Learn why we believe in ‘customer success’ and how our company culture helps you to succeed.
Our mission is to democratize advanced analytics
TrendMiner develops and delivers advanced analytics solutions for the process industry, helping companies with their digital transformation so they can optimize their production processes, increase plant productivity and improve the overall equipment effectiveness. Our goal is to deliver innovative software that accelerates operational performance by putting advanced analytics in the hands of all subject matter experts in operations and maintenance.
Our self-service industrial analytics platform is based on advanced search technology built with pattern recognition and machine learning. It connects easily with existing data sources and allows users to gain insights into their operational production data, monitor production performance and predict problems early on.
In June of 2018, we became part of the Software AG family. In the Internet of Things (IoT) market, Software AG enables enterprises to integrate, connect and manage IoT components as well as analyze data and predict future events based on Machine Learning (ML) and Artificial Intelligence (AI). See how you benefit >>
What makes us different
We democratize analytics by giving insights to the people who need answers: the engineers and operators in the plant.
Predicting process failures has historically always been done with model-based technology – and that’s where we are fundamentally different. TrendMiner analytics achieves this goal using pattern recognition and machine learning. This means that it can be implemented quickly without a training phase for the data. This approach is of great value to companies in the process industries, where people who can train mathematical models are often scarce – but people who can understand visual patterns are quite common.
With a self-service solution, our customers find new ways to further optimize production processes by harnessing the knowledge of their engineers and historical process data.