The Quantitative Science and Technology team explores and applies data-driven methodologies to understand complex societal, technological, and economic challenges. We specialize in science, technology, and innovation (STI) studies, leveraging AI-driven foresight, large-scale data analytics, and algorithmic evaluation frameworks to assess the evolving landscape of innovation.

Our work revolves around developing and validating novel methodologies, including AI agents for large-scale information extraction from unstructured data sources, Large Language Models (LLM)-based evaluation tools, and text-based indicators. By integrating structured and unstructured data sources, we translate complex data into actionable insights, helping organizations make informed decisions in an increasingly uncertain world.

While our team works across diverse domains—economics, corporate strategy, sustainability, and policy evaluation—we are united by our commitment to methodological rigor, innovation-driven decision-making, and interdisciplinary problem-solving. We collaborate closely with ministries, businesses, the European Commission, and research institutions to provide customized AI solutions, scalable analytics, and transparent evaluation frameworks for decision-making and policy design.

• We are particularly interested in understanding:
  How can AI-driven analytics and novel big data approaches enhance our understanding of scientific and technological evolution?
• How can we measure, validate, and enhance the reliability of AI and LLM-based methodologies in innovation studies?
• What are the emerging AI-driven evaluation and foresight tools that can help organizations navigate technological change?
• How do we design robust, transparent, and explainable frameworks for AI applications in policy and decision-making?
• What is the role of advanced analytics in understanding the data economy, corporate foresight, and the changing nature of work and skills?
• How can algorithmic solutions, data storytelling, and interactive visualizations improve the communication of complex research findings?
• How can we expand the capabilities of AI-driven research methodologies while ensuring scalability, efficiency, and ethical considerations?

Through continuous methodological advancement, proactive customer collaboration, and a commitment to trust and transparency, we aim to pioneer the next generation of innovation analytics and contribute to AI’s responsible and effective integration into research and policy.

We have worked on knowledge graphs in Sfinno project.