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Our Vision

Improving cost efficiency, security & sustainability across all Industries

We dream of a world where products are sustainable, always running at their very best. A world where we no longer waste money or parts with early maintenance. 

Image by Steven Kamenar

How our dream becomes reality

The solution is that you know how every single product you have out there is doing.

Image by Noah Buscher

Helping you hit those sustainability goals

With governments world wide making lofy goals, our customers are faced with goals of their own. Reducing the amount of wear parts being replaced is only possible if you know exactly how your products are doing. With Ekkono you can not only know how your products are doing but you can predict when maintenance is actually needed.

Image by Eric Prouzet

Reducing your costs through predictive maintenance

We frequently hear companies say they shut down 2 or 3 times a year to replace wear parts that might break. With Ekkono, you can predict when you'll need to shut down saving you time and resources. 

Image by Gabriella Clare Marino

Knowing that size really does matter...

We know your product packs a punch and we know there isn't always a lot of room left for more which is why our solution fits onto the smallest of devices.

Our Story

Ekkono is a Swedish software company founded in 2016. It is based on the research by Rikard König, a PhD and Senior Lecturer in Machine Learning. He presented his work to serial entrepreneur Jon Lindén, who immediately fell in love with the idea of making IoT devices more intelligent. Together with Anders Alneng and Joakim Andersson, both previous co-workers to Jon, they founded Ekkono. Today, the company consists of a diverse team of experienced entrepreneurs, Machine Learning Engineers, Data Scientists and Embedded Developers. Together, we’re striving to make the earth a better place with technology.

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Ekkono is born out of seven years of machine learning research. More precisely in the area of high performance computing and predictive analytics. The result is a resource efficient small-footprint solution which can run most applicable machine learning algorithms on small platforms. We deliver an edge computing platform for connected things packaged in a Software Development Kit.

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