Thanks to data science, we help you generate a business model change that takes advantage of the power of Machine Learning solutions. A consultancy that combines specialist expertise with the technical capabilities of IT and data analysts that characterize us. In this way, business and IT are aligned towards a direction of shared innovation.
Product as a Service
Monetizing the data of a company also means reaching more profitable and less risky business models, in a win-win logic. In this perspective, the product is realized as if it were a service (PAAS - Product as a Service). It is a winning paradigm change for industrial companies, which, thanks to Machine Learning algorithms, can offer their customers products by maximizing revenues and minimizing costs, for example those of invested capital.
We optimize the Research and Development process to automate, speed up and integrate it with the productive ecosystem. Thanks to Machine Learning technology, we develop algorithms that learn from previous iterations of trials, tests and discarded prototypes. In this way, custom software development will be more streamlined and customized to your needs, making more effective and functional research and development in the entire production ecosystem.
Adaptive Quality Control
Through automation processes with Machine Learning algorithms, adaptive quality control is built through the use of performance data and laboratory controls. It’s an advanced quality control, meaning that it not only reduces the costs of defectiveness and assembly, but manages the entire production chain in order to improve and satisfy customers.
We support our customers in the implementation of an adaptive management of maintenance work on industrial equipment and products using Machine Learning technology. The whole business benefits from this, as evidenced by an increase in profit and an enhancement of the economic impact. The result is smart maintenance both at a technical and at a pricing level, adapting the interventions over time according to selected parameters.