[{"data":1,"prerenderedAt":71},["ShallowReactive",2],{"sol-detail-ai-quality-inspection":3,"sol-work-ai-quality-inspection":32,"ill-solutions\u002Fai-quality-inspection":70},{"slug":4,"title":5,"problem":6,"answer":7,"outcomes":8,"services":13,"industries":18,"faq":22,"seo":29},"ai-quality-inspection","AI Quality Inspection","Manual inspection misses defects, slows the line and produces inconsistent results between shifts.","AI quality inspection uses computer-vision models trained on images of your products to detect defects, measure dimensions and verify assembly in real time, running on cameras and edge devices on the line. Results feed reject mechanisms and quality dashboards. AIYatch handles data collection, labelling strategy, model training, edge deployment and integration with your MES or quality systems.",[9,10,11,12],"Consistent, objective inspection at line speed","Fewer escaped defects and less rework or scrap","Traceable inspection records per unit or batch","Early warning when defect rates drift",[14,15,16,17],"computer-vision","mlops-ai-infrastructure","open-source-ai","data-analytics-predictive-ai",[19,20,21],"manufacturing","energy-utilities","logistics",[23,26],{"q":24,"a":25},"What if we have very few examples of defects?","That is common. We use anomaly-detection models that learn from good units, synthetic augmentation and active learning that asks operators to label the most useful new images. As real defects are captured in production, the model improves. We confirm feasibility on a small pilot before committing to full delivery.",{"q":27,"a":28},"What hardware is needed?","Usually industrial cameras with suitable lighting and an edge device such as an NVIDIA Jetson or industrial PC. We help specify the setup during discovery, and existing cameras can often be reused. Models can also run on your servers if latency allows, and we integrate results with reject mechanisms and your quality or MES systems.",{"title":30,"description":31},"AI Visual Quality Inspection for Manufacturing | AIYatch","Detect defects at line speed with computer-vision inspection trained on your products, deployed on edge devices and integrated with your MES and quality dashboards.",[33,51],{"path":34,"title":35,"description":36,"industry":37,"services":38,"results":39,"cover":49,"coverAlt":50},"\u002Fwork\u002Fmanufacturing-defect-detection","Computer-vision defect detection on a packaging line","Camera-based inspection that catches print, seal and label defects in real time on a high-speed packaging line, with results fed to the plant's quality system.","Manufacturing",[14,15,17],[40,43,46],{"value":41,"label":42},"~96%","defect recall on the held-out test set",{"value":44,"label":45},"-60%","defective units reaching final inspection",{"value":47,"label":48},"\u003C40 ms","inference per frame at line speed","\u002Fcovers\u002Fwork\u002Fmanufacturing-defect-detection.svg","Abstract navy cover with a rising cyan course line",{"path":52,"title":53,"description":54,"industry":55,"services":56,"results":59,"cover":69,"coverAlt":50},"\u002Fwork\u002Fmultimodal-product-catalogue","Multimodal AI catalogue system for a home-and-garden retailer","Vision-language models that turn supplier photos and spec sheets into structured, searchable product listings, with attribute extraction, image tagging and visual search.","Retail & E-commerce",[57,14,58],"multimodal-ai","generative-ai-llm",[60,63,66],{"value":61,"label":62},"~10×","faster new-product listing",{"value":64,"label":65},"~93%","attribute accuracy on the QA sample",{"value":67,"label":68},"+9%","search-to-product-page click-through","\u002Fcovers\u002Fwork\u002Fmultimodal-product-catalogue.svg","\u003Csvg xmlns=\"http:\u002F\u002Fwww.w3.org\u002F2000\u002Fsvg\" viewBox=\"0 0 640 480\" role=\"img\" aria-hidden=\"true\">\u003Cg fill=\"none\" stroke=\"currentColor\" stroke-width=\"5\" stroke-linecap=\"round\" stroke-linejoin=\"round\">\u003Ccircle cx=\"320\" cy=\"210\" r=\"104\"\u002F>\u003Cline x1=\"393.5\" y1=\"136.5\" x2=\"438.8\" y2=\"91.2\" stroke-width=\"12\"\u002F>\u003Cpath d=\"M250.7 250A80 80 0 0 1 244.8 182.6\" stroke-width=\"3.5\" stroke-opacity=\"0.45\"\u002F>\u003Crect x=\"262\" y=\"170\" width=\"116\" height=\"84\" rx=\"14\"\u002F>\u003Ccircle cx=\"342\" cy=\"236\" r=\"16\" stroke-width=\"3.5\"\u002F>\u003Cline x1=\"336\" y1=\"230\" x2=\"348\" y2=\"242\"\u002F>\u003Cline x1=\"348\" y1=\"230\" x2=\"336\" y2=\"242\"\u002F>\u003Ccircle cx=\"342\" cy=\"236\" r=\"5\" fill=\"var(--accent, #0E7490)\" stroke=\"none\"\u002F>\u003Cg stroke-width=\"3.5\">\u003Cline x1=\"60\" y1=\"372\" x2=\"580\" y2=\"372\"\u002F>\u003Cline x1=\"60\" y1=\"400\" x2=\"580\" y2=\"400\"\u002F>\u003Ccircle cx=\"90\" cy=\"386\" r=\"8\" stroke-width=\"3.5\" stroke-opacity=\"0.45\"\u002F>\u003Ccircle cx=\"180\" cy=\"386\" r=\"8\" stroke-width=\"3.5\" stroke-opacity=\"0.45\"\u002F>\u003Ccircle cx=\"270\" cy=\"386\" r=\"8\" stroke-width=\"3.5\" stroke-opacity=\"0.45\"\u002F>\u003Ccircle cx=\"360\" cy=\"386\" r=\"8\" stroke-width=\"3.5\" stroke-opacity=\"0.45\"\u002F>\u003Ccircle cx=\"450\" cy=\"386\" r=\"8\" stroke-width=\"3.5\" stroke-opacity=\"0.45\"\u002F>\u003Ccircle cx=\"540\" cy=\"386\" r=\"8\" stroke-width=\"3.5\" stroke-opacity=\"0.45\"\u002F>\u003Crect x=\"100\" y=\"320\" width=\"80\" height=\"52\" rx=\"10\"\u002F>\u003Crect x=\"460\" y=\"320\" width=\"80\" height=\"52\" rx=\"10\"\u002F>\u003Cpath d=\"M132.3 346L137.9 351.6L149.1 339.7\"\u002F>\u003C\u002Fg>\u003Cg stroke-width=\"3.5\">\u003Crect x=\"280\" y=\"326\" width=\"80\" height=\"46\" rx=\"10\"\u002F>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fsvg>",1792973682660]