We start with a state machine that speaks OT protocols. Then push this directly into an API that is secured through a zero trust proxy architecture. This allows anyone to build an HMI (Human Machine Interface) or meld with analytics like jupyter playbooks and any other big data architecture. The platform combines high availability with big data systems. The secret sauce is that it is simple to configure and deploy. The platform enables engineers to think of a sensor or motor as an object instead of a set of digital registers. Once the infrastructure is in place, it allows you to export the data that you are already collecting into the big data management platform of your choice securely and easily.

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The Industrial Internet of Things (IIoT), sometimes called Industry 4.0, has a rapidly expanding foot print, but the current infrastructure does not connect easily to data management and analytics. Flexible, easier to deploy, automated infrastructure must support and extend those legacy systems while incorporating new features and functions. That makes it easy for businesses to test and iterate with newer capabilities, while maintaining robustness found in older systems. The intention is not to disrupt the market but to enable it to transform without pain and loss.
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The platform aims at building, testing, and releasing new infrastructure or manufacturing capabilities with less cost, greater speed, and higher frequency. The approach helps reduce the cost, time, and risk of delivering changes by allowing for more incremental updates to applications in production that usually causes an outage. Redundancy, security, and zero-touch automation will be used to deploy a tiered control and data acquisition platform that maintains data integrity and delivers real-time control of the plant. This will enable businesses to incorporate continuous integration capabilities into the energy, manufacturing, and transportation sectors.