At proteanTecs, we’re on a mission to enable the electronics industry to continue scaling. proteanTecs is a dynamic fast-paced start-up company, transforming the way reliability of electronics is achieved. In a world where machines are gaining immense responsibility over our lives, sudden failure is not an option. proteanTecs has developed a cloud-based platform, which combines data created in chip-embedded Agents, with machine learning, to predict faults before they become failures. Our solutions provide unprecedented insights throughout the value chain, from Chip Vendors to System Vendors and Digital Service Providers. The company was founded by seasoned industry veterans, including three former founders of Mellanox Technologies, with deep knowledge and experience in the electronics industry and is backed by worldwide leading investors. At proteanTecs we combine different disciplines in creating a revolutionary solution. Our team is highly interdisciplinary, ranging from SaaS and Machine Learning experts, to top designers in Chips and Circuits, as well as masters of EDAproteanTecs develops revolutionary Universal Chip Telemetry™ for electronic systems throughout their entire lifecycle, increasing their performance and reliability. By applying machine learning to novel data created by embedded Agents, proteanTecs provides meaningful insights unattainable until today, leading to new levels of quality, reliability and scale. Founded in 2017 and led by seasoned industry veterans, the company is headquartered in Israel with offices in New Jersey and San Francisco.
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Universal Chip Telemetry (UCT) is a new capability based on embedded Agents, custom tailored to represent and automatically cover a specific design. They monitor, detect and report from within chips. The UCT level is determined by the coverage and variety of Agents integrated. UCT, combined with machine learning data analytics, provides unprecedented visibility into electronics, creating a shared language of measurements across the value chain. During chip and system production and use, machine learning is applied to data created by the Agents, and meaningful insights are provided by the software analytics platform.
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