Mobius · Build & deploy your own AI Apps
Build computer vision applications, not just models.
Store media, build datasets, train models, and ship the operational logic around them, in one environment, on one continuous loop.
- Datasets
- Models
- AI Apps
- Inference
A model returns a detection. An application makes a decision.
A trained model gives you a class and a confidence score. Everything that makes it operationally useful sits outside it: severity, thresholds, scene geometry, event logic, alerting, and the plug-ins that turn a detection into an action.
Mobius builds both in the same environment, versioned together, so the severity, threshold, and alerting logic behind a detection ships with the model rather than waiting on a separate engineering build.
Annotate once. Train many.
A label schema separates what your experts know from what any one model consumes. Classes, sub-classes, severity, notes and custom fields are captured once, with your subject-matter experts, against data you already hold.
Every model trained afterwards draws from that same source. No re-labelling per model. No drift between teams. No standards debate on the third project.
- 01Define the schema with subject-matter experts, not annotators working from a spreadsheet
- 02Interpret existing archives against it, rather than starting from zero
- 03Train detectors, classifiers and segmentation models from one annotated corpus
A workflow you can see.
Compose models, logic and plug-ins into an application on a node-based canvas. Configure inputs, outputs and parameters in place.
Dataset and model versioning is tracked throughout, so any result traces back to the data and configuration that produced it.
From build to inference without an export step.
Automation triggers route the right media to the right application. Inference runs against live streams and archived media alike.
There is no model export, no separate serving stack, and no handoff to another team to get a result into production.
Monitor the model and the logic separately.
A model can be accurate while the application around it is wrong. Mobius evaluates both, model performance and logic performance, as distinct signals.
Corrections re-enter the label schema. Retrain several models at once against the same validation set, compare them directly, and promote the one that performs.
- 01Monitor multiple models running simultaneously in production
- 02Evaluate plug-in and threshold logic independently of model output
- 03Retrain, compare against a fixed validation set, and promote deliberately
Start from an application, not a model.
A model catalogue gives you a starting point. An AI App catalogue gives you a working configuration, model, logic, thresholds and outputs, that you adapt to your assets and your standards.
Explore AI AppsOverhead line inspection
Component detection, severity classification and defect reporting across pole and tower assets.
Turbine blade survey
Surface defect detection with severity banding and repeat-visit comparison.
Conveyor monitoring
Continuous stream inference with threshold logic and operator alerting.
Site activity
Zone-based event logic built on scene intrinsics and extrinsics.
Fits the workflow you already run.
Applications built in Mobius deliver into Prism and Orb. Results reach inspection and operations teams in the tools they already use, rather than arriving as a payload someone has to find a home for.
Build your first application.
Mobius is available to enterprise teams on request. Tell us what you inspect and we'll show you the loop against your own media.
