Ekkono Solutions: Heralding the Future of Smarter and Autonomous Connectivity
Jon Lindén, CEO
Almost a decade back, a meticulous research program on predictive modeling was underway at the University of Borås, Sweden. The culmination of the seven-year-long project was a unique, extremely lightweight, advanced analytics software. It was then that RikardKönig decided to bring the results of his research program into the mainstream business market, as he laid the foundations for Ekkono in 2016 together with four esteemed entrepreneurs.
An organization that was founded on a mission to make connected things smarter and add cognitive capabilities to the world of connected things, Ekkono provides a horizontal solution that can be applied to any industry use case, from consumer to industrial IoT. Jon Lindén, co-founder and the CEO of Ekkono says, “With billions of connected devices and the number growing every passing second, the resources and time spent on manually supervising them ruins the business prospect of IoT. We make IoT devices smarter and self-learning to become more intuitive and self-sufficient.” Leveraging its finesse by implementing machine learning on the connected devices, enabling tailored, individualized learning, is what sets Ekkono in a league of its own.
We take IoT from connected to smart
Often, it is practically challenging to transfer and process the mounting volumes of data generated by various devices, and as a result, most organizations send only hourly averages of the data to the cloud for processing. Privacy is also a concern as it becomes a common question whether a vendor should and should not be allowed to download and process the raw sensor data. Ekkono comes to the rescue with an embedded software that runs at the edge, on the device, and that is robust, configurable, and designed for programmers. In industries where most companies struggle to find and recruit data scientist, Ekkono’s solution strikes as an easy bet, as programmers can use its capabilities to roll out new solutions to differentiate or evolved their products. Described as a mathematical toolbox, the software manages the input data along with model training and provides execution and data output. The organization has interacted with numerous customers, and in the process, learned their needs and requirements before packaging an optimized solution.
A machine manufacturer approached Ekkono as they wanted to deploy predictive maintenance capabilities where similar machines in different environments had a service cycle of somewhere between six weeks and five years... Applying machine learning to the data of a few sensors, like temperature and pressure, on the machine control system, improved service levels through scheduled rather than unplanned stops.
Sensor-based IoT services along with machine learning are on the verge of radically changing the core foundation of our communities and the manner in which people live. To that end, Ekkono has laid a special focus on the processing industry and automotive. They find the space very compelling, as the monetary benefits from predictive maintenance, automation and production optimization are huge. The team is also working along with system integrators who build the customer solutions that embed Ekkono’s software. “We are a product company, and the way we go to the market is through our partners, and we believe in the ecosystem approach,” concludes Lindén.