Elemental Machines (“EM”) was founded in 2015 by Sridhar Iyengar, a serial entrepreneur with two previously successful ventures in AgaMatrix (Sanofi’s white label glucometer device) and Misfit Wearables (acquired by Fossil).
During the course of building a reliable consumer-based wearable, Sridhar solved a seemingly intractable issue within the traditional supply chain: reproducibility. Elemental was founded on the notion that their supply chain solution would be applicable in life sciences, particularly during drug discovery (R&D) and manufacturing.
EM’s sensor-based platform can be used to gather and assess critical equipment and environmental parameters that can and do affect critical processes, often making them irreproducible.
We envision a world in which money isn’t wasted trying to duplicate processes. This will be done by measuring everything.
We make sensors and software to optimize complex physical operations. Critical data is encoded everywhere in the physical world, but much of this data remains hidden from those who need it most. Unseen, unmeasured factors drive inefficiencies worth billions of dollars to the dominant industries in our economy, from yield loss in pharmaceutical production to machine down-time in advanced manufacturing to the epidemic of experimental irreproducibility in life sciences R&D.
At Elemental Machines, we believe that rich, well-annotated datasets from connected sensors can eliminate this waste. We are on a mission to create products that make it easy to gather and transform this data into actionable insights to transform these industries for the better.
When the team is not racing bicycles, rock climbing, figure skating, kayaking, backcountry skiing, brewing craft beer or throwing axes (at targets) it is focused intently on bringing value to our partners and customers by disrupting industries that are still recording and using information the same way it was done thousands of years ago. IoT continuous data collection, combined with artificial intelligence and machine learning algorithms, will revolutionize scientific discovery.
One of the fundamental requirements for doing any sort of data science or machine learning is to have high quality data that is reliable and easily accessible. Data such as a lab notebook locked in a filing cabinet is secure, but not accessible. Having data in a variety of locations is less than desirable as this creates data silos. The best solution is to have all your data in one place, such as in the Cloud.
The best way to get reliable, usable data is with IoT devices that are constantly collecting data in the background while you are doing higher-level tasks. Whether you want to collect data from your assets, such as laboratory freezers, refrigerators, ovens, incubators, liquid nitrogen tanks, pH sensors, and balances or understand the ambient temperature and humidity in the lab itself, Elements are the answer. Easy to install and battery-powered, they stream high-resolution data to the Elemental Insights dashboard for easy access to visualizations and a lab-wide view of equipment performance.