Matroid

Matroid, Inc. is a computer vision company founded in 2016 by Reza Zadeh, an Adjunct Professor at Stanford University. Zadeh, who also serves as the company's CEO, has an extensive background in machine learning, having been a founding team member at Databricks and having co-authored Apache Spark's MLlib. His academic and professional work in distributed computing and machine learning, including creating the algorithm for Twitter's "Who-To-Follow" feature, provided the foundation for Matroid.

The company provides a no-code platform that enables enterprises to create and deploy computer vision models, referred to as detectors, without needing programming expertise. This platform allows users to analyze live and archived video streams to identify objects, people, defects, and events in real-time. Matroid's user-friendly interface allows domain experts to train and deploy these detectors quickly, a process that might otherwise take months. The platform is versatile, serving industries such as manufacturing, security, industrial IoT, and aerospace. Clients can use the technology for various applications, including safety compliance, quality inspection, and monitoring standard operating procedures. The business model involves providing its software through cloud-based and on-premise solutions, working with existing camera infrastructures. Matroid has formed partnerships with companies like Oracle, HP, and Eagle Eye Networks to integrate its technology.

Matroid has raised a total of $33.5 million in funding. Key funding rounds include a $3.5 million Seed round in January 2017, a $10 million Series A in September 2017, and a $20 million Series B in October 2020. The Series B round was led by Energize Ventures and included participation from previous investors like New Enterprise Associates (NEA) and Intel Capital. This funding is aimed at accelerating product development and expanding the company's presence in the manufacturing and industrial IoT sectors.

Keywords: computer vision, no-code platform, machine learning, AI, video analytics, object detection, defect detection, industrial IoT, manufacturing automation, safety compliance, process monitoring, real-time alerts, enterprise AI, deep learning, visual inspection, asset tracking, Reza Zadeh, Stanford, AI infrastructure, video surveillance analytics