less late rework, fewer change loops. 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