About the Authors

Ricardo Cid is a mechanical engineer specialized in mechatronics who has been working with embedded systems for more than 20 years. At school, he was already helping a team at the engineering institute (UNAM) connect a washing machine to the internet even before the term IoT was coined (to this day, he has not figured out why connecting a washing machine to the internet is necessary). After finishing school, he moved to NYC to design stage robots for artists in his free time while working as the head of engineering for a very successful company in the travel industry, where he pioneered creating systems in the early days of the cloud, acquiring extensive experience in big data and massive traffic volumes.

Around 2015, he took a six-month residency at the Museum of Arts and Design at Columbus Circle with a project that earned him multiple awards, consisting of creating 3D-printed mechanisms that danced to music. Because of his unique skill set, which included mechanical engineering, electronics, enterprise software, APIs, cloud, and big data, one of the biggest and most prestigious real estate portfolios in NYC offered him the unique opportunity to build a smart building operating system from scratch, using more than 15 skyscrapers as a sandbox.
During that amazing gig, Ricardo experimented with massive amounts of data and conceived a series of applications for a then-new type of technology called machine learning. Ricardo and his team conceived dozens of prototypes, some of them never saw the light, but they laid the foundation for a series of algorithms that eventually saved hundreds of megawatts in multiple buildings across the U.S., including those of the federal government and the largest bank in the world.

At the end of his sixth anniversary at that company, Ricardo realized there was a massive opportunity to bring much of that intelligence to edge devices, avoiding critical cybersecurity and reliability single points of failure. In 2023, Ricardo created a design studio exclusively dedicated to architecting and building edge solutions that run ML models in constrained environments.