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Solutions  >  technologies

 

The products offered by our company are based on a number of innovative solutions, the result of years of in-depth Research conducted by a solid interdisciplinary team of highly qualified professionals.

The main R+D activity is Science and Technology Innovation using Technology and Methodologies in relation to basic sciences and Artificial Intelligence, thus creating added value for our customers and for our staff.

Following the success of a number of these basic technology Modules with many commercial and industrial applications, we have placed them at the disposal of our customers' R&D teams so that they may become part of their own solutions. These Algorithms, converting the lines of segmentation, forecasting, learning etc. ... may be selected from a number of modes, including the required training and consultation for use in extremely different environments.

ADAN

ADAN is an Artificial Intelligence software product which allows unsupervised data structuring.

ADAN is an automatic learning tool which carries out conceptual data grouping.

It performs autonomous extraction of the underlying structure of a data base by similarity grouping of the various items in relation to relevant variables.

ADAN in depth

EVA

An Artificial Intelligence Tool which Captures Expert Decision Criteria and Builds Intelligent Assessment Systems.

EVA also attempts to reduce the time spent at a later stage on the routine process of classification of the situations or items assessed.

EVA in depth

Optimiser

Algorithms based on dynamic programming, Network Flow and genetic algorithms for optimisation of complex problems.

Optimiser in depth

Symbolic forecaster

Forecasting system for time series, based on analysis of contextual information concerning the series to be forecast.

Symbolic forecaster in depth

Demand Forecast

The demand forecast solutions are based on the association of two powerful techniques: symbolic forecast and neural networks.

This association enables the forecast results to be sensitive to nonlinear effects in the time series as well as to incorporate a-priori expert or user knowledge.
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