Tuurny
San Francisco physical AI startup deploying precision robotics and computer vision to surgically extract RAM chips and components from e-waste at scale.
Tuurny is a San Francisco-based physical AI and robotics startup building an intelligent platform for the circular economy, with a focus on recovering critical semiconductor components from electronic waste. The company was founded by Sina Ghashghaei (CEO) and operates with a mission to address U.S. chip supply shortages by recovering intact, reusable memory modules and rare earth materials from discarded electronics rather than exporting them as bulk scrap.
The company's core system moves beyond conventional e-waste shredding. Using advanced computer vision, Tuurny's robots identify individual components on circuit boards — including RAM ICs, capacitors, and connectors — assess their condition, and then use precision robotic actuators to surgically remove them intact without damage. The system also databases recovered components for traceability and secondary market reuse.
Tuurny's flagship robotic cell is called Nantul, a fully automated system that integrates computer vision with precision robotics and thermal processing. Nantul is capable of autonomously identifying, thermally treating, and extracting up to 300 intact RAM ICs per cell per hour, operating without manual intervention. The company secured its first six-figure commercial contract prior to public announcement, validating commercial demand.
Tuurny was accepted into the NVIDIA Inception Program, which supports AI startups revolutionizing industries. The company also received a NASA grant to collaborate with Texas A&M University on the development of its advanced computer vision system. In April 2026, Tuurny issued a press release announcing commercial deployment of the Nantul system to address the U.S. RAM shortage and reduce export of rare earth-rich e-waste.
The company is also listed on Crunchbase and PitchBook (Tuurny Robotics). The CEO's contact is [email protected].
Primary type & automation activities this supplier delivers:
Contact Tuurny
WEBSITE
https://www.tuurny.com/HEADQUARTERS
United States
Company Facts
Founded
2025
Primary Role
Manufacturer
Company Size
-
Primary Region
North America
Annual Sales
-
Funding Stage
-
Funding Total
-
Robots Could Turn E-Waste Into a Source of Legacy Chips
In response to increasing regulatory pressures regarding electronic waste, Tuurny, a San Francisco-based startup, is developing an innovative automated system aimed at enhancing e-waste recycling. With global e-waste projected to reach 82 million tonnes annually by 2030, current recycling methods capture less than one-third of the recoverable metal value from discarded electronics. Tuurny’s robotic system, named Nantul, is designed to identify and extract reusable components, particularly RAM integrated circuits, from circuit boards before they are shredded. The company plans to deploy dozens of these machines in early 2027 through a partnership with Areera, a UK-based television recycler that processes 1,500 tonnes of televisions monthly. Tuurny’s approach contrasts with traditional recycling methods, which often destroy valuable components by mixing them into bulk streams. Instead, Nantul employs advanced robotics and computer vision to carefully remove and sort components, aiming to create a new supply chain from recycled materials. Sina Ghashghaei, Tuurny’s founder, emphasizes the importance of recovering components from legacy systems, where sourcing replacements can be challenging. The technology, which combines suction, controlled heat, and robotic controls, is designed to minimize damage during extraction. While experts acknowledge the technical feasibility of Tuurny’s approach, challenges remain in ensuring the robots can adapt to the variability of e-waste and operate cost-effectively. The success of this initiative could significantly impact the recycling industry and address supply chain concerns for critical components in various sectors.
E-waste Robotics Electronics-recycling Computer-vision
