Business wire
OSARO’s latest AI-powered software eliminates costly retraining downtime, streamlines new SKU introduction, and maximizes productivity in dynamic order fulfillment environments
OSARO Unveils AutoModel: AI-Powered Order Fulfillment Robots Adapt to New Products in Real Time
Tim Cox | ZingPR for OSARO | tim@zingpr.com
OSARO®, a global leader in machine-learning-enabled robotics for high-volume fulfillment centers, has launched OSARO AutoModel™. This marks a significant advancement in its OSARO SightWorks™ perception platform. In order fulfillment, where speed is critical, businesses often face days or even weeks of costly downtime to update their robots for new SKUs. In contrast, OSARO AutoModel enables robots to automatically learn and adapt to new items, processes, and workflows with zero downtime. OSARO AutoModel accelerates the introduction and onboarding of new SKUs and increases robot productivity, allowing for greater flexibility and efficiency in kitting, piece-picking, and autobagging.
This press release features multimedia. View the full release here: https://www.businesswire.com/news/home/20241031154653/en/
Unlike other robotic fulfillment solutions, OSARO AutoModel™ enables robots to learn and adapt to new SKUs in real time, accelerating fulfillment processes and maximizing operational efficiency. (Graphic: Business Wire)
“In the fast-paced world of order fulfillment, the ability to adapt quickly to a dynamic mix of SKUs and market opportunities is crucial for success,” said Derik Pridmore, CEO of OSARO. “OSARO AutoModel revolutionizes robotic automation by eliminating the downtime and delays associated with introducing new products. It empowers businesses of all sizes in the e-commerce, manufacturing, distribution, 3PL, and CPG sectors to gain a competitive advantage by offering their customers a superior fulfillment experience.”
OSARO AutoModel™, the core AI engine within the OSARO SightWorks™ platform, expands the capabilities of AI-powered order fulfillment robots by empowering them to learn and adapt to new tasks and environments in real time. OSARO AutoModel is particularly well suited for high-volume piece-picking and kitting applications where robots need to be able to quickly and accurately on-board thousands of SKUs, such as e-commerce, logistics, and manufacturing.
| Key benefits of OSARO AutoModel include: | ||
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Fast deployment: learn new SKUs quickly, minimizing training time and maximizing ROI |
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Maximum autonomy: achieve high pick accuracy and efficiency with OSARO's advanced AI perception algorithms |
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Zero disruption: adapt to new SKUs, packaging, and workflows without costly retraining or reprogramming |
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Continuous improvement: benefit from real-time AI model updates and ongoing performance optimization |
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| Where to get more information | ||
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Watch the OSARO AutoModel video to learn how OSARO is solving a critical challenge in robotic automation: adapting to new products and tasks without costly downtime. |
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To learn more about how OSARO SightWorks™ Perception Software enables robots to perceive, grasp, and perform tasks in the order fulfillment process, visit osaro.com. |
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ABOUT OSARO
OSARO is a leading provider of AI-powered robotics solutions for e-commerce, logistics, and manufacturing. The company's advanced machine-learning software enables industrial robots to perceive, grasp, and manipulate a wide variety of objects, streamlining complex tasks like piece-picking, kitting, and autobagging. OSARO's solutions are designed to improve efficiency, reduce costs, and enhance productivity in high-volume fulfillment centers. In the rapidly evolving world of logistics technology, OSARO offers smarter automation, low-risk adoption, and its signature Hypercare support — from concept exploration to onsite installation. OSARO’s intelligent robots enable goods-to-robot (G2R) use cases, where the greatest gains are yet to be made on the automated warehouse floor. For more information, visit osaro.com.
View source version on businesswire.com: https://www.businesswire.com/news/home/20241031154653/en/
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