Combining Machine Learning, Mathematical Models, and Domain Expertise to
Turn Data into Valuable Software Solutions and create a Positive Impact on Life.
Combining Machine Learning, Mathematical Models, and Domain Expertise to Turn Data into Valuable Software Solutions and create a Positive Impact on Life.
We are a boutique digital solutions provider specialized in delivering tailor-made predictive software that utilizes both machine learning and mathematical models. Through the fusion of domain expertise and cutting-edge AI technology, we assist businesses in extracting maximum value from their data.
Struggling to unlock the full potential of your data? We help you setup the right scalable data solutions that allow you to swiftly connect the dots.
Digital twins that comprise a virtual representation of reality. These models allow you to anticipate the future and fully leverage your data and process knowledge.
We pave the way for you to develop in-house machine learning solutions. Acquire in-depth machine learning knowledge through our training and coaching program.
We turned a complex and time-consuming computational model into a fast data-driven artificial intelligence model. This allowed our client to evaluate more scenario's; ultimately leading to a significant cost reduction.
We helped the Waffle Company to forecast their daily revenue across their network of distribution centers. This enabled our client to fine-tune operational staff planning.
We created a predictive maintenance tool to avoid unplanned machine failure and optimize maintenance efforts.
In this project, we helped Jan De Nul make sense of their field measurements by alligning their various data sources into tools that allowed them to quickly and adequately analyze their offshore operations
The iNose boosts air quality management with real-time monitoring of odor and emissions. In this project, we improved the air quality monitory by applying anomaly detection processing to raw measurements.
We turned a complex and time-consuming computational model into a fast data-driven artificial intelligence model. This allowed our client to evaluate more scenario's; ultimately leading to a significant cost reduction.
We helped the Waffle Company to forecast their daily revenue across their network of distribution centers. This enabled our client to fine-tune operational staff planning.
We created a predictive maintenance tool to avoid unplanned machine failure and optimize maintenance efforts.
In this project, we helped Jan De Nul make sense of their field measurements by alligning their various data sources into tools that allowed them to quickly and adequately analyze their offshore operations
The iNose boosts air quality management with real-time monitoring of odor and emissions. In this project, we improved the air quality monitory by applying anomaly detection processing to raw measurements.
We turned a complex and time-consuming computational model into a fast data-driven artificial intelligence model. This allowed our client to evaluate more scenario's; ultimately leading to a significant cost reduction.
We helped the Waffle Company to forecast their daily revenue across their network of distribution centers. This enabled our client to fine-tune operational staff planning.
We created a predictive maintenance tool to avoid unplanned machine failure and optimize maintenance efforts.
In this project, we helped Jan De Nul make sense of their field measurements by alligning their various data sources into tools that allowed them to quickly and adequately analyze their offshore operations
The iNose boosts air quality management with real-time monitoring of odor and emissions. In this project, we improved the air quality monitory by applying anomaly detection processing to raw measurements.
Pieter Vaes
Project manager, Deme Group
We were really satisfied with the collaboration with PropheSea. The entire project went smooth, from project initiation to deployment.
Chris Desomer
Project Manager, .Ocean
During the entire project cycle, PropheSea promoted an agile attitude to come up with solutions even in non-ideal situations, leading to great results. Working with PropheSea means open, transparent communication and qualitative reporting.
Research and Development Engineer, Deme Group
Due to PropheSea’s proactive and flexible attitude, the surrogate model was delivered with a short throughput time while maintaining the highest quality level.
How the power of machine learning technology can be further enhanced? By taking into account domain expertise in every step of the process.
PropheSea was founded in 2020 by Tomas Van Oyen, driven by the belief that, in addition to machine learning, process knowledge (defined as mathematical models) continue to play a crucial role in to create added value with predictive software.
By complementing data-driven AI technology with process expert knowledge (defined in mathematical models), PropheSea aims to achieve revolutionary breakthroughs and efficiency gains while respecting specific expert knowledge and physical laws.
How the power of machine learning technology can be further enhanced? By taking into account domain expertise in every step of the process.
PropheSea was founded in 2020 by Tomas Van Oyen, driven by the belief that, in addition to machine learning, process knowledge (defined as mathematical models) continue to play a crucial role in to create added value with predictive software.
By complementing data-driven AI technology with process expert knowledge (defined in mathematical models), PropheSea aims to achieve revolutionary breakthroughs and efficiency gains while respecting specific expert knowledge and physical laws.
Predicting the weather is essential for our day-to-day activities. Conventional forecasts are based on physical (conservation) equations implemented using numerical models. Generative machine learning technology is leading to a paradigm shift in weather forecasting.
Physics Informed Neural Networks (PINNs) are an advanced methodology to improve predictive modelling by integrating prior knowledge into the solution.
How physics informed neural networks (PINNs) can improve the development of a digital twin of a combined heat and power generation system.
Predicting the weather is essential for our day-to-day activities. Conventional forecasts are based on physical (conservation) equations implemented using numerical models. Generative machine learning technology is leading to a paradigm shift in weather forecasting.
Physics Informed Neural Networks (PINNs) are an advanced methodology to improve predictive modelling by integrating prior knowledge into the solution.
How physics informed neural networks (PINNs) can improve the development of a digital twin of a combined heat and power generation system.
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