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Senseye Releases PROGNOSYS, Bringing Machine Uptime as a Service to Manufacturers, Zero-Cost Trial

Senseye announces the release of their ground-breaking PROGNOSYS software as a service solution. PRONOSYS helps manufacturers maximize Overall Equipment Effectiveness (OEE) and save expenses for manufacturing businesses by reducing machine downtime. For a limited time only, manufacturers can trial PROGNOSYS for zero cost.

Using the principles of condition monitoring enabled by advanced machine learning and the Internet of Things, PROGNOSYS predicts costly failures in machinery months in advance, helping businesses to save money by avoiding downtime.

To help manufacturers prove the value, Senseye has introduced a limited-time on-boarding program to help manufacturers learn about the value of PROGNOSYS for zero cost. This program provides a no-cost installation and use of the cloud-solution for a period of three months.

PROGNOSYS works by taking in measurements like vibration, humidity, acoustic emissions and power usage and uses technology like artificial intelligence and machine learning to predict when and how machinery is likely to fail. It’s designed to be affordable for all manufacturers and used with any type of machinery. It’s also entirely cloud-based so there’s nothing to maintain.

Simon Kampa, CEO of Senseye says “We’ve been working hard on developing this complex technology from its origins in Aerospace and Defence to be easy to use and accessible to the manufacturing industry. We’re thrilled to be running this pilot to show off PROGNOSYS and demonstrate the power of the Industrial Internet of Things”.

Senseye is accepting applications to join the web-based pilot programme at no cost, on a first-come-first served basis. If you’d like to stop downtime getting you down, then sign up at

About Senseye Ltd

Senseye develops PROGNOSYS, an infrastructure free software solution that delivers a new approach to prognostics and condition monitoring to predict failures in machinery months in advance. Senseye brings over 40 years of real world industry know-how, combined with deep expertise in machine learning and data science to designing a fresh and new approach to prognostics and condition monitoring to improve Overall Equipment Effectiveness.

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