Machine vision and predictive models on the factory floor.
We build vision and sensor driven AI for quality inspection, safety monitoring, and predictive maintenance, deployed at the edge where production runs.
Manufacturing is where AI pays back fastest, because defects, downtime, and scrap already have a price attached to them.
Our industrial AI work combines computer vision with sensor and ERP data, so a detected defect becomes a traceable quality record rather than an isolated alert on a screen.
We deploy at the edge for line speed inference, with cloud retraining, so plants keep running even when connectivity does not.
What we deliver
Computer Vision and Industrial AI capabilities.
Visual quality inspection
Defect detection, classification, and measurement on production lines, with confidence thresholds per part.
Predictive maintenance
Sensor and vibration models that flag likely failures before they cause unplanned downtime.
Safety and compliance monitoring
PPE detection, restricted zone alerts, and incident review across camera estates.
OCR and label verification
Reading serials, batch codes, and labels to catch mismatches before shipment.
Edge deployment
Optimised models on industrial hardware for low latency inference without cloud round trips.
Plant system integration
Results written back to Plex ERP, MES, and quality systems so action follows detection.
How we work
A short path from idea to production value.
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01
Define the defect
Work with quality engineers to specify exactly what must be detected and at what tolerance.
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02
Collect and label
Capture representative images and sensor data, including the rare failure cases that matter most.
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03
Train and validate
Model training with false positive and false negative targets agreed by the plant team.
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04
Deploy and monitor
Edge rollout, drift monitoring, and periodic retraining as products and tooling change.
Business outcomes
What changes for your business.
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Less scrap and rework
Defects are caught at the station where they occur rather than at final inspection.
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Fewer unplanned stops
Maintenance moves from reactive to scheduled based on real equipment signals.
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Traceable quality records
Every inspection result lands in the systems auditors and customers ask about.
FAQ
Questions we are asked most.
How much labelled data do we need?
Less than teams expect. We often start with a few hundred labelled examples per defect class and use augmentation and active learning to improve from there.
Will it run without internet?
Yes. Inference runs on edge hardware in the plant. Cloud is used for retraining and reporting, and the line keeps running if the link drops.
Can it integrate with Plex ERP?
Yes. We already build Plex integrations, so inspection outcomes can create quality records, holds, or alerts automatically.
What hardware is required?
Usually industrial cameras with controlled lighting and a compact edge server. We specify it as part of the pilot.
How the work runs
A delivery rhythm you can plan around.
Discover
Workshops to map processes, systems and the outcome you are measured on.
Design
Solution blueprint, data model and delivery plan agreed before we build.
Build
Two week sprints with working software and a demo at the end of each one.
Launch
Migration, UAT, enablement and a cutover plan your teams can rehearse.
Support
Managed support, enhancements and quarterly roadmap reviews.
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