Harwell, Oxfordshire, 11th July 2017. As part of a continued expansion into North America, Rezatec has now partnered with Matchpoint Inc. to bring innovative applications of satellite data analytics to the water industry in North America. Rezatec and Matchpoint aim to help water companies in the southern states including Georgia, North & South Carolina, Florida and Texas, reduce non-revenue water lost through pipeline leakage, particularly in more rural areas with less monitored pipelines.
Matchpoint are dedicated to helping companies save valuable resources by supporting the effective and efficient management of water infrastructure using the latest technology. They have the extensive water industry expertise, access to innovative SMART technologies and a 360-degree, holistic approach to generate meaningful data and actionable insight. Their focus is on providing visibility and transparency to managers of underground assets, predicting and preventing asset failure events in real time and reducing the risks associated with aging infrastructure.
Philip Briscoe, Chief Operating Officer at Rezatec stated, “We are very pleased to be working with Matchpoint in US. They are well-established and well-reputed in the water industry and are aligned carefully with our mission to enable the use of innovative technology, like ours, to reduce water loss through leakage detection and to improve the efficiency of water utility operations.”
Simon Wick, VP Water Asset Management at Matchpoint Inc. commented, “Water is invaluable, and it is our mission to save and make better use of it. Our clients rely on us for the knowledge to operate and maintain each critical asset within their system, including the resource itself. Rezatec allows us to provide a comprehensive solution with decision based on fact, not speculation.”
Rezatec and Matchpoint will now continue to promote the accurate, cost-efficient, highly valuable and scalable nature of satellite data analytics when used to detect existing leaks, monitor infrastructure assets and predict asset failures.
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