Industry · Industrial
AI Solutions for Manufacturing
Visual inspection, predictive maintenance and safety monitoring on the factory floor.
Overview
On a production line the constraints are physical. Inference has to happen in the cycle time of the machine, cameras sit in poor lighting, and network access is often unreliable or deliberately restricted. Cloud-first AI designs tend to fail here for reasons that have nothing to do with the model.
We build vision and sensor systems for manufacturers that run at the edge, next to the equipment, and sync to central systems when they can. Defect detection is tuned to the cost asymmetry that matters on your line, whether that is a missed defect reaching a customer or a false reject stopping throughput.
The same discipline applies to predictive maintenance: models built on your historian data, validated against actual failure events, and surfaced as a work order in your CMMS rather than an alert nobody owns.
Where AI pays off in Manufacturing
Automated visual inspection
Defect classification on the line, running at cycle time on edge hardware, with a review queue for borderline cases.
Predictive maintenance
Failure and remaining-life models on sensor and historian data, raising work orders in your maintenance system.
Safety and compliance monitoring
PPE and exclusion-zone detection with privacy-preserving processing that keeps footage on site.
Yield and process optimisation
Parameter recommendations that lift first-pass yield, validated against historical batches before deployment.
What we design around
Manufacturing constraints
- Edge inference within machine cycle time
- Operation without reliable network connectivity
- OT and IT segregation, and plant security policy
- Few labelled defect examples for rare failure modes
Services we apply here
How we build for Manufacturing
Proof in production
Related case studies
Frequently asked
Manufacturing: common questions
Can vision models run without internet access on the plant floor?
Yes. Models are compiled for edge devices and run locally, buffering results and syncing to central systems when a connection is available.
We only have a handful of defect images. Is that enough?
Often yes. Rare defects are handled with anomaly detection against normal product plus targeted augmentation, and the labelled set grows from production review as the system runs.
How does predictive maintenance reach our maintenance team?
As a work order in your existing CMMS with the predicted failure mode and confidence attached, not as a separate dashboard someone has to remember to check.
Let’s solve your data challenge