Computer Vision Development Services for Enterprise-Grade Automated Visual Inspection

Automated visual inspection uses cameras and computer vision to check products, assets and work areas against a defined standard, flag deviations and record visual evidence for every decision.

Image sources such as cameras, CCTV, drones or phones, suitable lighting, trained models, edge or cloud compute, a review screen for uncertain cases and integrations with your business systems.

Feasibility testing, data annotation, model training, validation, edge or cloud deployment, system integration, review interfaces and ongoing monitoring and retraining.

Surface defects, missing or incorrect parts, label and print errors, dimensional issues, missing PPE, restricted-zone entry, fire and smoke, and displays that do not match the standard.

Computer vision is the field of AI that enables machines to interpret images and video, detecting objects, classifying conditions and spotting anomalies the way a trained inspector would, at scale.

Traditional machine vision follows fixed rules and struggles with variation. AI learns from examples and handles changing lighting, materials and new defects. Many systems combine both.

Accuracy depends on your images and defect types, so we measure it on your own held-out images and report escape rate and false rejects separately.

A few hundred labeled images per defect type is a practical start. Anomaly models can learn from good samples alone.

Edge suits real-time or offline needs like production lines and live safety alerts. Cloud suits large volumes from distributed sites. Many deployments use both.

Often, yes. We test image quality first and recommend new cameras or lighting only when the results show it is needed.

Yes. Results can trigger PLC rejects and post to MES, ERP, maintenance and EHS systems through standard integrations.

Uncertain cases go to a reviewer with a marked-up image and confidence score. Their decision becomes new training data.

We focus on equipment and behavior, not identity, blur faces and number plates where they are not needed, and keep footage in your chosen region or on-premise.

Yes. We combine motion-aware models, thermal cameras and temporal checks to tell fire and smoke apart from fog, dust or steam, with physical sensors as backup.

A feasibility test takes 1 to 2 weeks. A first inspection point typically reaches a monitored pilot in 8 to 12 weeks.

Yes. Share sample images under NDA and we will report what can be detected, expected accuracy and the recommended setup.

You do. All models, label sets, pipelines and images belong to you, and your images are never used to train models for others.

Yes. US businesses engage us through Triazine Software LLC, with US contracting and a US team. Call +1 (347) 941-1655 or email sales@triazinesoft.com.