[{"data":1,"prerenderedAt":67},["ShallowReactive",2],{"ind-detail-manufacturing":3,"ind-work-manufacturing":47,"ill-industries\u002Fmanufacturing":66},{"slug":4,"title":5,"icon":6,"summary":7,"challenges":8,"useCases":13,"relatedServices":29,"faq":34,"seo":44},"manufacturing","Manufacturing","gear","AIYatch builds AI for manufacturers that reduces downtime, defects and planning effort: visual quality inspection, predictive maintenance from sensor data, production and demand planning, and document automation for orders and supplier paperwork. Models are grounded in your ERP, MES and machine data and can run at the edge on the factory floor when connectivity or latency demands it.",[9,10,11,12],"Manual inspection misses defects and does not scale with line speed.","Unplanned machine downtime is expensive and maintenance is still schedule-based.","Production planning relies on spreadsheets and tribal knowledge.","Order, delivery and supplier documents are keyed in by hand into ERP systems.",[14,17,20,23,26],{"title":15,"text":16},"Visual quality inspection","Camera-based defect detection and segmentation models trained on your parts, running on edge devices and integrated with line control and reporting.",{"title":18,"text":19},"Predictive maintenance","Anomaly detection and remaining-useful-life models over vibration, temperature and PLC data that trigger maintenance before failure.",{"title":21,"text":22},"Production and demand planning","Forecasting and optimisation models that align production schedules with demand, capacity and material availability.",{"title":24,"text":25},"Supplier and order document automation","Document AI that extracts purchase orders, delivery notes and certificates and posts them into ERP with exception handling.",{"title":27,"text":28},"Shop-floor knowledge assistant","A RAG assistant over manuals, SOPs and maintenance logs that answers technician questions on a tablet, with citations to the source page.",[30,31,32,33],"computer-vision","data-analytics-predictive-ai","ai-automation","open-source-ai",[35,38,41],{"q":36,"a":37},"Can visual inspection run without cloud connectivity?","Yes. We train models in the cloud or on your servers and deploy optimised versions to edge devices or industrial PCs on the line. Inference happens locally with millisecond latency; results and sample images sync to a central dashboard when a connection is available.",{"q":39,"a":40},"How much data do you need for defect detection?","Often a few hundred labelled images per defect type is enough to start, especially with pre-trained models and augmentation. Rare defects are handled with anomaly-detection approaches that learn what “good” looks like. We confirm feasibility on a small sample before committing to full delivery.",{"q":42,"a":43},"How do you connect AI to our ERP or MES?","Through the systems’ APIs, database views or file exports, depending on what is available. We build a thin integration layer with logging and retries so AI outputs appear as work orders, quality records or planning inputs inside the tools your teams already use.",{"title":45,"description":46},"AI for Manufacturing: Inspection & Maintenance | AIYatch","Manufacturing AI that cuts downtime and defects: visual quality inspection, predictive maintenance, production planning, document automation and shop-floor knowledge assistants.",[48],{"path":49,"title":50,"description":51,"industry":5,"services":52,"results":54,"cover":64,"coverAlt":65},"\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.",[30,53,31],"mlops-ai-infrastructure",[55,58,61],{"value":56,"label":57},"~96%","defect recall on the held-out test set",{"value":59,"label":60},"-60%","defective units reaching final inspection",{"value":62,"label":63},"\u003C40 ms","inference per frame at line speed","\u002Fcovers\u002Fwork\u002Fmanufacturing-defect-detection.svg","Abstract navy cover with a rising cyan course line","\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\">\u003Cpath d=\"M360.8 187L375.3 189.2A86 86 0 0 1 375.3 210.8L360.8 213A72 72 0 0 1 355 231.1L355 231.1L365.4 241.4A86 86 0 0 1 352.7 258.9L339.6 252.2A72 72 0 0 1 324.3 263.3L324.3 263.3L326.6 277.8A86 86 0 0 1 306.1 284.5L299.5 271.4A72 72 0 0 1 280.5 271.4L280.5 271.4L273.9 284.5A86 86 0 0 1 253.4 277.8L255.7 263.3A72 72 0 0 1 240.4 252.2L240.4 252.2L227.3 258.9A86 86 0 0 1 214.6 241.4L225 231.1A72 72 0 0 1 219.2 213L219.2 213L204.7 210.8A86 86 0 0 1 204.7 189.2L219.2 187A72 72 0 0 1 225 168.9L225 168.9L214.6 158.6A86 86 0 0 1 227.3 141.1L240.4 147.8A72 72 0 0 1 255.7 136.7L255.7 136.7L253.4 122.2A86 86 0 0 1 273.9 115.5L280.5 128.6A72 72 0 0 1 299.5 128.6L299.5 128.6L306.1 115.5A86 86 0 0 1 326.6 122.2L324.3 136.7A72 72 0 0 1 339.6 147.8L339.6 147.8L352.7 141.1A86 86 0 0 1 365.4 158.6L355 168.9A72 72 0 0 1 360.8 187Z\"\u002F>\u003Ccircle cx=\"290\" cy=\"200\" r=\"30.2\"\u002F>\u003Cpath d=\"M429 268L441.4 268.9A52 52 0 0 1 441.4 285.1L429 286A40 40 0 0 1 423.9 298.2L423.9 298.2L432.1 307.6A52 52 0 0 1 420.6 319.1L411.2 310.9A40 40 0 0 1 399 316L399 316L398.1 328.4A52 52 0 0 1 381.9 328.4L381 316A40 40 0 0 1 368.8 310.9L368.8 310.9L359.4 319.1A52 52 0 0 1 347.9 307.6L356.1 298.2A40 40 0 0 1 351 286L351 286L338.6 285.1A52 52 0 0 1 338.6 268.9L351 268A40 40 0 0 1 356.1 255.8L356.1 255.8L347.9 246.4A52 52 0 0 1 359.4 234.9L368.8 243.1A40 40 0 0 1 381 238L381 238L381.9 225.6A52 52 0 0 1 398.1 225.6L399 238A40 40 0 0 1 411.2 243.1L411.2 243.1L420.6 234.9A52 52 0 0 1 432.1 246.4L423.9 255.8A40 40 0 0 1 429 268Z\"\u002F>\u003Ccircle cx=\"390\" cy=\"277\" r=\"16.8\"\u002F>\u003Cg stroke-width=\"3.5\">\u003Cline x1=\"160\" y1=\"390\" x2=\"520\" y2=\"390\"\u002F>\u003Cline x1=\"160\" y1=\"412\" x2=\"520\" y2=\"412\"\u002F>\u003Ccircle cx=\"160\" cy=\"401\" r=\"11\"\u002F>\u003Ccircle cx=\"520\" cy=\"401\" r=\"11\"\u002F>\u003Crect x=\"200\" y=\"356\" width=\"34\" height=\"34\" rx=\"5\"\u002F>\u003Crect x=\"320\" y=\"356\" width=\"34\" height=\"34\" rx=\"5\"\u002F>\u003Crect x=\"470\" y=\"356\" width=\"34\" height=\"34\" rx=\"5\"\u002F>\u003C\u002Fg>\u003Ccircle cx=\"290\" cy=\"200\" r=\"7\" fill=\"var(--accent, #0E7490)\" stroke=\"none\"\u002F>\u003Ccircle cx=\"390\" cy=\"277\" r=\"7\" fill=\"var(--accent, #0E7490)\" stroke=\"none\"\u002F>\u003Ccircle cx=\"487\" cy=\"373\" r=\"8\" fill=\"var(--accent, #0E7490)\" stroke=\"none\"\u002F>\u003C\u002Fg>\u003C\u002Fsvg>",1792973679956]