Ripcord, Inc. is a robotic digitization company that specializes in automating the management of paper records. Founded in 2014 and headquartered in Los Angeles, California, Ripcord develops a platform that utilizes advanced technologies, including artificial intelligence, machine learning, and vision-guided robotics, to create digital twins of physical documents. Its flagship solution, Canopy, captures, enriches, and organizes critical content, allowing enterprises to enhance their business processes with improved speed, quality, and accuracy. Formerly known as Ripcord Digital, Inc., the company rebranded in June 2017 to reflect its focus on innovative digitization solutions.
VASTEC is a small business with big capability. Headquartered in Tampa, with locations in Alexandria, VA, Dallas, and Houston, VASTEC has earned a reputation for quality of service.Founded in 2006, the VASTEC mission is to provide quality products and services utilizing its experienced and skilled personnel, ensuring accurate and timely delivery to clients,while maintaining a secure environment where client-sensitive data is protected at the highest level.
LearningPal
Acquisition in 2022
LearningPal is a developer of an AI-based enterprise platform focused on digitizing paper documents into well-structured data. The platform utilizes advanced image and handwriting recognition technology, allowing it to process various templates and handwriting styles effectively. By automating repetitive office tasks, LearningPal aims to enhance productivity and enable clients to achieve maximum output while minimizing costs. The solution is designed to save time and improve efficiency in document processing, ultimately unleashing the potential of employees within organizations.
Engine ML
Acquisition in 2020
Engine ML Inc., established in 2018 and based in San Francisco, California, specializes in distributed deep learning technology solutions. As a subsidiary of Ripcord, Inc., the company addresses the challenges associated with training deep learning models, which can be time-consuming and resource-intensive. Engine ML's infrastructure leverages the computational power of numerous GPUs, enabling clients to accelerate their model training processes by up to 50 times. This capability allows organizations to optimize their use of deep learning engineers and gain a competitive edge by developing high-quality models in fields such as computer vision, natural language processing, speech, and robotics. By providing a more efficient and scalable alternative to traditional infrastructure, Engine ML aims to streamline the deep learning workflow and alleviate the burdens of manual training.
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