Build medical device software at speed. Keep the evidence intact.
Copilot4DevOps works inside Azure DevOps, where your design inputs, tests and approvals already live. Elicit, analyse and generate with AI — and leave a reviewable record behind every change.
Where medtech delivery actually stalls. It isn't the engineering.
Documentation lags the build
Design inputs shift inside the sprint. The specification, the trace matrix and the test evidence catch up weeks later, assembled by hand from three tools.
Traceability gets rebuilt, not maintained
Links between need, requirement, test and defect are reconstructed for the audit rather than kept current. Nobody trusts the matrix between reviews.
Review is the queue
Ambiguity in a requirement surfaces at verification, not at elicitation. Rework lands on the QA team with the release date already fixed.
You don't have to settle for the status quo. The evidence can be produced by the same act that does the work.
Everyone on the design team, working in the same place.
Business analysts
QA and verification
Engineering
Quality and regulatory
Twelve capabilities. Three that medtech teams reach for first.
Read the annotated drawing, not a description of it
Device inputs arrive as marked-up screens, labelling artwork and hazard diagrams. Images inside uploaded documents and work item fields are detected, kept with their captions and context, and read alongside the text.
Verification cases that trace back on their own
User stories, acceptance criteria, test cases and test steps derived from the work item's own content and published into the backlog with links intact — so coverage is a query, not a spreadsheet.
Catch the untestable requirement at review
Requirement quality is assessed against the failure modes that cause rework — ambiguity, incompleteness, untestable phrasing, missing acceptance criteria — before anything reaches development.
Ask about work items, pipelines and artifacts in natural language. Pick a specialist agent from the prompt or let intent decide; context carries across follow-ups.
Turn documents, notes, transcripts and legacy specifications into structured, publishable work items — then get the next relevant item type suggested from your hierarchy.
Refine content in place inside the work item. Rewrite a description, tighten acceptance criteria, or edit Test Steps tables inline and by chat with their structure preserved.
Before a change is accepted, see what it touches and what has to be re-verified downstream.
Turn a requirement or document into a flow diagram, with editable diagram markdown beside it.
Sketch the interface a requirement implies, so intent is settled before build.
Produce procedures and documents from the live project state rather than a stale copy.
Move content between forms and item types instead of retyping it into a new template.
Answer questions against the project's own artifacts, with the reasoning left on the record.
Surface what an item links to, what changed, and what that means for the work in front of you.
Reusable prompts for the requests your team repeats, so output stays consistent between people.
Ground responses in your standards, templates, glossaries and prior specifications, so output reads like your organisation wrote it.
Assistance becomes automation: agents built from a plain-language description, a template, or your own configuration — triggered manually, on a schedule, or by a DevOps event.
What an auditor asks for. What the platform already holds.
Copilot4DevOps supports your quality process; it does not replace your quality system or certify your product.
Four steps, all of them inside Azure DevOps.
Install into your organisation
It runs as an extension in the Azure DevOps you already use. No migration, no parallel repository of truth.
Point it at your inputs
Upload specifications and attachments. Embedded images are detected, kept with their context and read alongside the text.
Work in the tab you already open
Chat, analyse, generate and AI Edit sit on the work item. Pick a single agent, several, or let intent decide.
Keep the human in the loop
Agents can be set to pause and raise a task when a decision is needed, instead of proceeding on their own.
Used by teams whose requirements are evidence.
Modern Requirements has been the go-to Microsoft partner for requirements management since 2015, working with device, life science and healthcare organisations where every change has to be reviewable.
I have never had such good collaboration with a vendor in my work life.
It plugs into the stack you already run.
The objections worth answering.
Can we let AI touch a controlled design record at all?
Nothing is committed silently. Output is proposed, reviewed and published by a person, and agents can be configured to stop and raise a task instead of acting alone.
Who can run the agents?
Seven distinct permission actions cover management, deletion, execution, enablement, job history, job deletion and task handling — set across the organisation and overridable per project, team or Azure DevOps group.
Does this change how our team works today?
Existing features, workflows and user interactions are unchanged by the V9 architecture migration. The agent layer sits behind the same interface.
Our inputs are drawings and annotated screenshots, not prose.
Images inside uploaded documents, attachments and Azure DevOps fields are detected, kept with their surrounding context and rendered inline, so text and visuals are read together.
What happens to the record when someone leaves?
The trail lives in Azure DevOps against the work items, so history survives staffing changes, tool churn and audits alike.
How long before we see anything?
Install, point it at one existing requirement set, and generate test cases against it in the first session. There is nothing to stand up first.
See it run against your own requirements.
Thirty minutes, your Azure DevOps project, one of your real specifications.