DAI is developing a decentralized AI system consisting of local AI nodes, autonomous AI hubs and a distributed AI network. The goal is to create models that can run locally on edge devices, support private and resilient AI services, and cooperate across a network of nodes without depending entirely on centralized cloud infrastructure.
The project focuses on training, fine-tuning and optimizing AI models for decentralized and edge environments. These models will support local language and multimodal AI services, knowledge-base assistants, sensor and video analytics, node monitoring, task routing and energy-aware workload distribution across the DAI network.
DAI Nodes are designed to provide local AI capacity for businesses, public institutions and communities, while DAI Green Hubs combine edge computing, renewable energy and communication infrastructure to support resilient AI services even in situations with limited internet connectivity or unstable power supply.
The proposed work aims to advance privacy-first, local-first and energy-efficient AI infrastructure. By combining edge AI, federated learning, distributed computing and intelligent network coordination, DAI seeks to create a scalable foundation for future AI services for smart cities, critical infrastructure, SMEs and local communities.
Principal Investigator, Company and Country
Ryslan Pylypenko, DAI d.o.o., Slovenia