Our Platform

Establish a Wireless Sensor Network

Equipment & Process Data Collected & Analysed

Industrial Systems Integration

System Simulation & Modelling

Optimisation Predictions

A hybrid cloud-based, flexible, heterogeneous sensor data management platform for deploying industrial-scale IoT applications. The Brains Platform ingests different types of sensor data, applies data quality algorithms to create a data lake of calibrated sensor data and applies machine learning and physical models to deliver previously unknown insights and recommendations through decision support software.

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Brains WSN

The Brains WSN (Wireless Sensor Network) delivers hardware, wireless network and integration software to provide a single data view of sensor data scalable over long distance and short range wireless networks. Brains WSN integrates data from existing sensors, industrial automation and SCADA systems, unstructured data sources, such as maintenance logs, and new sensors to fill any data gaps. It applies data quality algorithms to calibrate sensor data and create a data lake that can be used for analysis. Brains WSN enables development of a complete asset profile that feeds our applications and Brains.VOS.



The Brains.app product is a real-time analytics and decision support application used by plant operators, systems engineers and operations management teams to gain operational insights and make decisions that drive productivity and efficiency improvements. We apply best practice equipment models to design the overall system model, compare performance against ideal operation and identify sources of variability. We deliver optimisation set points for the target technical and financial processes in order to reduce variability, improve per unit operating cost and increase throughput. Each model is tailored for the target process.



The Brains.VOS (Virtual Optimisation Simulator) product is a process simulation environment that is calibrated continuously with real-time data. Brains.VOS identifies circuit bottlenecks, carries out “what if” scenarios and trains operators on best ways to handle similar situations. All optimisation control recommendations can be verified within the offline environment, removing production risk while allowing for simulation. A variety of advanced statistical methods are used to identify patterns in process bottlenecks and deliver predictions on future system performance under various load conditions.