In close collaboration with your field experts, we develop tailor-made algorithms based on state-of-the-art Machine Learning and Artificial Intelligence technologies. We create models which automate complex processes or predict the future based on your historical data. Some applications include quality control on a production line, predictive maintenance on engines and route optimisation.

Machine learning models for predicting or classifying tasks

What Are you interested in predicting future events ? Do you want to categorize elements in a systematic way? Do you simply think that a predictive model would facilitate certain repetitive tasks? Based on your historical data, we work with you to develop tailor-made machine learning algorithms. Our cyclical approach (data cleaning, research of relevant variables and exchanges with business experts) allows us to move forward side by side towards your goal.
  • Optimizing your decisions
  • Reducing work time on repetitive tasks
  • Our pragmatic approach based on the moto : Data determines the models.

Tools for automating processes or complex tasks

What Do you want to automate a complex repetitive task? Do you want to improve current decision making? We develop custom algorithms that perform time-consuming tasks for you. If the automation of production lines has relieved human beings of repetitive manual work, data analysis allows you to relieve yourself of repetitive intellectual tasks.
  • A relevant reorganisation of your resources
  • Optimization of repetitive intellectual tasks
  • Valorizing field expert knowledge
  • A rigorous, methodical and intuitive analytical approach

AI-based anomaly detection algorithms

What Are your monitored systems encountering any anomalies? Are your installations slowly degrading without human awareness? Is quality control of your production based on specific criteria? By understanding how your systems work, we create models estimating their normal behaviour. We use this basis to detect when exceptional or abnormal events occur. Quality control and predictive maintenance are only a couple examples of the use of anomaly detection.
  • Optimization of costs due to unplanned breakdowns
  • Improvement of quality control systems
  • The ability to translate business knowledge into technical knowledge
  • Strong knowledge of fundamental statistics

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