Internet of Things (IoT) innovators, IoT.nxt is developing a model library of off-the-shelf Artificial Intelligence (AI) models with targeted outcomes to drive value for businesses in diverse industries.

“Our goal with this approach is to generate value, driving insights and to continuously develop tools to make using AI a frictionless experience. Our aim is to remove the complexity and challenges, including AI and machine learning (ML) skills, understanding the technology, benefits and uses, and access to good quality and quantities of data,” says Ricardo Ludeke, Data & Analytics Manager at IoT.nxt.

“When AI is used as part of an IoT solution it allows for devices to analyse data and make decisions and act on the data without human involvement. The industries that currently utilise AI in one form or another include healthcare, agriculture, finance, manufacturing, automobile, surveillance and robotics,” Ludeke says.

Projects already rolled out by IoT.nxt include:

  • Predictive Maintenance (Equipment failure prediction is one part of this. Other tasks can also be augmented with AI, such as root cause analysis, remedy prescription and maintenance scheduling).
  • Pattern Recognition – Anomaly or outlier detection.
  • Image Analytics – Object detection, recognition, and counting.
  • Quality inspection.
  • Supply chain optimisation.
  • Manufacturing process optimisation.

Although there is widespread adoption of AI solutions across a broad range of industries, it often seems daunting to any businesses that need it and they often do not know where to start. “Our aim is to remove the complexity and challenges, using AI and ML skills, understanding the technology, benefits and uses, and access to good quality and quantities of data.

“AI, ML, and ultimately data analytics allows companies to reduce costs by finding more efficient ways of doing business, better customer service and improved decision making,” he adds.

IoT.nxt develops bespoke AI solutions ranging from unsupervised pattern recognition models to detect the operating behaviour of devices and highlight anomalies to forecasting models that can predict energy usage and carbon emissions. Its most advanced prescriptive analytics models can be used to prescribe the best actions to take to achieve the desired outcome and actuate control back to its Raptor edge gateway. Depending on the use-case or problem at hand, the IoT.nxt teams develop the right model to solve the problem.

“Highly skilled resources are required to develop these bespoke solutions, as well as a thorough understanding of the problem and the technology, and good quality data. IoT.nxt’s Commander platform sets itself apart by making the right data available. To accelerate adoption, we have devised the idea to develop a model library. The model library will enable users to easily configure and deploy an AI solution to solve their problems without being an AI expert. This allows businesses to focus on the implementation and value generation instead of acquiring skilled resources or understanding complicated technology to develop an AI solution from scratch,” Ludeke says.

Examples and benefits of the AI models that will be available in the model library include:

  • General anomaly and outlier detection models will enable users to detect anomalies in the operation of their equipment or to detect outliers in a fleet. This allows users to effectively manage and maintain a fleet of equipment by identifying potential issues in operation before complete failure and take corrective actions to return the equipment to normal operation.
  • Time-series forecasting, and classification models will enable users to predict the future trend or categorise their equipment’s specific behaviour at a point in time. Knowing the future trend could be helpful in planning and decision making, while categorising how different equipment is operating could be valuable in identifying areas of concern to prioritise and focus our efforts.
  • Our video intelligence models will enable a range of features, including object detection, counting, and tracking, which can monitor several video feeds for potential security issues.
  • Other planned AI models include a predictive maintenance solution, an energy management solution, and a production yield optimisation solution. These are more comprehensive end-to-end solutions that will enable more complex workflows.