1. Defect labelling tools
For AI, good labels matter most.
Scientific Visual has spent more than ten years working in defect recognition and has developed proprietary software for fast, high‑quality annotation. Assisted labelling workflows guide trained operators through images, suggest regions of interest, and make it easy to create precise masks instead of drawing everything by hand.
Beyond speed, Scientific Visual focuses on label quality. Built‑in tools compare AI‑generated masks with trusted ground truth so that inconsistencies and missed defects are found early, before they contaminate the training set. Your team can perform labelling on its own using these tools, collaborate with Scientific Visual’s trained labellers, or outsource the entire dataset creation process; in every case, the goal is to reach production‑grade labels much faster than with generic tools.
2. Training your first AI model
Once a reliable labelled dataset exists, Scientific Visual trains AI models tailored to your process and defect types.
The service is flexible: you may prefer to stay hands‑off and receive a ready‑to‑use model, or want your engineers involved in reviews and acceptance criteria. Both ways are supported, with clear transparent performance metrics.
Two complementary modes of AI use exist.
In assisted defect detection, the AI highlights likely defects and suggests classifications while a human reviewer confirms or corrects the results. This is the recommended entry point for a first project because it builds confidence and provides continuous feedback to improve the model.
In automatic defect detection, validated models run without human review for routine production steps where stable, high‑confidence performance has been demonstrated. Moving from assisted to automatic is possible at a later stage.
3. Deployment
Scientific Visual supports deployment from first installation to stable day-to-day operation. We help you translate a validated model into a practical production configuration, aligned with your IT constraints, and manufacturing workflow.
To avoid surprises, we assist your team in installing and running the first configuration end-to-end: hardware sizing and computer requirements, environment setup, model packaging, performance checks, and a clear validation procedure so you can confirm results under your own conditions. If you have internal software developers, we provide guidance on maintaining the solution over time, including update strategies, versioning and rollback, and repeatable deployment practices.
Scientific Visual helps in the entire value chain — from ideation to running software and advising the computing infrastructure, and support management – by acting as the “co-intellect” collaborator assisting on the path we have already travelled.
Starting choice: Assisted or automatic defect detection?
Every project begins by choosing the right balance between human expertise and automation.
Scientific Visual helps define where assisted defect detection is most valuable—for example, early development, complex new products, or critical process steps—and where fully automatic detection can safely increase throughput and reduce operator load.
Our team recommends starting with assisted defect detection and expanding toward automation, mid‑size metrology and R&D organizations can adopt AI at their own pace.
Scientific Visual’s role is to provide the tools and expertise to make that first AI defect detection project successful, repeatable, and ready to grow.
