By leveraging cutting-edge technologies, businesses can analyse and interpret large datasets, uncover hidden patterns, trends, and correlations, and make well-informed decisions to drive innovation, efficiency, and competitive advantage.
Digital Sustainability refers to the application of data science and artificial intelligence techniques to derive meaningful and actionable insights from vast amounts of digital information. The DSAI team develops Machine Learning and Artificial Intelligence models based on either synthetic or customer data that can be integrated in customer production workflows.
Industrial Data Science
Combining open-source intelligence (OSINT) with industrial datasets, we apply multimodal AI and time-series analysis to make industrial processes and electronic commerce more efficient and more sustainable.
- Reduced downtime and financial losses by enhancing data center reliability and uptime.
- Lower energy consumption and operational costs through improved capacity planning and resource utilization.
- Enhanced sustainability and reduce environmental impact.
Green Data Science
The application of multi-modal sensor analysis (local images, remote sensors, time-series measurements) combined with large data sets (weather and climate data), geospatial analysis, and physics-informed AI, supports predictions of agricultural yield (related to climate conditions, crop diseases, and agricultural pests) and sustainable agricultural practices.
- Actionable insights into environmental conditions through physics-informed machine learning.
- Transparent, explainable, and trustworthy machine learning models.
- Address issues related to sustainability and the circular economy in various industries.
Cultural Data Science
Cultural Data Science deals with digitalisation of culture from the GLAM (Galleries, Libraries, Archives, and Museums) sector. Combined with our technical expertise in multimodal information retrieval and Extended Reality, these activities ensure the long-term preservation of and access to our cultural heritage.
- Support digitalization and information retrieval in national libraries, archives, galleries, museums, as well as academic humanities research.
- Adhere to FAIR (Findable, Accessible, Interoperable and Reusable) data standards.
- Tailor-made software solutions based on affordable open-source technologies.
