qAnalytics
Scaling MBSE - automation in systems engineering, built for SysML v1 and v2 teams.
Model governance
Every model change gets feedback right away, so issues never get the chance to accumulate.
A lightweight review mechanism gives you continuous control over the model lifecycle.
Enables baseline creation and model diffing, so every state is captured and comparable.
| Project | Lifecycle states | Validation | Changes | Metrics | Pipeline | ||
|---|---|---|---|---|---|---|---|
| State | Reviews | Findings | 2w | Users | Status | Last change | |
Battery Unit |
0 | 6 |
2 | 1 | 2mo | ||
| AirplaneEngineProfile | 0 | 12 |
+34 | 5 | now | ||
| Airplane Model | 1 | 102 |
3 | 7 | 10d | ||
| Chassis | 1 | 10 |
0 | 0 | 11mo | ||
| Drivetrain | 0 | 7 |
+120 | 13 | now | ||
| ElectricVehicle | 0 | 4 |
0 | 0 | 6mo | ||
Digital Threads
Digital threads break down the tool, data, and information silos that many engineering organizations run into today. With qAnalytics, we connect your requirements, MBSE, and engineering tools along the development process — and let AI do what it's best at: connecting large data sets and keeping them in sync.
For example: when a requirement changes, the AI step finds the related elements in your MBSE tool, checks for inconsistencies and errors, and reports them on a dashboard.
Capabilities of qAnalytics
Eight capabilities, one continuous pipeline.
A MBSE pipeline: the automatic sequence every model change runs through.
Each change triggers a series of steps — quality, analysis, documentation, and more — that run automatically and in order, with no manual work required.
Pipelines start on a commit or sync, a schedule, a manual run, or a review being approved or rejected — stages then run sequentially where order matters, or in parallel when checks are independent.
Know exactly where every element stands.
Every model element moves through a tracked lifecycle — so you always know what's changed, what's been reviewed, and what's ready.
Continuous quality
Validation runs automatically as part of the pipeline, checking every change against your own quality rules.
Each commit shows how many validations it resolved and introduced; selecting one lists every erroneous element, filterable by error type, element type, and more.
Four eyes, full audit trail, zero guesswork.
Structured reviews bring a second set of eyes to model changes — with the full history captured automatically.
Every element in the model is colored by its lifecycle state, so blind spots surface immediately: green is approved, yellow is in review, red is modified and needs attention — down to whatever depth you drill into.
Capture a baseline. Stay in sync.
qAnalytics gives you a lean configuration management approach for your MBSE models — so every state of the model is captured, comparable, and never lost.
Every baseline sits on the timeline with its version, author, date and branch — so you can always see exactly what was captured, when, and where it came from.
See your system's health at a glance.
Continuous, up-to-date metrics keep your team ahead of problems instead of reacting to them.
Lifecycle states, change history, and validation findings, all trended over a chosen time range — alongside project metrics like active users, branches, and open reviews.
Extend your pipeline with MBSE AI Agents.
Beyond the default ingest and validation stages, add AI agents to the qAnalytics pipeline that handle open-ended MBSE work automatically. A few examples of what they can do:
Works inside the tools your engineers already use.
qAnalytics plugs into your existing modeling and issue-tracking tools — no workflow disruption.
Runs where your data lives — on your premises.
Built for organizations that need full control over where their engineering data lives.
Built on 9 years of hands-on MBSE experience.
Over the past 9 years, we've supported large engineering organizations — across automotive, aerospace, and medical technology — through every stage of MBSE adoption: methodology, tooling, modeling languages, and training.
qAnalytics was built directly from the problems we saw repeatedly in that work: model changes with no audit trail, quality issues surfacing too late, and no automated way to keep models and issues in sync.
See DevOps for MBSE in action.
Email us and we'll set up a short demo to show how your team's model changes could run through an automated, continuously validated pipeline.
Continuous feedback means fewer surprises at milestones — see how catching issues early translates into faster, higher-quality delivery.