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Senseye

Read 11 Senseye reviews and testimonials from customers, explore 5 case studies and customer success stories, and watch customer videos to see why companies chose Senseye as their Maintenance Management Systems

Senseye™ is the leading cloud-based product for Predictive Maintenance 4.0. It is used by maintenance teams to half unplanned downtime and increase maintenance efficiency, saving money by using proprietary machine-learning algorithms to automatically forecast machine failure and remaining useful life, achieving a typical ROI of less than 3 months.

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Customer Rating Review Score based on 1310 reference ratings
4.8/5.0 (1310)

11Testimonials

  • "Successful Predictive Maintenance requires multiple disciplines working together to enact change. Senseye has for some time been a leader in the Machine Health Management area and the launch of Senseye …

    David Metcalfe Chief Executive Officer

  • “We are pleased to have partnered with Senseye on this corporate initiative to enable Predictive Maintenance and improve operational efficiencies. The results and ROI were executed in a prompt manner …

    Bruno Longchamps Global Aluminum Manufacturing Intelligence Manager

  • “Senseye is supporting our Predictive Maintenance programme across multiple production facilities and has helped us lower overall downtime and increase OEE.”

    Damian Wheeler Engineering Director
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5 Case Studies

  • Schneider Electric - Customer Case Study

    Cyril Perducat EVP IoT & Digital Offers

  • INSEE - Customer Case Study

  • Nissan - Customer Case Study

    Damian Wheeler Engineering Director
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Additional Senseye Information & Resources

Read Senseye Reviews, Testimonials & Customer References from 11 real Senseye customers.

Browse Senseye Case Studies, Customer Success Stories, & Customer References from 5 businesses that use Senseye.

FeaturedCustomers has 1,910,686+ validated customer references including reviews, case studies, success stories, customer stories, testimonials and customer videos that will help you make better software purchasing decisions.