AI-DAPT reduces the time and costs associated with developing AI solutions, increasing the quality and transparency of models and facilitating the reuse of data and functionality. Integration into real industrial scenarios facilitates access to new markets and the replicability of solutions, improving competitiveness at national and international level.

Develop an intelligent AI-Ops framework that integrates end-to-end automation and a human-in-the-loop approach throughout the entire AI data and model lifecycle.

Build scalable, observable, and adaptive AI data pipelines that learn from context and ensure continuous reliability and quality.

Integrate science-driven AI models with data-driven approaches, creating transparent and consistent hybrid solutions.

Validate the scientific and practical value of the project through application in four industrial sectors and integration into existing AI solutions.

Greater efficiency in the development and management of AI solutions, with reduced time and operating costs.

Better model quality thanks to structured, explainable, and unbiased data.

Easy access to representative and reusable data, thanks to advanced documentation, annotation, and data augmentation techniques.

Optimization of resources through reduction of duplication of activities and standardization of pipelines.

Increased business competitiveness through more reliable, transparent, and adaptable AI solutions.

Creation of new data-centric business models that can be replicated in multiple industrial contexts.

Promoting interoperability between systems, sources, and data spaces, fostering integrated AI ecosystems.

Strengthening trust in AI technologies through human-in-the-loop tools and explainable AI.