proteanTecs, a global leader in deep data solutions for electronics health and performance monitoring, has unveiled a first-of-its-kind system production analytics solution that brings embedded chip telemetry and machine learning together to tackle rising complexity in high-performance markets like AI, cloud, telecommunications, and automotive.
Designed to bridge the longstanding gap between silicon behavior and system performance, the new solution provides unprecedented parametric visibility during functional tests — revealing how chips operate in real-world conditions, not just in isolation. This enables system vendors to detect hidden issues such as power integrity failures, thermal inconsistencies, and assembly faults, while optimizing performance and energy efficiency at scale.
“Our embedded HW monitoring system serves as a sophisticated monitor for the system, capturing critical telemetry. Together with a dedicated software stack, system quality, power consumption, and performance are significantly improved,” said Evelyn Landman, co-founder and CTO at proteanTecs. “We deliver the first-ever deep parametric visibility during PCB, Module, and System functional testing — under actual workloads and configurations.”
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At its core, the platform combines hardware-embedded on-chip Agents with a cloud-based analytics engine and edge-deployed machine learning models. This architecture allows for inline test decisions across the production lifecycle — from new product introduction to high-volume manufacturing — while delivering real-time insights and actionable feedback to design and production teams.
“Today’s rapid adoption of complex AI and hyperscale computing is stretching traditional production methods beyond their limits,” added Uzi Baruch, Chief Strategy Officer. “We’re transforming test from a static, reactive process to a predictive, data-driven workflow that adapts to each device’s actual behavior.”
Already deployed by major system vendors, the solution has shown measurable ROI by simplifying debug, accelerating bring-up, and boosting production quality and yield — all while providing ongoing population-level analytics for improved reliability.