AI Visual Inspection & Defect Detection — Where Standard Systems Fail

Q: How is AI visual inspection different from traditional AOI?

Traditional automated optical inspection (AOI) relies on fixed rules and templates: thresholds, golden-image comparison, hand-tuned filters. It works when parts and lighting are consistent. AI visual inspection learns the appearance of good and defective parts from examples, so it tolerates variation — lighting, texture, position — and catches defect types that rules were never written for.

Q: Can you detect defects with very limited labeled data?

Yes. Many real inspection problems have few labeled defects because defects are rare. We use anomaly detection, self-supervised pretraining, synthetic data and augmentation so a system can flag deviations from normal even when you have only a handful of defective examples.

Q: What materials and defect types can you handle?

We work across metal, welds, PCBs, textiles and fabric, composites and painted or coated surfaces — detecting cracks, scratches, porosity, contamination, missing or misplaced components and other surface and structural defects.

Custom computer-vision and deep-learning inspection for the parts rule-based AOI and off-the-shelf machine vision can't handle: low contrast, variable lighting, limited labeled data, and defect types nobody wrote a rule for.

Tell us about your inspection problem

PoC on your images in 3 weeks, not 3 months.

The problem

When automated optical inspection breaks down

Classic AOI and template-based machine vision assume a consistent world. Real production lines aren't consistent. These are the cases where teams come to us.

Low contrast & variable lighting

Micro-cracks, scratches and surface defects that barely register against the background — and change appearance with every shift in lighting, angle or vibration.

Very limited labeled data

Defects are rare, so you have thousands of good parts and a handful of bad ones. Supervised templates have nothing to learn from; anomaly detection does.

Novel & unpredictable defects

New defect types appear that no rule anticipated. A learned model of "normal" flags the deviation instead of waiting for someone to write a new rule.

Harsh, non-standard conditions

Vibration, electromagnetic interference, reflective or curved surfaces, line speed — environments where off-the-shelf vision pipelines quietly lose accuracy.

What we build

Custom AI defect detection, end to end

Most real projects sit at the intersection of computer vision, signal processing and edge engineering. We build the whole pipeline, not just a model.

AI optical inspection

Deep-learning automated optical inspection that goes beyond fixed rules — detecting surface defects, missing or misplaced components and contamination under real-world variation.

Anomaly & surface defect detection

Surface defect detection and anomaly detection that learn "normal" from mostly-good data, so the system flags deviations even with few labeled defects.

Real-time inspection on the edge

Models optimized for line speed and deployed on industrial PCs, GPUs, ARM or accelerators — real-time defect detection without depending on the cloud.

Integration & handover

Wired into PLCs, cameras and your MES/QA systems, documented and inspectable, built on open source so your team can run and extend it after we're gone.

Coverage

Defect detection across materials

Industrial computer vision tuned to the material and the defect — not a one-size-fits-all template.

Weld defect detection
Metal surface defects
PCB defect detection
Textile & fabric defect detection
Composite micro-cracks
Painted & coated surfaces
Casting porosity
Assembly & completeness checks

Typical problem

Detecting micro-cracks in composites under variable lighting and vibration — low contrast, unpredictable angles and very few labeled examples, exactly where standard vision pipelines fail. We start by finding out, on your data, whether it can be solved at all.

Bring us your hardest inspection case

FAQ

AI visual inspection — common questions

How is AI visual inspection different from traditional AOI?

Traditional AOI relies on fixed rules and golden-image comparison — great when parts and lighting are consistent. AI visual inspection learns the appearance of good and defective parts from examples, so it tolerates variation and catches defect types that rules were never written for.

Can you detect defects with very limited labeled data?

Yes. Because defects are rare, we lean on anomaly detection, self-supervised pretraining, synthetic data and augmentation — so the system can flag deviations from normal with only a handful of defective examples.

Can it run in real time on the production line or at the edge?

Yes. We optimize models for real-time inference and deploy on edge hardware (industrial PCs, GPUs, ARM, accelerators) so inspection runs at line speed without a constant cloud connection.

What materials and defect types can you handle?

Metal, welds, PCBs, textiles and fabric, composites and painted or coated surfaces — cracks, scratches, porosity, contamination, missing or misplaced components and other surface and structural defects.

How fast can we find out if it works on our data?

Typically a working proof of concept in about three weeks, run on your images and in your conditions — so you learn early whether the approach is viable before committing to a full build.

Talk

Have an inspection problem standard tools can't solve?

Tell us the part, the defect and what you've already tried. We read every submission and reply within 7 days.

Tell us about your challenge