[For Quality Managers] Understanding the Basis for Judgment. Appearance Inspection AI that can learn from small amounts of data.
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Understanding the basis and risks of AI's judgments. Capable of learning from a small amount of data. Intuitive operation without the need for AI knowledge. Achieving continuous quality management that does not end with a PoC.
The practical "visual inspection AI" for manufacturing sites allows for high-precision learning and operation from a small amount of data with intuitive operation. It is a user-friendly and effective AI tool for site managers. Target Industries: Manufacturing (semiconductors, foundry, automotive, processed foods) / Chemicals / Pharmaceuticals Target Audience: - Production Management Department: Planning and managing production, improving production efficiency, reducing defective products - Production Technology Department: Optimizing production processes, introducing new technologies, maintaining equipment, utilizing automation technologies - Quality Control Department: Inspecting product quality, detecting and addressing defects, establishing quality assurance processes, confirming compliance with standards - DX Promotion Department: Promoting digital transformation, implementing AI, improving operations through data analysis - Companies that have already introduced image inspection machines and face challenges with over-detection Main Features: - Intuitive operation: No AI knowledge required. Easy from AI model creation to operation and quality management. - Learning from a small amount of data: Domain-specific data augmentation technology generates training data tailored to the on-site environment. - Quality management through visualization: XAI technology visualizes the reasoning behind AI decisions, making model improvement easier. - Quality verification for AI: QAAI enables quality verification of data and models, visualization, and management of change points.
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basic information
【Specifications】 1. Operating Interface: Intuitive UI/UX 2. Data Learning: Supports learning from a small amount of data 3. Technology: Domain-specific data augmentation technology, XAI (Explainable AI), QAAI (Quality Assured AI) 4. Annotation Tool: Automatic annotation, multi-class annotation 5. Data Management: Dataset version control features 6. Learning Algorithms - Good Product Learning (Unsupervised Learning): AI model training using only OK data - Defective Product Learning (Supervised Learning): High-precision anomaly detection using both OK and NG data 7. Sample Generation Function: Generation of NG data 【Operating Environment】 - Cloud: Low cost, no infrastructure management required, continuous updates - On-premises: Customization, integration with existing systems *For more details, please refer to the 'Related Catalog' and contact us.
Price information
Negotiable *It varies depending on the requirements and configuration, so please contact us. 【License Option Examples】 1. Cloud-based license 2. On-premises license 3. Cameras and lighting are required separately.
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Applications/Examples of results
【Examples of Use】 1. Semiconductor manufacturing: Wafer inspection, chip appearance inspection, packaging defect inspection 2. Foundry industry: Surface inspection of cast products, internal defect detection, product dimension inspection 3. Automobile manufacturing: Inspection of scratches and dents on body panels, defect detection of engine parts, quality inspection of interior parts 4. Processed food industry: Packaging defect inspection, foreign object detection in contents, error detection in labels 5. Chemical industry: Appearance inspection of chemical products, defect detection in packaging, confirmation of product uniformity 6. Pharmaceutical manufacturing: Inspection of chipping and cracking in tablets, foreign object detection in packaging, error detection in labels *We have prepared "product materials" and "case studies," so please contact us for more information.
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Company information
Our company is an AI startup founded by researchers from the University of Tokyo and the French research institution Inria, with a mission to "improve quality and efficiency through advanced AI technology" specifically for the manufacturing and logistics industries. Our technology meets the needs of quality control, production technology, and DX promotion departments, enabling optimization of production processes and risk management. The founder, Yamamoto, has conducted AI research at Yahoo Japan and Inria, presenting results at top international conferences such as WWW and RecSys. Over 80% of our team consists of researchers who studied AI and computer science at world-class universities like the University of Tokyo, Cambridge, and Imperial College. With their international perspectives and advanced skills, we provide innovative solutions to quality control challenges. In particular, in mission-critical areas that emphasize the interpretability of AI, our XAI (explainable AI) technology brings transparency to the reasoning behind AI decisions for managers in quality control and production technology departments, supporting safer and more reliable system operations. This leads to improvements in production efficiency and quality, promoting sustainable growth in the manufacturing and logistics industries.