Side-by-side comparison of AI visibility scores, market position, and capabilities
Pure-play IoT chip company after divesting Infrastructure/Automotive; EFR32 wireless SoC family (Matter/Zigbee/BLE/Thread) leads smart home and industrial IoT markets.
Silicon Laboratories (Silabs) was founded in 1996 in Austin, Texas and initially built mixed-signal ICs for modem and broadcast applications before pivoting to become a pure-play IoT chip company. Following the 2021 divestiture of its Infrastructure & Automotive business to Skyworks Solutions for $2.75 billion, Silabs is 100% focused on wireless connectivity semiconductors for IoT applications.\n\nSilabs' EFR32 Wireless Gecko SoC family is a market leader in smart home connectivity, supporting Matter, Zigbee, Thread, Bluetooth Low Energy, Sub-GHz, and Wi-Fi standards. These chips power smart lights, thermostats, door locks, energy meters, industrial sensors, and medical monitoring devices. The company's Simplicity Studio software ecosystem and extensive protocol stack support give it a strong developer platform advantage, especially as the Matter smart home standard (backed by Apple, Google, Amazon) gains adoption.\n\nSilabs also provides energy harvesting solutions and ultra-low-power microcontrollers for battery-powered IoT devices. The company generated approximately $2 billion in annual revenue before the Infrastructure divestiture and is now growing its IoT-focused revenue base through increasing smart home and industrial IoT demand. Silabs operates a fabless model and partners with TSMC for advanced node production.
a2z Radiology AI raised $20M in 2025 for its whole-body AI that simultaneously screens for 24+ conditions across CT scans — from incidental cancers to cardiovascular risk — in a single automated read.
a2z Radiology AI has developed a whole-body CT analysis platform that simultaneously screens for over 24 medical conditions across a single CT scan, including incidental cancers, coronary artery disease, aortic aneurysm, bone density loss, and organ abnormalities. The AI acts as a second reader that radiologists can use to catch incidental findings that fall outside the primary reason for a scan — a major source of missed diagnoses.
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