Digital Transformation

Digital Transformation

Our courage to pursue the strategy enables us to transform

There are billions of data elements that must be understood and managed in order to improve and automate difficult human workloads. These work loads and the possibilities of error will help and encourage future changes. To unify and manage massive data is the key to the power of the next generation of e-commerce, tracking, fraud detection, supply chain and logistics applications.

Big Data

There is a complex architecture within the pharmaceutical, food and beverage sectors. There are also different levels for the automated interface between corporation and control systems: devices, machinery, production lines, government authorities, different suppliers (brand owners, CMO, sub suppliers, re-packagers), warehouse, wholesale distributors and logistics.



The primary need for these industries is a software ecosystem designed to manage the massive serialization data storage and the information flow to ensure:


  • Maximum data safety
  • Flexibility in different communication requirements
  • Minimum downtime for software updates and patch releases
  • A unique point of connection to enable interface with different players, software and hardware solutions

Above all, “Deep Learning” of “Machine Learning”, is inspired by the neural networks and can uncover hidden data layers to identify complex patterns. Artificial Neural Networks (ANN) are models influenced by biological neural networks. For example: the central nervous systems of living creatures and most distinctly the brain. Therefore, it’s very useful to understand the data, make predictions, suggest recommended actions; for example: for the interpretation of unstructured data or many other clever behaviors without explicit human instructions.

The combined effect of automatic learning from data, images, examples, texts, etc., together with the sophisticated learning models and the high computing capacity, has given great opportunities to progress.

Machine and Deep Learning

Machine and Deep Learning

Above all, “Deep Learning” of “Machine Learning”, is inspired by the neural networks and can uncover hidden data layers to identify complex patterns. Artificial Neural Networks (ANN) are models influenced by biological neural networks. For example: the central nervous systems of living creatures and most distinctly the brain. Therefore, it’s very useful to understand the data, make predictions, suggest recommended actions; for example: for the interpretation of unstructured data or many other clever behaviors without explicit human instructions.

The combined effect of automatic learning from data, images, examples, texts, etc., together with the sophisticated learning models and the high computing capacity, has given great opportunities to progress.

Neural Networks

Specifically, in computer vision, deep learning is implemented mainly through the so called “Convolutional Neural Networks” (CNNs). CNNs learn more and more abstract representations of the input with each step (convolution). In the case of object recognition, a CNN might start with raw pixel data, then learn highly discriminative features such as edges, followed by basic shapes, complex shapes, patterns and textures.



Deep learning has
many benefits:


  • Reduced time to market             
  • Minimum mechanical complexity, which implies decreased production and maintenance costs
  • Increased ability to automate productions where human intervention is still necessary               
  • Faster machine reconfiguration for new products

Cloud Computing

Cloud and analytics services connections are growing. The main trend is the Hybrid Cloud, that has the possibility of connecting a private environment with one or more public cloud systems. This is to ensure greater flexibility, cost optimization, and above all adequate management of legal requirements in terms of privacy and confidentiality of data. There is also a growing interest in Edge Computing, an architecture with distributed resources that supports the centralized Cloud resources, bringing specific processing and analysis closer to the place where the information is really gathered (for example from the sensors). In this way, it is possible to increase the efficiency of the collection and analysis activities avoiding moving large amounts of data between the periphery and the on-premises or Cloud systems.

Our solution

From object detection, to image classification, to OCR, Optical Character Reading: there are many applications of the Artificial Intelligence. Our Industrial Computer Vision platform is based on Deep Learning technology with the target to overcome the classical approaches, for example by using «unsupervised training» to reduce data classification costs and training times, and to support the training phase also through the immense computing power available today on the Cloud . The system is designed not only for computer vision, but also for the analysis and the correlation between structured data, ideal for predictive maintenance, deviations, anomalies and time series, etc.