Vertical
Manufacturing
Defect detection at line speed and failure prediction before downtime — running entirely on your factory floor, no production data leaving the facility.
The challenge
Manual inspection is slow, inconsistent, and expensive at scale. Predictive maintenance based on rule-based thresholds misses subtle degradation patterns and generates alert fatigue. Cloud-based AI introduces latency and IP exposure — a serious concern in precision manufacturing where process know-how is core competitive IP.
The goal is a system that catches defects early, predicts equipment failures before they become downtime, and runs entirely on the factory floor — with no sensitive production data leaving the facility.
Our approach
- ▲ Visual defect detection. Inspection models that catch surface defects, dimensional deviations, and assembly failures — deployed on-device at line speed. Typical outcome: false-positive rate under 3%, zero manual re-inspection queues for passing parts.
- ▲ Predictive maintenance. Vibration, temperature, and acoustic signals fused to detect early equipment degradation — 48–72 hours before a threshold breach. Unplanned stoppages replaced by planned service windows.
- ▲ Right-sized models for your hardware. Automated search for the smallest model that hits your accuracy and latency targets. Runs on your existing embedded hardware.
- ▲ Production data stays on-premise. All inference, training data, production imagery, and sensor logs remain inside your factory network. No IP exposure, no vendor lock-in on the inference stack.
In practice
Visual Inspection Deployment
Developed and deployed an automated neural architecture search pipeline for a manufacturing quality inspection use case. Delivered a model 3× smaller than the baseline with equivalent detection accuracy — running entirely on embedded hardware, no GPU server required.
Multi-Site Quality & Maintenance AI
Active Horizon Europe proposal combining vision-based inspection and predictive maintenance across a consortium of European manufacturers — targeting cross-site model generalisation without sharing raw production data between sites.
Pižurica et al. Generic neural architecture search toolkit for efficient and real-world deployment of visual inspection CNNs in industry. Published methodology underpinning our inspection deployment work.
Have a quality or maintenance challenge?
We scope quickly and build incrementally — starting with the highest-ROI use case.
Get in touch