Ripcord

Ripcord is a robotic digitization company that develops platforms and software to convert physical documents into digital, searchable data and streamlined workflows. Its Canopy solution uses artificial intelligence and machine learning to capture, enrich, and organize content, enabling enterprises to simplify processes and improve accuracy. The company combines sensors, automation, and vision-guided robots to create a digital twin of information, perform document scanning and recognition, and accelerate data capture, organization, and retrieval. This supports faster, higher-quality processing and moves organizations toward paperless document management and more efficient operations. Headquartered in Los Angeles, Ripcord was founded in 2014.

Michael Coombs

EVP of Finance

Joni Woo

SVP of Customer Success

3 past transactions

VASTEC

Acquisition in 2025
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 offers an AI-driven enterprise solution that converts paper documents into structured data efficiently. It employs advanced handwriting recognition technology to accommodate various templates and writing styles, aiming to automate repetitive office tasks and maximize productivity.

Engine ML

Acquisition in 2020
Engine ML Inc. is a technology company based in San Francisco, California, specializing in distributed deep learning solutions. Founded in 2018, Engine ML addresses significant challenges in deep learning by enabling training processes to occur up to 50 times faster than conventional methods. This acceleration allows clients to reduce training times, facilitating the development of superior models in areas such as computer vision, natural language processing, speech, and robotics. The company provides infrastructure that leverages the computational power of multiple GPUs, streamlining access to machine learning engineering cycles. Engine ML aims to optimize the utilization of deep learning engineers, alleviating the burdens of building costly and complex infrastructures while tackling the critical issue of distributed training in machine learning.
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