AI-powered visual inspection systems that detect defects in real time — purpose-built for automotive, manufacturing, and Industry 4.0 production environments. Faster, more consistent, and at a fraction of the cost of manual inspection.
From factory floors to food processing lines — our systems work wherever visual quality matters.
Detect surface defects, cracks, dimensional errors, and assembly mistakes on production lines in real time.
Classify produce by size, colour, and defect — or verify pill counts, blister pack integrity, and label accuracy.
Inspect printed circuit boards for solder bridges, missing components, polarity errors, and trace defects.
Detect weaving defects, colour inconsistencies, holes, and pattern mismatches across high-speed fabric rolls.
Verify label placement, barcode readability, fill levels, cap integrity, and expiry date printing.
Inspect castings, stampings, welds, and painted surfaces for dimensional accuracy and surface finish — deployed at Tier 1 & Tier 2 automotive component manufacturers.
Our end-to-end pipeline handles everything from image capture to defect classification and alerting.
Industrial cameras, line-scan sensors, or your existing hardware capture frames at line speed.
OpenCV pipelines normalise lighting, remove noise, and segment regions of interest.
Trained YOLO / CNN models classify defects with confidence scores in under 50ms per frame.
Trigger PLC signals, reject mechanisms, dashboards, or email/SMS alerts based on defect type.
Defect trends, yield rates, and shift reports stored and visualised in a real-time Industry 4.0 dashboard — integrates with MES and ERP systems.
Surface defects (scratches, dents, cracks, pinholes), dimensional anomalies (incorrect shape, missing features), colour and texture deviations, contamination (foreign objects, stains), assembly errors (missing components, wrong orientation), and print or label quality issues. The exact defect taxonomy is defined during the project discovery phase based on your product and production line — we do not apply a generic model.
Not necessarily. Many industrial inspection systems work with standard industrial cameras (GigE or USB3 Vision) and good lighting — both of which are far more affordable than they were five years ago. For edge deployment we use NVIDIA Jetson or similar embedded AI hardware, which runs inference locally without a cloud round-trip. We assess your existing equipment during discovery and specify only what you actually need.
Yes — edge deployment is our default recommendation for production-line inspection. Running inference on-device means sub-50ms decision latency, no dependency on internet connectivity, and no raw image data leaving your facility. Cloud connectivity can be added for model updates, aggregate analytics, and remote monitoring dashboards, but the core inspection logic runs locally.
Timeline depends on defect complexity and the quantity and quality of labelled images available. A straightforward binary pass/fail inspection with a good dataset can be trained and deployed in 4–8 weeks. Multi-class defect classification with a diverse defect taxonomy typically takes 10–16 weeks including data collection, annotation, training, validation, and integration with your production line. We provide a detailed timeline after a site visit and requirements discussion.
In production deployments we typically achieve 99%+ detection rates with false-positive rates low enough for unattended operation. The exact figures depend on defect type, lighting conditions, image resolution, and the quality of training data. We run a validation phase before go-live where the system is tested against a held-out labelled dataset — you see the numbers before committing to production deployment.