Combining Software Engineering, AI, physics-based Modelling and Domain Expertise so our Customers
can make high-stakes Decisions with Confidence.
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 decision intelligence software that utilizes tailored blends of machine learning, generative AI and physics-based 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.
Decision Intelligence Solutions than support decision making with confidence. These solutions 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 machine learning and physics-based modelling skills 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 Chez Albert 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.
Prophesea's configurable Energy Management System Fore-Sight reduces the energy cost for the Transfo site in Zwevegem. The EMS automatically manages an EV charging station, a heat pump, a large battery, a combined heat and power (CHP) unit, and solar panels optimally.
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.
Project manager, DEME Group
We were really satisfied with the collaboration with PropheSea. The entire project went smooth, from project initiation to-deployment.
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 Develop 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.
Owner, The Waffle CC - Chez Albert
We enjoyed working with PropheSea, especially their proactive mindset. They brought valuable insights that translated into tangible improvements in our day-to-day operations.
OT team lead, BTMA
Start2ML was a very insightful and interactive training tuned to the needs of our company. One of our engineers already applied some of the algorithms a week after the training was finished. I'm looking forward to further cooperation with Prophesea.
Project Manager, Kortrijk Business Park - Vesta
For us, the project with Prophesea was the first in which we actively controlled solar panels and EV charging stations in a smart way. Prophesea was particularly proactive and helpful in providing solutions. I am very satisfied with what we have achieved together.
Co-founder and CEO - Qweriu
PropheSea helped us strengthen the intelligence behind our air quality monitoring. Together, we developed an anomaly detection system that can identify unusual measurements in real time and help us distinguish genuine air quality events from sensor-related deviations..
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 decision intelligence software.
By complementing data-driven and generative 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.
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Climate change is driving more frequent and intense heatwaves across Europe, pushing surface water temperatures to levels that increasingly disrupt industries relying on freshwater for cooling. Power plants, data centres and chemical facilities face growing risks: reduced efficiency, curtailments and regulatory constraints on thermal discharge. PropheSea, together with RMI and Antea Group will develop a new machine learning demonstrator to face this challenge.
Within the Destination Earth initiative, the Pilot Service Dynamic Line Rating explores how high-resolution forecasts can support smarter grid operation and planning. The first results demonstrate the strong potential of dynamic line rating.
Thanks to advanced satellite data, companies can respond faster and smarter to fluctuations in energy production and energy prices.
Traditional machine learning is powerful, but a lot of tasks were still a nightmare to automate. Things like competitor analysis that requires continuous monitoring, document workflows that demand contextual understanding, customer interactions that need intelligent decision-making, and many more. Agentic AI addresses these challenges directly.
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
Dynamic Line Rating (DLR) provides a smarter alternative. By adjusting line capacity based on actual weather conditions, DLR allows operators to safely push more power through existing lines.
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
