We are looking for innovative success stories in which the use of AI-based technologies play a central role
The Call for Projects is aimed at organisations that have developed or adopted innovative artificial intelligence-based solutions and can demonstrate their impact.
A committee of experts made up of representatives of CIDAI members will review all the applications received and select those that best fit the themes of the congress, prioritising innovation, novelty and the degree of impact on companies and society.
If you have any questions about this call, you can send an email to events@eurecat.org
This year's program will cover the following subjects.
Which do you think your project fits best?
BEYOND LANGUAGE MODELS (I): MULTIMODAL MODELS
We are looking for real-world projects and use cases that explore the potential of multimodal models beyond language models, showing how different data sources have been combined and what value they bring to specific applications.
BEYOND LANGUAGE MODELS (II): WORLD MODELS
These models, often linked to robotics, autonomous systems, digital twins or advanced simulation, open up new possibilities for training AI systems that interact with the physical world or simulated environments.
We are looking for projects and use cases that explore the development or application of these world models in different sectors, showing how they have been used to improve the perception, planning or decision-making of intelligent systems.
FROM AI MODELS TO AI SYSTEMS: AGENTS, MULTIAGENTS, ORCHESTRATION
Concepts such as autonomous agents, multi-agent systems or model orchestration open up new possibilities for building more adaptive applications, capable of planning, coordinating actions and solving complex tasks.
We are looking for projects and real use cases that explore this transition from models to AI systems, including agent-based architectures, multi-agent environments or model orchestration.
VERTICAL AI MODELS: SPECIALIZATION VS GENERALIZATION
These models can offer advantages in terms of data quality, adaptation to context, computational efficiency and alignment with regulatory or linguistic requirements.
We are looking for real-world projects and use cases that explore the development or application of sovereign and vertical AI models in different sectors as well as initiatives that analyze the balance between generalist and domain-specific models.
LLMOps, OR HOW TO OPERATE LLMS AT AN ENTERPRISE SCALE
This session seeks real projects and use cases that show how organizations are integrating LLMs into business processes, addressing issues such as model observability and assignment, inference cost optimization or the management of multiple models in production.
We are especially looking for initiatives that explain how they have gone from prototype to production, what technical and organizational challenges have been faced and what real impact has been obtained in terms of business value.
DATA SPACES: FROM CONCEPT TO REALITY
Although the concept has generated great expectations, the current challenge is to move from conceptual design to real implementations capable of generating value from shared data.
In this session we are looking for real projects and use cases that show how data spaces are being deployed in different sectors, such as industry, mobility, health, energy, the public sector or others, and what real impact they are generating in terms of innovation and value creation, and what role AI plays in it, whether to exploit shared data, develop new data-based services or facilitate analysis and decision-making mechanisms.
FROM DATA SCARCITY TO SYNTHETIC DATA: AI PROJECTS AND USE CASES
We are looking for real-world projects or use cases from any sector that demonstrate the value of synthetic data: how it was generated, under what conditions, and how it has been used in training, validating, or deploying AI models and applications.
TRAINING AI WITHOUT CENTRALIZING DATA: FEDERATED LEARNING EXPERIENCES
This session seeks to identify real-world projects and use cases that demonstrate the potential of federated learning to collaboratively train AI models while keeping data at its source.
AI, REGULATION AND TECHNOLOGICAL SOVEREIGNTY: BUILDING ARCHITECTURES FOR AUDITABLE SYSTEMS
In this context, the issue of technological sovereignty also emerges, including the use of infrastructures and models developed in Europe.
We are looking for real projects and use cases that show how AI architectures are being designed and deployed to facilitate regulatory compliance. Initiatives that illustrate approaches to the concept of compliance by design, model monitoring and algorithmic governance mechanisms in real environments or initiatives linked to the concept of regulatory sandbox are of particular interest.
DATA, MODELS AND CLIMATE: CAN AI REALLY HELP UNDERSTAND CLIMATE CHANGE?
We are looking for projects that show how AI is being applied to better understand the climate system, integrating different sources of environmental data, improving the modeling and prediction of climate-related processes, and to the extent possible illustrating it with real cases.
REASONING MODELS: TOWARDS A NEW GENERATION OF AI SYSTEMS
We are looking for projects exemplified with real use cases that explore the potential of reasoning models in applied environments, whether in areas such as complex data analysis, operations planning, decision-making, or intelligent process automation. We are particularly interested in initiatives that show how these models integrate into broader AI systems, what advantages they offer over conventional generative models, and what technical and operational challenges their deployment in real environments poses.
