A decentralized, energy-optimized intelligence layer connecting AVA Nodes into a unified network

The RI-Net (Resonant Intelligence Network) is a decentralized AI network architecture designed to connect multiple AVA Nodes into a single, coordinated, resonant computing system. Its purpose is to provide a stable, scalable and energy-efficient infrastructure for running large language models, real-time AI workloads and distributed data-processing tasks across multiple locations.

The network can scale from a few AVA Nodes to thousands, supporting enterprise, national or global deployments.

What does RI-Net do?

RI-Net links AVA Nodes together and enables:

• – intelligent load balancing across all connected nodes
• – energy-aware routing, prioritizing lower-consumption compute paths
• – resonant pattern-based communication that reduces network noise
• – real-time synchronization of models and data flows
• – high fault tolerance – the network automatically re-optimizes if a node becomes unavailable

RI-Net operates purely through software — no special networking hardware is required.

Key advantages

• – energy-efficient distributed AI network (at least 30–40% savings)
• – faster response times and lower latency
• – strong privacy: processing happens on local nodes
• – stable performance even under high workloads
• – horizontally scalable from a handful of units to large clusters
• – cloud-independent, fully private infrastructure can be built

Where can it be deployed?

• – enterprise AI systems distributed across multiple sites
• – national digital infrastructures (education, healthcare, public services)
• – university and research environments
• – industrial, energy, or IoT-heavy ecosystems
• – decentralized or critical infrastructure where cloud cannot be used

Technology foundations

RI-Net integrates with all core layers of the AVA architecture:

• – AVA Core – central operational logic
• – RCF-LIM – optimized model execution
• – HGO – GPU/CPU workload balancing
• – ADC-Optim – energy-level system management
• – RCF-Secure – network security and anomaly detection

This combination enables RI-Net to function as a uniquely stable, energy-optimized, intelligent network.

Project status

• – ready-to-start project, architectural concept complete
• – easy to demonstrate with small pilot networks
• – scalable to national-level infrastructure
• – low integration risk
• – aligned with global trends in edge AI and privacy-preserving AI