Long gone are the days when Artificial Intelligence (AI) systems required warehouse-sized hardware and dedicated teams of computer scientists. AI software and automation solutions are primarily deployed in IIoT digital transformation to quickly ‘learn’ how a machine works and present systemic optimization opportunities based on a production line’s, equipment’s, or machine’s processes and ‘inner states’.

AI is further used to reduce data collection to comprehensible and effective levels. Automated data pre-processing features are also fully configurable in real-time and can be tuned to break down and visualize disparate engineering pipelines. The goal of AI deployment is to automate systems as much as possible to reduce or remove the human element at the device edge.

Relayr’s AI is powered by cutting-edge machine learning algorithms which can be fully configured to suit each customer’s specific needs. Cutting down on engineering and specialist costs, flexible software technologies mean that our AI systems can ‘learn’ any production line or device to create aggregate per-line or fine-grained per-device results.

There are two main areas in which AI and automated systems are used. Automatic calibration enables unsupervised employment of anomaly detection algorithms to help find bottlenecks, pain points and areas which can be optimized. AI is also used to automatically train dataset creation and calibration for the deployment of predictive system algorithms, thus enabling predictive maintenance and pre-event problem solving. The dashboards at the frontend have label collection infrastructure in place to create event documentation according to predetermined or desired rules or third-party integrations.

But what does all of this mean to you and your digital transformation journey? There are three main functional areas and processes from across the industrial spectrum where relayr recommends and deploys AI and automated analytic systems.



AI solutions can be used to enhance manufactured equipment with out-of-the box intelligence that enables predictive maintenance for equipment and machine end-consumers. Software can provide manufacturers with information on product misuse, faulty operation parameters, or even faulty deployment of equipment on the customer side.

Data is then assessed and bundled to provide easily understandable overviews giving you control over device or equipment lifecycles. The addition of predictive maintenance and servicing features is a key USP for both manufacturers and customers. The benefits for customers are obvious; on the manufacturing side, however, these features allow manufacturers to offer their customers uptime guarantees, performance guarantees and advanced targeted maintenance warnings. AI and analytics data also provide an opportunity for manufacturers, who can use AI to aid R&D for future products and to provide performance benchmarking and test benches for the current products.


The self-learning anomaly detection features of production line AI systems can be adapted to a per-device and/or per-production line basis in order to signal anomalous patterns or to alert to malfunctions in real-time. Root cause analysis on a per-device or per-production line basis is the key to discovering the primary manner in which a production line’s machines operate. Anomaly detection software can also be easily tweaked to detect bottlenecks on production lines which, considering the drastic effects of even small undetected slowdowns, can save considerable resources and time.

As an alternative, self-calibrating predictive systems can flag the likelihood of future problems or failure in per-device and/or per-production line basis, which enables effective maintenance planning. Systematic analysis allows for the optimization of inputs for discrete or continuous production processes, thereby giving businesses the ability minimize or maximize desired outcomes. This is particularly helpful when attempting to optimize or harmonize power consumption or processing line throughput.


In service-oriented digital retrofitting projects, predictive maintenance capabilities based on AI diagnostics data are a central objective. Smart, machine-learning systems present root cause analytics to allow for the optimal allocation of resources and expertise. Predictive systems minimize the amount of corrective and regular maintenance and fuel the migration from preventive or, even worse, reactive to business positive maintenance. Knowing what will happen in the future could also open-up opportunities to trade in spare parts or to readjust the supply chain to suit acute rather than projected needs.

Moving away from reactive managing, AI predictive and root cause analysis puts the power over resources and manpower back into managers’ and operating engineers’ hands. Empowerment over future risk ties the business processes into the AI systems and the AI systems into an integral part of future business development.