Sarcos Technology and Robotics Corporation (PDYNW)
NASDAQ (NMS) NASDAQ (NMS) Primary Summary
PDYNW Chart
Sarcos Technology and Robotics Corporation Investors
| Name | Quantity | Cost | Value | Profit ($) | Gain (%) |
|---|---|---|---|---|---|
| LV Lindsay Voak Greenland Capital Management LP | 306,565 | $33,691.49 | $12,262.6 | -$21,428.89 | -63.6% |
| MA Marie-Andree Alain Federation des caisses Desjardins du Quebec | 3,500 | $2,730 | $140 | -$2,590 | -94.87% |
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Sarcos Technology and Robotics Corporation Profile
| Transportation Infrastructure Industry | Industrials Sector | Benjamin G. Wolff CEO | NASDAQ (NMS) Exchange | 80359A114 CUSIP |
| US Country | 71 Employees | - Last Dividend | - Last Split | 27 Sep 2021 IPO Date |
Overview
Palladyne AI Corp., initially known as Sarcos Technology and Robotics Corporation before its name change in March 2024, is an innovative software company established in 2017. Primarily based in Salt Lake City, Utah, the company specializes in advancing the capabilities of stationary and mobile robotic systems through its state-of-the-art Artificial Intelligence (AI) and Machine Learning (ML) software platforms. By focusing on enhancing the utility and functionality of third-party robotic systems within the United States, Palladyne AI Corp. caters to a diverse range of industries including industrial manufacturing, warehousing and logistics, defense, infrastructure maintenance and repair, energy, aerospace, and aviation. The core of the company's offerings lies in enabling robotic systems to comprehend their surroundings, learn from experiences, and adapt to changing conditions with minimal direct programming and training, thereby revolutionizing the way these systems operate in various environments.
Products and Services
- AI/ML Software Platform:
This platform stands at the forefront of Palladyne AI Corp.'s offerings, designed to imbue robots with the ability to observe, learn from, and interact with both structured and unstructured environments. Its unique proposition is the facilitation of real-time, dynamic operations that allow robotic systems to adapt swiftly to new challenges without the need for extensive programming. By generalizing experiences, these robots can perform functions across a wide spectrum of industries with enhanced efficiency and minimal human intervention.
