Your partner in building impactful data-driven solutions. We combine technical depth with a clear focus on practical outcomes.
End-to-end data science and analytics solutions. We help you extract insights, build predictive models, and turn raw data into actionable decisions.
Custom algorithm design and implementation tailored to your business needs.
We validate your ideas quickly, scope the opportunities, and create a clear roadmap for a scalable ML adoption without wasting time on unnecessary experimentation.
Support for research teams, universities, and innovation-focused individuals with rigorous statistical analysis, experiment design, model selection, and technical guidance for data-driven projects.
This project aimed to develop a machine learning framework that uses pressure sensor data to accurately pinpoint the location, size, and start time of leaks in Water Distribution Networks. The solution reduces the physical search space for leak nodes by 92%, empowering utility operators to minimize water loss and financial damage through rapid response times.
Water distribution network leak localization (journal article)
An ML-driven forecasting pipeline that ingests tabular sales and operational data to deliver predictions from one month to one year ahead. Used to gain clear demand visibility and optimize inventory management and promotions.
Solution that uses optimization methods to find the best placement of pressure sensors in a water distribution system to improve water quality and reduce water loss. Sensor configurations are ranked by their leak localisation rate, the fraction of scenarios where the optimizer pinpoints the true leak within a defined radius. The results are aggregated into interactive visualisations that let engineers compare sensor layouts at a glance.
We supported PhD researchers with data interpretation, method selection, and a practical path to their thesis goals. We corrected faulty neural-network implementations and delivered focused training on the mathematics behind advanced deep learning, including specialized topics where standard courses fall short.