System Architecture

We implement complex software architectures
based upon the most ground-breaking technologies
available on the market.

Some of our clients in System Architectures

Machine Learning and Big Data

We have used BIG DATA infrastructures in a number of scenarios: from IoT to Machine Learning, from Social Networks to Data Analysis in order to:

  • store and process a huge amount of data gathered from sensors (over 100 thousand collections a day per device);
  • develop dashboards aimed at analysing and displaying data in real-time;
  • design predictive systems based on Machine Learning algorithms (Multi-class, Regression- or Binary-based) able to generate predictions in real-time;
  • manage multi-level relationships (friendship, kinship, interests on more than 4 milions users) so as to provide social stream, likes, comments, search results, in real-time.

System Integration

We've got expertise to integrate and let interact each other all IT systems available in any organization (ERP, CRM, legacy systems, …).

We have designed and implemented software architectures to integrate the IT systems of the main telecommunication companies in Italy, some big banking institutions, as well as in the ITS industry (Intelligent Transport Systems).

We hold a very high know-how and expertise about EAI and ESB platforms. We work with the main ESB and service buses available on the market (Tibco, Mule ESB, IBM, Oracle).

We exploited our know-how and expertise in Artificial Intelligence by developing a platform performing Deep Learning algorithms to train models and provide predictions

It is a platform designed to be used and managed by non-expert users.

It is built on application layers that are horizontally scalable and able to recognize patterns by analyzing huge amount of data.

It is not focused on nor targeted to only one specific domain: it is able to analyze and manage any kind of data, regardless of the domain the data belong to.

It can find out by itself the best model to be applied to data under analysis, while user doesn't need to make any choice or setting.

It embeds a model self-improving capability, for the sake of a feature that allows to add authomatically new data and to clear the data set from fake data.

Supported Algorithms:
Neural Networks, Random Forest, SVM and Logistic Regression, Probabilistic Classifier, Linear regression, Isotonic regression, Survival regression, Gradient boost regressor, LSTM Neural Networks, IsolationForest, LGBRegressor, LGBClassifier.