Senseye boosts global potential with multi-language predictive maintenance

07 March 2019

Senseye, a provider of predictive maintenance software, today announced that it has more than doubled the size of its potential market by adding multiple language support to its award winning scalable predictive maintenance product.

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Senseye, which uses machine learning to automate condition monitoring and prognostics analysis, has translated its industrial operations software tool into five additional languages. Previously available in English only, Senseye can now be used on the shop-floor by maintenance and operations people that speak French, German, Japanese, Russian and Spanish to cater to existing customers, with additional languages being added.

Making Senseye available in multiple languages is a key part of the company’s expansion plans and one of several product enhancements to help large industrial companies reduce unplanned machine downtime and increase overall equipment effectiveness. Senseye has also revamped its dashboards to allow a deeper level of data analysis and can now provide additional contextual information to help users understand precisely what is happening to their assets.

Simon Kampa, Chief Executive Officer of Senseye, comments: “Our predictive maintenance product is now proven in a range of large-scale industrial environments and it has enormous potential worldwide. By enhancing our product and making Senseye available in a wider range of languages, we have more than doubled the size of our addressable market and made it much easier for organizations around the world to reduce unplanned machine downtime and increase overall equipment effectiveness.”                                                                                                                                                                                                                                                                                                                                                                                                                                     

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