Artificial Intelligence for Profitability

This technology integrates advanced AI algorithms into conventional information-processing workflows to overcome the limitations of traditional analysis. Its approach focuses on “strategic exploitation”, whereby data is not merely a static metric but a dynamic asset that predicts future behaviour.

The solution addresses the complexity of today’s data through:

• Intelligent ETL Processes: Data extraction, transformation and loading processes that use clustering techniques to automatically organise unstructured repositories.
• Non-linear Predictive Models: Moving beyond conventional statistical models through the use of decision trees and pattern-mining algorithms capable of identifying useful signals in environments with excessive “noise” or irrelevant information.
• Data Streaming Architecture: The ability to adapt information systems to environments that change from second to second, allowing the model to learn and evolve in real time.

• Proven Experience: More than 15 years of effective technology transfer to both the public and private sectors.

• Processing Speed: Development of optimised algorithms to minimise response times, even when handling massive volumes of data.

• Superior Accuracy: Specialisation in fine-tuning predictive models to maximise accuracy in financial and operational decision-making.

• Direct Access to the Source: As a research group within a public university, it offers direct contact with the scientists who develop the technology, without commercial intermediaries or hidden licence costs.

Commercially available. The group has solutions already implemented in various business and technology management applications, demonstrating a very high capacity to adapt to each client’s specific needs.

• Technology and Software Development Companies.
• Banking, Insurance and Finance.
• Large-scale Retail and Logistics.
• Public Administrations.

Alejandro Rabasa Dolado – Operations Research Centre