A working model to connect and reduce downtime of global production machinery

2022-10-18

Thursday, October 27, 2022 | 16:00 CEST

Learn how to create a secure and scalable solution for machine predictive maintenance to reduce downtime and development costs for industry.

Everyone needs meaningful information from their machines to prevent downtime and optimize their production machinery. The problem however remains: how to connect new and legacy machines in order to gather meaningful telemetric data? What setup allows companies to address security and data sovereignty concerns while connecting machines and robots for predictive maintenance and production optimization?

In this webinar, Sahitya Parvathaneni, CPO & Co-founder of Utpatti, and Andrew Heimerman, the Lead Solutions Engineer of Staex, will present a model for not only connecting and controlling machines, but also a way to have safe and scalable predictive maintenance. The Staex platform provides fully decentralized networking and software orchestration, so that modern applications and services can be used with machinery in factory settings. Utpatti provides decentralized AI software to reduce machine breakdowns and reduce scrap. Together, Staex and Utpatti create a solution that connects and optimizes production machinery while still ensuring that sensitive manufacturing data never leaves the factory premises.

Watch the webinar below!

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Staex is a secure public network for IoT devices that can not run a VPN such as smart meters, IP cameras, and EV chargers. Staex encrypts legacy protocols, reduces mobile data usage, and simplifies building networks with complex topologies through its unique multi-hop architecture. Staex is fully zero-trust meaning that no traffic is allowed unless specified by the device owner which makes it more secure than even some private networks. With this, Staex creates an additional separation layer to provide more security for IoT devices on the Internet, also protecting other Internet services from DDoS attacks that are usually executed on millions of IoT machines.

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