Senseye raises £3.5 million in Series A funding round

11 December 2017

Southampton, UK, 11 December 2017 – Senseye Limited, the leader in predictive maintenance software, today announced it has raised £3.5 million at the close of a Series A funding round led by MMC Ventures, a venture capital fund investing in early stage, high growth companies. The round was also supported by existing investors Breed Reply, IQ Capital and Momenta Partners. 

Named NMI Emerging Tech Company of the Year 2016, and highly commended for ‘Tech Business of the Year’ in the 2016 startups.co.uk awards, the company will use the capital to meet fast-growing customer demand for its Senseye automated condition monitoring diagnostics and prognostics product - the leading-edge software solution that enables industrial companies to easily predict the failure of machines months in advance. 

Senseye’s cloud-based solution helps manufacturers reduce maintenance costs by automatically identifying machine failure through machine learning algorithms fed with data from the Industrial IoT.  Users can benefit from up to a 40% reduction in maintenance costs, as well as lowering unplanned downtime by up to 50%. The product requires no additional hardware or customisation, which means it can be installed quickly, realising the benefits almost immediately. 

At the core of Senseye are the product’s unique advanced prognostics algorithms, which enable manufacturers to monitor and understand the remaining useful life of thousands of machines based at multiple sites – without the need for on-site expert technical input. 

Already trusted by a growing number of Fortune 500 industrial and manufacturing companies, including leading global automotive and automation brands, the funding will enable Senseye to meet the diverse needs of existing and new customers by expanding the company’s research and development teams. This will ensure the product remains at the forefront of predictive maintenance technology globally. 

“We’re delighted to have secured the right capital and strategic partners to help us to accelerate our success,” said Senseye CEO Dr Simon Kampa, who co-founded the company in 2014. 

“We’re confidently on the way to making unplanned machine downtime a thing of the past and leading an emerging $5 billion market.” 

Simon Menashy, Partner at MMC Ventures, said: "We believe that predictive analytics is going to have a transformative impact on industrial companies over the next decade. Over the past year we have met and assessed more than 250 companies applying AI and machine learning technologies across every sector - Senseye's mix of cutting-edge data science and deep understanding of engineering puts them amongst the very best we have seen. I'm excited to work with them to create some of that transformation and build a big company in the process." 

Emanuele Angelidis, CEO, Breed Reply, said: “Manufacturing is one of the sectors we expect to benefit most from IoT technology. Companies like Senseye which combine data and machine learning have the potential to significantly reduce costs and improve efficiencies in manufacturing leading to major improvements in productivity.” 

“Senseye is leading the emerging field of machine prognostics and the prediction of remaining useful life. The combination of having created the most advanced algorithms dedicated to this field, together with a strong focus on ease of installation and usability, creates a compelling ROI and will drive adoption of this technology throughout the manufacturing sector,” said Ed Stacey, IQ Capital Managing Partner. 

“We see predictive analytics offerings like Senseye’s driving the next wave of value in the Connected Industry, especially in the manufacturing, energy, smart spaces and logistics sectors,” said Ken Forster, Managing Director of Momenta Partners. 

Senseye is rapidly implemented, and immediately usable, through a simple subscription service - allowing the company to rapidly acquire new customers. For more information, visit www.senseye.io.

 

Senseye, predictive maintenance leader

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