Artificial intelligence, machine learning, and decision support systems

With extensive applicability, especially in a business context, distributed Machine Learning (ML) techniques and scalable Artificial Intelligence (AI) solutions enable the increase in productivity and efficiency of services, designing the best and most sustained decisions and consequent growth and competitiveness of organizations.

The CCG/ZGDV Institute coordinates research projects with the applicability of AI in different domains: Industry 4.0, Telecommunications, Smart Cities. It has a team specialized in the development of ML models, intelligent decision support systems and optimization algorithms. CCG/ZGDV's work covers all stages of the AI ​​life cycle, from data collection and analysis, to implementation in production, in order to maximize the impact and efficiency of projects.




CCG/ZGDV supports organizations through the following activities:

  • Applying advanced statistical techniques to large data sets for predictive modeling and analytical projects
  • Development of automated techniques to facilitate the use of ML algorithms
  • Performing data preparation steps, including extracting target data from multiple databases, integrating multiple datasets and creating derived variables, applying business rules, and quality control checks
  • Application of Predictive Analytics techniques that enable the prediction of trends and possible future results, through techniques such as statistical modeling and computational learning
  • Application of Descriptive Analytics techniques, which provide general information such as statistical reports and graphs
  • Development of complex analytical models for use in business operations and/or functional groups (e.g., pricing, production lead time, order lead time, intelligent optimization…)
  • Capacity to work with large data sets and scalable machine learning techniques
  • Application of Prescriptive Analytics techniques that provide recommendations and solutions to achieve specific results, as well as process optimization, using techniques such as simulation and optimization algorithms
  • Application of Diagnostic Analytics techniques, which allow you to investigate and understand what happened in the past, identifying causes and trends, through cluster analysis and data mining tools



Identify new R&D opportunities

Analyze data and identify new research and development opportunities.



Check trends and gaps

Analyze data from scientific literature and identify trends and gaps in research, helping to identify areas where more study is needed.



Optimize experimentation

Optimize resource allocation and plan experimentation to maximize impact and minimize costs.


Identify patterns and trends

Analyze investigation data and identify patterns and trends in real time.



Develop simulation models

Simulate different scenarios and evaluate the impact of different strategies.



Optimize production processes

Improve efficiency and reduce costs.




Develop new products and services

Identify new business opportunities and develop new products and services.



Monitor and analyze data in real time

Enable organizations to take immediate action to improve performance.