{"id":849,"date":"2026-03-05T08:58:22","date_gmt":"2026-03-05T08:58:22","guid":{"rendered":"https:\/\/digiom.fi\/kradi\/?page_id=849"},"modified":"2026-11-03T22:41:39","modified_gmt":"2026-11-03T20:41:39","slug":"fallstudien","status":"publish","type":"page","link":"https:\/\/marshallai.com\/de\/case-studies\/","title":{"rendered":"Fallstudien"},"content":{"rendered":"<div class=\"mai-section\">\n<div class=\"mai-container_inner-content\">\n<div class=\"gb-element-62143277\">\n<h1 class=\"gb-text mai-headline mai-reveal translation-block\">Erprobt am <em>Rand<\/em><\/h1>\n\n\n\n<p class=\"gb-text gbp-section__text mai-reveal\">Von der Beseitigung von Fehlern bei der Produktion \u00fcber die Steuerung der st\u00e4dtischen Infrastruktur bis hin zur L\u00f6sung der schwierigsten visuellen Herausforderungen \u2013 sehen Sie, wie unsere Deep-Learning-Plattform die gr\u00f6\u00dften visuellen Herausforderungen bew\u00e4ltigt und sofortige Erfolge erzielt.<\/p>\n<\/div>\n<\/div>\n\n\n\n<div class=\"mai-container_inner-content\">\n<div><ul class=\"gbp-list--unstyled gb-looper-5cf487fa\">\n<li class=\"gb-loop-item post-853 case-study type-case-study status-publish has-post-thumbnail hentry category-cosmetic-manufacturing category-quality-assurance tag-aerosol-manufacturing tag-case-study tag-computer-vision tag-cosmetic-manufacturing tag-defect-detection tag-edge-computing tag-machine-vision tag-quality-assurance\">\n<article class=\"mai-card-article gb-element-ff0abb7c\">\n<a href=\"https:\/\/marshallai.com\/de\/case-study\/taerosol-automated-qa-in-high-mix-manufacturing\/\"><img loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"512\" alt=\"\" data-media-id=\"4239\" class=\"gb-media-264cf724 mai-card-article-image\" src=\"https:\/\/marshallai.com\/wp-content\/uploads\/2026\/09\/MarshallAI_inspection-station_P1042518-Large-768x512.webp\" srcset=\"https:\/\/marshallai.com\/wp-content\/uploads\/2026\/09\/MarshallAI_inspection-station_P1042518-Large-768x512.webp 768w, https:\/\/marshallai.com\/wp-content\/uploads\/2026\/09\/MarshallAI_inspection-station_P1042518-Large-500x333.webp 500w, https:\/\/marshallai.com\/wp-content\/uploads\/2026\/09\/MarshallAI_inspection-station_P1042518-Large-18x12.webp 18w, https:\/\/marshallai.com\/wp-content\/uploads\/2026\/09\/MarshallAI_inspection-station_P1042518-Large.webp 1280w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \/><\/a>\n\n\n\n<div class=\"gb-element-b7e65b9a\">\n<span class=\"gb-text mai-card-article-title\"><a href=\"https:\/\/marshallai.com\/de\/case-study\/taerosol-automated-qa-in-high-mix-manufacturing\/\">Qualit\u00e4tskontrolle in einer hochmixigen Fertigung meistern: Schnelle Umr\u00fcstungen und &gt;99%-ige Genauigkeit bei Taerosol<\/a><\/span>\n\n\n\n<div class=\"gb-element-462c7cbe\">\n<p class=\"gb-text mai-card-article-text\">Die Auftragsfertigung und die h\u00e4ufigen Produktwechsel bedeuteten fr\u00fcher eine st\u00e4ndige Herausforderung bei der Bew\u00e4ltigung von Qualit\u00e4tssicherungsengp\u00e4ssen. Entdecken Sie, wie Taerosol seine Kosmetiklinie mit einem schnell anpassungsf\u00e4higen KI-System ausgestattet hat, das \u00fcber 99 % der komplexen Etiketten- und Verpackungsfehler erkennt.<\/p>\n<\/div>\n\n\n\n<div class=\"gb-element-62fba4e0\">\n<a class=\"gb-text mai-button-secondary-block\" href=\"https:\/\/marshallai.com\/de\/case-study\/taerosol-automated-qa-in-high-mix-manufacturing\/\">Lesen Sie den Fall<\/a>\n\n\n\n<span class=\"gb-shape gb-shape-8d885a20\"><svg height=\"100%\" width=\"100%\" preserveaspectratio=\"none\" viewbox=\"0 0 1200 40\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n  <path stroke-width=\"2\" stroke=\"currentColor\" fill=\"none\" d=\"M0,20 L1200,20\"><\/path>\n<\/svg><\/span>\n\n\n\n<p class=\"gb-text mai-article-card-tag\"><span>Kosmetische Produktion<\/span> <span>Qualit\u00e4tskontrolle<\/span><\/p>\n<\/div>\n<\/div>\n<\/article>\n<\/li>\n\n<li class=\"gb-loop-item post-2282 case-study type-case-study status-publish has-post-thumbnail hentry category-beverage-industry category-quality-assurance tag-beverage-industry tag-case-study tag-defect-detection tag-drinks-industry tag-edge-computing tag-label-verification tag-quality-assurance tag-rogue-can\">\n<article class=\"mai-card-article gb-element-ff0abb7c\">\n<a href=\"https:\/\/marshallai.com\/de\/case-study\/eliminating-rogue-cans-high-speed-edge-ai-qa-in-beverage-manufacturing\/\"><img loading=\"lazy\" decoding=\"async\" width=\"663\" height=\"634\" alt=\"\" data-media-id=\"4025\" class=\"gb-media-264cf724 mai-card-article-image\" src=\"https:\/\/marshallai.com\/wp-content\/uploads\/2026\/09\/image.webp\" srcset=\"https:\/\/marshallai.com\/wp-content\/uploads\/2026\/09\/image.webp 663w, https:\/\/marshallai.com\/wp-content\/uploads\/2026\/09\/image-500x478.webp 500w, https:\/\/marshallai.com\/wp-content\/uploads\/2026\/09\/image-13x12.webp 13w\" sizes=\"auto, (max-width: 663px) 100vw, 663px\" \/><\/a>\n\n\n\n<div class=\"gb-element-b7e65b9a\">\n<span class=\"gb-text mai-card-article-title\"><a href=\"https:\/\/marshallai.com\/de\/case-study\/eliminating-rogue-cans-high-speed-edge-ai-qa-in-beverage-manufacturing\/\">Rogue-Kanister eliminieren: Hochgeschwindigkeits-QA in der Getr\u00e4nkeherstellung<\/a><\/span>\n\n\n\n<div class=\"gb-element-462c7cbe\">\n<p class=\"gb-text mai-card-article-text\">Ein globaler Getr\u00e4nkehersteller hat 15 Jahre lang versucht, \u201cverr\u00fcckte Dosen\u201d auf seinen Hochgeschwindigkeits-F\u00fcllanlagen zuverl\u00e4ssig zu erkennen. Entdecken Sie, wie MarshallAI ein Edge-Vision-System mit minimaler Latenz einsetzte, um 2.500 Dosen pro Minute zu inspizieren und so unzureichende Produkte sowie kostspielige mechanische St\u00f6rungen mit nahezu perfekter Pr\u00e4zision zu verhindern.<\/p>\n<\/div>\n\n\n\n<div class=\"gb-element-62fba4e0\">\n<a class=\"gb-text mai-button-secondary-block\" href=\"https:\/\/marshallai.com\/de\/case-study\/eliminating-rogue-cans-high-speed-edge-ai-qa-in-beverage-manufacturing\/\">Lesen Sie den Fall<\/a>\n\n\n\n<span class=\"gb-shape gb-shape-8d885a20\"><svg height=\"100%\" width=\"100%\" preserveaspectratio=\"none\" viewbox=\"0 0 1200 40\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n  <path stroke-width=\"2\" stroke=\"currentColor\" fill=\"none\" d=\"M0,20 L1200,20\"><\/path>\n<\/svg><\/span>\n\n\n\n<p class=\"gb-text mai-article-card-tag\"><span>Getr\u00e4nkeindustrie<\/span> <span>Qualit\u00e4tskontrolle<\/span><\/p>\n<\/div>\n<\/div>\n<\/article>\n<\/li>\n\n<li class=\"gb-loop-item post-2213 case-study type-case-study status-publish has-post-thumbnail hentry category-manufacturing category-quality-assurance tag-case-study tag-computer-vision tag-defect-detection tag-edge-computing tag-false-reject-reduction tag-high-speed-inspection tag-machine-vision tag-manufacturing tag-metal-manufacturing tag-quality-assurance tag-sub-millimeter-precision\">\n<article class=\"mai-card-article gb-element-ff0abb7c\">\n<a href=\"https:\/\/marshallai.com\/de\/case-study\/turvanasta-sub-millimeter-defect-detection\/\"><img loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"455\" alt=\"\" data-media-id=\"2172\" class=\"gb-media-264cf724 mai-card-article-image\" src=\"https:\/\/marshallai.com\/wp-content\/uploads\/2026\/03\/MAI-turvanasta-studs2-768x455.webp\" srcset=\"https:\/\/marshallai.com\/wp-content\/uploads\/2026\/03\/MAI-turvanasta-studs2-768x455.webp 768w, https:\/\/marshallai.com\/wp-content\/uploads\/2026\/03\/MAI-turvanasta-studs2-300x178.webp 300w, https:\/\/marshallai.com\/wp-content\/uploads\/2026\/03\/MAI-turvanasta-studs2-1024x607.webp 1024w, https:\/\/marshallai.com\/wp-content\/uploads\/2026\/03\/MAI-turvanasta-studs2.webp 1046w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \/><\/a>\n\n\n\n<div class=\"gb-element-b7e65b9a\">\n<span class=\"gb-text mai-card-article-title\"><a href=\"https:\/\/marshallai.com\/de\/case-study\/turvanasta-sub-millimeter-defect-detection\/\">Qualit\u00e4tskontrolle im Sub-Millimeterbereich in gro\u00dfem Ma\u00dfstab: Sicherung der Ertragssicherheit f\u00fcr Turvanasta<\/a><\/span>\n\n\n\n<div class=\"gb-element-462c7cbe\">\n<p class=\"gb-text mai-card-article-text\">Legacy-Vision-Systeme verworfenen perfekt geeignetes Material. Entdecken Sie, wie MarshallAI eine Edge-Computing-L\u00f6sung implementierte, um falsche Ablehnungen zu eliminieren und Hunderte Millionen von Sub-Millimeter-Metallelementen mit absoluter Pr\u00e4zision zu pr\u00fcfen.<\/p>\n<\/div>\n\n\n\n<div class=\"gb-element-62fba4e0\">\n<a class=\"gb-text mai-button-secondary-block\" href=\"https:\/\/marshallai.com\/de\/case-study\/turvanasta-sub-millimeter-defect-detection\/\">Lesen Sie den Fall<\/a>\n\n\n\n<span class=\"gb-shape gb-shape-8d885a20\"><svg height=\"100%\" width=\"100%\" preserveaspectratio=\"none\" viewbox=\"0 0 1200 40\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n  <path stroke-width=\"2\" stroke=\"currentColor\" fill=\"none\" d=\"M0,20 L1200,20\"><\/path>\n<\/svg><\/span>\n\n\n\n<p class=\"gb-text mai-article-card-tag\"><span>Fertigung<\/span> <span>Qualit\u00e4tskontrolle<\/span><\/p>\n<\/div>\n<\/div>\n<\/article>\n<\/li>\n\n<li class=\"gb-loop-item post-2174 case-study type-case-study status-publish has-post-thumbnail hentry category-smart-cities category-traffic-management tag-case-study tag-computer-vision tag-edge-computing tag-smart-cities tag-traffic-control tag-traffic-counting tag-traffic-management tag-traffic-statistics\">\n<article class=\"mai-card-article gb-element-ff0abb7c\">\n<a href=\"https:\/\/marshallai.com\/de\/case-study\/vantaa-traffic-infrastructure-actuation\/\"><img loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"432\" alt=\"\" data-media-id=\"2135\" class=\"gb-media-264cf724 mai-card-article-image\" src=\"https:\/\/marshallai.com\/wp-content\/uploads\/2026\/04\/MarshallAI-case-study-AI4Cities_multi-view-768x432.webp\" srcset=\"https:\/\/marshallai.com\/wp-content\/uploads\/2026\/04\/MarshallAI-case-study-AI4Cities_multi-view-768x432.webp 768w, https:\/\/marshallai.com\/wp-content\/uploads\/2026\/04\/MarshallAI-case-study-AI4Cities_multi-view-300x169.webp 300w, https:\/\/marshallai.com\/wp-content\/uploads\/2026\/04\/MarshallAI-case-study-AI4Cities_multi-view-1024x575.webp 1024w, https:\/\/marshallai.com\/wp-content\/uploads\/2026\/04\/MarshallAI-case-study-AI4Cities_multi-view.webp 1500w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \/><\/a>\n\n\n\n<div class=\"gb-element-b7e65b9a\">\n<span class=\"gb-text mai-card-article-title\"><a href=\"https:\/\/marshallai.com\/de\/case-study\/vantaa-traffic-infrastructure-actuation\/\">Aktive physische Infrastruktur: Wie MarshallAI 99,8 % Zuverl\u00e4ssigkeit unter schwierigen Bedingungen erreichte<\/a><\/span>\n\n\n\n<div class=\"gb-element-462c7cbe\">\n<p class=\"gb-text mai-card-article-text\">Erfahren Sie, wie die Edge-Vision von MarshallAI komplexe Objekte unter extremen Bedingungen fehlerfrei erkennt und dabei eine Zuverl\u00e4ssigkeitsrate von 99,8 % f\u00fcr die Fu\u00dfg\u00e4ngererkennung erreicht, w\u00e4hrend gleichzeitig physische Infrastruktursteuerungen in Echtzeit ausgel\u00f6st werden.<\/p>\n<\/div>\n\n\n\n<div class=\"gb-element-62fba4e0\">\n<a class=\"gb-text mai-button-secondary-block\" href=\"https:\/\/marshallai.com\/de\/case-study\/vantaa-traffic-infrastructure-actuation\/\">Lesen Sie den Fall<\/a>\n\n\n\n<span class=\"gb-shape gb-shape-8d885a20\"><svg height=\"100%\" width=\"100%\" preserveaspectratio=\"none\" viewbox=\"0 0 1200 40\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n  <path stroke-width=\"2\" stroke=\"currentColor\" fill=\"none\" d=\"M0,20 L1200,20\"><\/path>\n<\/svg><\/span>\n\n\n\n<p class=\"gb-text mai-article-card-tag\"><span>Smart Cities<\/span> <span>Verkehrsmanagement<\/span><\/p>\n<\/div>\n<\/div>\n<\/article>\n<\/li>\n\n<li class=\"gb-loop-item post-2123 case-study type-case-study status-publish has-post-thumbnail hentry category-smart-cities category-traffic-management tag-ai-vision tag-case-study tag-computer-vision tag-emission-reduction tag-machine-vision tag-traffic-control tag-traffic-counting tag-traffic-management\">\n<article class=\"mai-card-article gb-element-ff0abb7c\">\n<a href=\"https:\/\/marshallai.com\/de\/case-study\/ai4cities-intelligent-traffic-intersection-management\/\"><img loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"432\" alt=\"\" data-media-id=\"2133\" class=\"gb-media-264cf724 mai-card-article-image\" src=\"https:\/\/marshallai.com\/wp-content\/uploads\/2026\/04\/MarshallAI-case-study-AI4Cities_co-reduction-768x432.webp\" srcset=\"https:\/\/marshallai.com\/wp-content\/uploads\/2026\/04\/MarshallAI-case-study-AI4Cities_co-reduction-768x432.webp 768w, https:\/\/marshallai.com\/wp-content\/uploads\/2026\/04\/MarshallAI-case-study-AI4Cities_co-reduction-300x169.webp 300w, https:\/\/marshallai.com\/wp-content\/uploads\/2026\/04\/MarshallAI-case-study-AI4Cities_co-reduction-1024x576.webp 1024w, https:\/\/marshallai.com\/wp-content\/uploads\/2026\/04\/MarshallAI-case-study-AI4Cities_co-reduction.webp 1500w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \/><\/a>\n\n\n\n<div class=\"gb-element-b7e65b9a\">\n<span class=\"gb-text mai-card-article-title\"><a href=\"https:\/\/marshallai.com\/de\/case-study\/ai4cities-intelligent-traffic-intersection-management\/\">Intelligenterer Verkehr, weniger Emissionen: MarshallAI und AI4Cities<\/a><\/span>\n\n\n\n<div class=\"gb-element-462c7cbe\">\n<p class=\"gb-text mai-card-article-text\">MarshallAI ist in kritischen Systemen und in kontinuierlicher Betriebsbereitschaft erprobt. Aufgestellt in Helsinki und Paris steuert unsere Deep-Learning-Plattform die Live-Infrastruktur autonom, um Emissionen um bis zu 8 % zu reduzieren. <\/p>\n<\/div>\n\n\n\n<div class=\"gb-element-62fba4e0\">\n<a class=\"gb-text mai-button-secondary-block\" href=\"https:\/\/marshallai.com\/de\/case-study\/ai4cities-intelligent-traffic-intersection-management\/\">Lesen Sie den Fall<\/a>\n\n\n\n<span class=\"gb-shape gb-shape-8d885a20\"><svg height=\"100%\" width=\"100%\" preserveaspectratio=\"none\" viewbox=\"0 0 1200 40\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n  <path stroke-width=\"2\" stroke=\"currentColor\" fill=\"none\" d=\"M0,20 L1200,20\"><\/path>\n<\/svg><\/span>\n\n\n\n<p class=\"gb-text mai-article-card-tag\"><span>Smart Cities<\/span> <span>Verkehrsmanagement<\/span><\/p>\n<\/div>\n<\/div>\n<\/article>\n<\/li>\n\n<li class=\"gb-loop-item post-2091 case-study type-case-study status-publish has-post-thumbnail hentry category-security category-smart-cities tag-ai-security-monitoring tag-case-study tag-crowd-analytics tag-crowd-counting tag-intrusion-detection tag-ip-camera-security tag-legacy-camera-integration tag-local-network tag-privacy-first-ai tag-security tag-security-monitoring tag-theme-park tag-video-management-system tag-visitor-flow tag-vms\">\n<article class=\"mai-card-article gb-element-ff0abb7c\">\n<a href=\"https:\/\/marshallai.com\/de\/case-study\/sarkanniemi-crowd-analytics-and-security-using-edgeai\/\"><img loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"512\" alt=\"\" data-media-id=\"2092\" class=\"gb-media-264cf724 mai-card-article-image\" src=\"https:\/\/marshallai.com\/wp-content\/uploads\/2026\/04\/sarkanniemi-hero-768x512.webp\" srcset=\"https:\/\/marshallai.com\/wp-content\/uploads\/2026\/04\/sarkanniemi-hero-768x512.webp 768w, https:\/\/marshallai.com\/wp-content\/uploads\/2026\/04\/sarkanniemi-hero-300x200.webp 300w, https:\/\/marshallai.com\/wp-content\/uploads\/2026\/04\/sarkanniemi-hero-1024x683.webp 1024w, https:\/\/marshallai.com\/wp-content\/uploads\/2026\/04\/sarkanniemi-hero.webp 1500w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \/><\/a>\n\n\n\n<div class=\"gb-element-b7e65b9a\">\n<span class=\"gb-text mai-card-article-title\"><a href=\"https:\/\/marshallai.com\/de\/case-study\/sarkanniemi-crowd-analytics-and-security-using-edgeai\/\">Aktualisierung der Legacy-Infrastruktur: Crowd Analytics &amp; Security ohne Cloud-Abh\u00e4ngigkeit<\/a><\/span>\n\n\n\n<div class=\"gb-element-462c7cbe\">\n<p class=\"gb-text mai-card-article-text\">Erfahren Sie, wie wir die bestehende Kamerainfrastruktur des Themenparks S\u00e4rk\u00e4nniemi nutzten, um Live-Videos \u00fcber ein lokales Netzwerk mit geschlossenem Kreislauf zu verarbeiten \u2013 was die Analyse von Sicherheit und Massenfluss erm\u00f6glicht, w\u00e4hrend gleichzeitig die Privatsph\u00e4re gewahrt bleibt.<\/p>\n<\/div>\n\n\n\n<div class=\"gb-element-62fba4e0\">\n<a class=\"gb-text mai-button-secondary-block\" href=\"https:\/\/marshallai.com\/de\/case-study\/sarkanniemi-crowd-analytics-and-security-using-edgeai\/\">Lesen Sie den Fall<\/a>\n\n\n\n<span class=\"gb-shape gb-shape-8d885a20\"><svg height=\"100%\" width=\"100%\" preserveaspectratio=\"none\" viewbox=\"0 0 1200 40\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n  <path stroke-width=\"2\" stroke=\"currentColor\" fill=\"none\" d=\"M0,20 L1200,20\"><\/path>\n<\/svg><\/span>\n\n\n\n<p class=\"gb-text mai-article-card-tag\"><span>Sicherheit<\/span> <span>Smart Cities<\/span><\/p>\n<\/div>\n<\/div>\n<\/article>\n<\/li>\n\n<li class=\"gb-loop-item post-2124 case-study type-case-study status-publish has-post-thumbnail hentry category-defence category-edge-ai tag-case-study tag-computer-vision tag-edge-computing tag-edge-device tag-edge-processing tag-machine-vision tag-no-code-training tag-object-detection tag-offline-ai tag-rapid-deployment tag-xtech\">\n<article class=\"mai-card-article gb-element-ff0abb7c\">\n<a href=\"https:\/\/marshallai.com\/de\/case-study\/xtech-challenge-win-rapid-ai-deployment\/\"><img loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"403\" alt=\"\" data-media-id=\"2145\" class=\"gb-media-264cf724 mai-card-article-image\" src=\"https:\/\/marshallai.com\/wp-content\/uploads\/2026\/04\/xTech_MarshallAI_hero-768x403.webp\" srcset=\"https:\/\/marshallai.com\/wp-content\/uploads\/2026\/04\/xTech_MarshallAI_hero-768x403.webp 768w, https:\/\/marshallai.com\/wp-content\/uploads\/2026\/04\/xTech_MarshallAI_hero-300x158.webp 300w, https:\/\/marshallai.com\/wp-content\/uploads\/2026\/04\/xTech_MarshallAI_hero-1024x538.webp 1024w, https:\/\/marshallai.com\/wp-content\/uploads\/2026\/04\/xTech_MarshallAI_hero.webp 1200w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \/><\/a>\n\n\n\n<div class=\"gb-element-b7e65b9a\">\n<span class=\"gb-text mai-card-article-title\"><a href=\"https:\/\/marshallai.com\/de\/case-study\/xtech-challenge-win-rapid-ai-deployment\/\">Von der Idee bis zur Implementierung in nur 1 Stunde und 40 Minuten: Der Sieg beim xTech Global AI Challenge<\/a><\/span>\n\n\n\n<div class=\"gb-element-462c7cbe\">\n<p class=\"gb-text mai-card-article-text\">Erfahren Sie, wie unsere Plattform den xTech Challenge des US-Verteidigungsministeriums mit Hilfe einer benutzerdefinierten, vollst\u00e4ndig offline-basierten Objekterkennungsmethode in nur 1 Stunde und 40 Minuten gewann, wobei lediglich 77 Referenzbilder verwendet wurden.<\/p>\n<\/div>\n\n\n\n<div class=\"gb-element-62fba4e0\">\n<a class=\"gb-text mai-button-secondary-block\" href=\"https:\/\/marshallai.com\/de\/case-study\/xtech-challenge-win-rapid-ai-deployment\/\">Lesen Sie den Fall<\/a>\n\n\n\n<span class=\"gb-shape gb-shape-8d885a20\"><svg height=\"100%\" width=\"100%\" preserveaspectratio=\"none\" viewbox=\"0 0 1200 40\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n  <path stroke-width=\"2\" stroke=\"currentColor\" fill=\"none\" d=\"M0,20 L1200,20\"><\/path>\n<\/svg><\/span>\n\n\n\n<p class=\"gb-text mai-article-card-tag\"><span>Verteidigung<\/span> <span>Edge AI<\/span><\/p>\n<\/div>\n<\/div>\n<\/article>\n<\/li>\n\n<li class=\"gb-loop-item post-2309 case-study type-case-study status-publish has-post-thumbnail hentry category-logistics-supply-chain category-security tag-access-control tag-alpr-automatic-license-plate-recognition tag-border-security tag-case-study tag-edge-ai tag-freight-automation\">\n<article class=\"mai-card-article gb-element-ff0abb7c\">\n<a href=\"https:\/\/marshallai.com\/de\/case-study\/zero-friction-borders-automating-freight-traffic-with-finnish-customs\/\"><img loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"419\" alt=\"\" data-media-id=\"2312\" class=\"gb-media-264cf724 mai-card-article-image\" src=\"https:\/\/marshallai.com\/wp-content\/uploads\/2026\/04\/MarshallAI_Case-Study_Tulli_Truck-768x419.png\" srcset=\"https:\/\/marshallai.com\/wp-content\/uploads\/2026\/04\/MarshallAI_Case-Study_Tulli_Truck-768x419.png 768w, https:\/\/marshallai.com\/wp-content\/uploads\/2026\/04\/MarshallAI_Case-Study_Tulli_Truck-500x273.png 500w, https:\/\/marshallai.com\/wp-content\/uploads\/2026\/04\/MarshallAI_Case-Study_Tulli_Truck-1500x818.png 1500w, https:\/\/marshallai.com\/wp-content\/uploads\/2026\/04\/MarshallAI_Case-Study_Tulli_Truck-1536x838.png 1536w, https:\/\/marshallai.com\/wp-content\/uploads\/2026\/04\/MarshallAI_Case-Study_Tulli_Truck-2048x1117.png 2048w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \/><\/a>\n\n\n\n<div class=\"gb-element-b7e65b9a\">\n<span class=\"gb-text mai-card-article-title\"><a href=\"https:\/\/marshallai.com\/de\/case-study\/zero-friction-borders-automating-freight-traffic-with-finnish-customs\/\">Reibungslose Grenzen: Die Automatisierung des Frachtverkehrs durch die finnische Zollbeh\u00f6rde<\/a><\/span>\n\n\n\n<div class=\"gb-element-462c7cbe\">\n<p class=\"gb-text mai-card-article-text\">MarshallAI hat sich mit der finnischen Zollbeh\u00f6rde zusammengeschlossen und damit Pionierarbeit geleistet, um die Zukunft der automatisierten Grenzkontrolle zu gestalten. Erfahren Sie, wie unsere Edge-Vision-Technologie den Schwerlastverkehr umgehend f\u00fcr den Transit freigibt und so eine grenz\u00fcberschreitungsfreie Erfahrung erm\u00f6glicht.<\/p>\n<\/div>\n\n\n\n<div class=\"gb-element-62fba4e0\">\n<a class=\"gb-text mai-button-secondary-block\" href=\"https:\/\/marshallai.com\/de\/case-study\/zero-friction-borders-automating-freight-traffic-with-finnish-customs\/\">Lesen Sie den Fall<\/a>\n\n\n\n<span class=\"gb-shape gb-shape-8d885a20\"><svg height=\"100%\" width=\"100%\" preserveaspectratio=\"none\" viewbox=\"0 0 1200 40\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n  <path stroke-width=\"2\" stroke=\"currentColor\" fill=\"none\" d=\"M0,20 L1200,20\"><\/path>\n<\/svg><\/span>\n\n\n\n<p class=\"gb-text mai-article-card-tag\"><span>Logistik &amp; Lieferketten<\/span> <span>Sicherheit<\/span><\/p>\n<\/div>\n<\/div>\n<\/article>\n<\/li>\n<\/ul><\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"mai-section\">\n<div 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title=\"Logo von Rani\" src=\"https:\/\/marshallai.com\/wp-content\/uploads\/2026\/02\/Rani-logo.webp\" srcset=\"https:\/\/marshallai.com\/wp-content\/uploads\/2026\/02\/Rani-logo.webp 400w, https:\/\/marshallai.com\/wp-content\/uploads\/2026\/02\/Rani-logo-300x60.webp 300w\" sizes=\"auto, (max-width: 400px) 100vw, 400px\" \/>\n<\/div>\n\n\n\n<div class=\"mai-container-logo\">\n<img loading=\"lazy\" decoding=\"async\" width=\"400\" height=\"80\" class=\"mai-image-logo\" alt=\"\" title=\"Logo der Stadt Lappeenranta\" src=\"https:\/\/marshallai.com\/wp-content\/uploads\/2026\/02\/Lappeenranta-kaupunki-logo.png\" srcset=\"https:\/\/marshallai.com\/wp-content\/uploads\/2026\/02\/Lappeenranta-kaupunki-logo.png 400w, https:\/\/marshallai.com\/wp-content\/uploads\/2026\/02\/Lappeenranta-kaupunki-logo-300x60.png 300w\" sizes=\"auto, (max-width: 400px) 100vw, 400px\" \/>\n<\/div>\n\n\n\n<div class=\"mai-container-logo\">\n<img decoding=\"async\" class=\"mai-image-logo\" alt=\"\" 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title=\"US-Armee-Logo svg\" src=\"https:\/\/marshallai.com\/wp-content\/uploads\/2026\/02\/US-Army-logo-svg.svg\"\/>\n<\/div>\n\n\n\n<div class=\"mai-container-logo\">\n<img decoding=\"async\" class=\"mai-image-logo\" alt=\"\" title=\"Telia-Logo-SVG\" src=\"https:\/\/marshallai.com\/wp-content\/uploads\/2026\/02\/Telia-logo-svg.svg\"\/>\n<\/div>\n\n\n\n<div class=\"mai-container-logo\">\n<img decoding=\"async\" class=\"mai-image-logo\" alt=\"\" title=\"Finnisch_Customs_Tulli_Logo_SVG\" src=\"https:\/\/marshallai.com\/wp-content\/uploads\/2026\/02\/Finnish_customs_Tulli_logo_svg.svg\"\/>\n<\/div>\n\n\n\n<div class=\"mai-container-logo\">\n<img decoding=\"async\" class=\"mai-image-logo\" alt=\"\" title=\"Logo der Stadt Helsinki in SVG-Format\" src=\"https:\/\/marshallai.com\/wp-content\/uploads\/2026\/02\/Helsingin-kaupunki-logo-svg.svg\"\/>\n<\/div>\n\n\n\n<div class=\"mai-container-logo\">\n<img decoding=\"async\" class=\"mai-image-logo\" alt=\"\" title=\"Logo der Streitkr\u00e4fte in SVG\" src=\"https:\/\/marshallai.com\/wp-content\/uploads\/2026\/02\/Puolustusvoimat-logo-svg.svg\"\/>\n<\/div>\n\n\n\n<div class=\"mai-container-logo\">\n<img decoding=\"async\" class=\"mai-image-logo\" alt=\"\" title=\"Logo von Cinia als SVG-Datei\" src=\"https:\/\/marshallai.com\/wp-content\/uploads\/2026\/02\/Cinia-logo-svg.svg\"\/>\n<\/div>\n\n\n\n<div class=\"mai-container-logo\">\n<img decoding=\"async\" class=\"mai-image-logo\" alt=\"\" title=\"Logo von Hafen von Turku als SVG\" src=\"https:\/\/marshallai.com\/wp-content\/uploads\/2026\/02\/Port-of-turku-logo-svg.svg\"\/>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>Proven at the Edge From eliminating false rejects on the factory floor to actuating city infrastructure, see how our deep learning platform solves the hardest visual challenges and delivers immediate ROI. Trusted by<\/p>","protected":false},"author":1,"featured_media":4239,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"content-type":"","page_theme":"","footnotes":""},"class_list":["post-849","page","type-page","status-publish","has-post-thumbnail"],"_links":{"self":[{"href":"https:\/\/marshallai.com\/de\/wp-json\/wp\/v2\/pages\/849","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/marshallai.com\/de\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/marshallai.com\/de\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/marshallai.com\/de\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/marshallai.com\/de\/wp-json\/wp\/v2\/comments?post=849"}],"version-history":[{"count":15,"href":"https:\/\/marshallai.com\/de\/wp-json\/wp\/v2\/pages\/849\/revisions"}],"predecessor-version":[{"id":4637,"href":"https:\/\/marshallai.com\/de\/wp-json\/wp\/v2\/pages\/849\/revisions\/4637"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/marshallai.com\/de\/wp-json\/wp\/v2\/media\/4239"}],"wp:attachment":[{"href":"https:\/\/marshallai.com\/de\/wp-json\/wp\/v2\/media?parent=849"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}