Google said on Tuesday that it will release a range of flagship hardware for its Google Meet platform -- an effort meant to bolster Meet's appeal among distributed and remote teams.
Video conferencing and collaboration players in hardware and software are increasingly turning to hardware-as-a-service plans and bundled hardware and software offerings in response to increased demand for collaboration tools during the COVID-19 crisis.
Most providers are also assuming that the video conferencing battle will continue beyond the COVID-19 pandemic given that CFOs see remote work as a permanent fixture to some degree in the future.
Google has been steadily building on this assumption. Earlier this year Google re-engineered the Meet service and brought it directly into Gmail, and then made the video conferencing tool free for everyone. The company also released an integrated workspace in Gmail that brings together chat, email, and voice and video calling, along with document collaboration and task management tools.
Today's hardware announcement is a continuation of Google and G Suite's ongoing release of future-of-work offerings. According to Google, the Series One hardware uses the latest in AI to deliver enhanced audio and video clarity and also utilizes TrueVoice, Google's proprietary, multi-channel noise cancellation, and voice amplification technology.
Each Series One kit includes a regular or extra-large true 4K smart camera, a compute system and a smart audio bar, and either a 10.1-inch touchscreen controller or remote, depending on the size bundle. The audio bar uses 8 beam-forming microphones and the largest kit configuration can process up to 44 channels simultaneously. Currently, Lenovo is the only hardware channel partner noted for the Series One release.
"All Series One room kits take advantage of the same tech used in Google's data centers," Google said in a blog post. "The Coral M.2 accelerator modules with Google Edge TPUs allow for AI-powered audio and video processing that preserves privacy and allows Series One to take advantage of future machine learning innovations while maintaining high performance and reliability for AV workloads."