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

RI-Net (Resonant Intelligence Network) is the system-level extension of the AVA Resonant Intelligence architecture. It is a decentralized intelligence network designed to connect multiple AVA Nodes into a single, coherent, resonantly operating system.

The purpose of RI-Net is to provide a stable, scalable, and energy-efficient infrastructure for distributed execution of large language models, real-time AI workloads, and data-intensive computations.

Why RI-Net is needed

Most current AI infrastructures are:

• – highly centralized,
• – cloud-dependent,
• – energy-intensive,
• – and increasingly problematic from a data-sovereignty perspective.

Organizations are looking for alternatives that:

• – run AI locally,
• – distribute workloads intelligently,
• – and avoid disproportionate increases in energy consumption.

The solution – a resonant intelligence network

RI-Net is a software-defined network architecture that:

• – connects AVA Nodes into a coordinated system,
• – distributes computational load intelligently,
• – applies energy-aware routing strategies,
• – and synchronizes execution through resonant behavioral patterns.

RI-Net is not a conventional compute cluster. It is a dynamically self-optimizing intelligence network.

What does RI-Net do?

• – Intelligent workload distribution
  across connected AVA Nodes
• – Energy-aware routing
  prioritizing lower-consumption execution paths
• – Resonant communication
  reducing network noise while maintaining coherence
• – Real-time synchronization
  of models and data flows
• – High fault tolerance
  automatic re-optimization when nodes become unavailable

Key benefits

• – 30–40% more energy-efficient distributed AI operation
• – lower latency and faster response times
• – strong data-privacy advantages through local processing
• – stable performance under high load
• – horizontal scalability: from a few nodes to thousands
• – fully cloud-independent, private infrastructures are possible

Deployment scenarios

• – enterprise AI networks across multiple sites
• – national digital infrastructures (education, healthcare, public services)
• – university and research environments
• – industrial, energy-sector, and IoT-heavy systems
• – decentralized and critical infrastructures

Technology foundations

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

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

This integration enables RI-Net to function as a stable, secure, and energy-optimized intelligence network.

Project status

• – ready-to-start project
• – architectural concept finalized
• – easily demonstrable with small-scale pilot networks
• – incrementally scalable to national or international levels
• – low integration risk

RI-Net represents the system-level realization of Resonant Intelligence — where local AI units no longer operate in isolation, but as part of a coordinated, energy-efficient intelligence network.