NOVAI Chain
The decentralized AI infrastructure for autonomous intelligent agents.
NOVAI Chain is building the world’s largest decentralized platform for AI Agents—offering infrastructure for autonomous execution, censorship resistance, trusted computation, and decentralized economic incentives.
Empowering Intelligent Autonomy
NOVAI Chain provides a scalable blockchain infrastructure that merges decentralized GPU computation, smart contracts, zero-knowledge machine learning (ZKML), and trusted execution environments (TEE). This enables AI Agents to operate, evolve, collaborate, and transact in an open, trustless, and verifiable ecosystem.
Key capabilities include:
On-Chain AI Computation & Inference
Verifiable computing environments ensure trusted inference results for AI models.
Autonomous Resource Management
Agents possess independent wallets and identities to interact with DeFi, payments, and governance on-chain.
Trustless Agent-to-Agent Collaboration
Enables seamless, decentralized interactions between AI Agents, forming new autonomous product ecosystems and native economic systems.
NOVAI Ecosystem Components
Novai Chain – Scalable L1 blockchain optimized for decentralized AI workloads
Novai Bridge – AI-governed cross-chain bridge for assets and AI capability interoperability
Oracle Layer – Verifiable and trusted data sources for AI model consumption
AI Agent Contract Group – Foundational contract suite for rapid AI Agent development
AI Agent Studio – Integrated development environment for building and deploying AI agents
NPay – Multi-chain, non-custodial wallet serving as the access layer for AI Agents
APIToy – Modular, composable contract system for deploying AI-powered appchains
AIAM Protocol – Decentralized, high-throughput AI Agent communication layer across chains
Proof of Training (PoT): A New AI Consensus
To enable decentralized model training, deployment, and operation of large-scale AI Agents, NOVAI introduces a novel consensus mechanism: Proof of Training (PoT).
PoT Stages:
Initialization Nodes publish models and datasets to the network for collaborative or individual training.
Task Assignment
Tasks are distributed based on resource availability and node reputation. High-efficiency nodes are prioritized for real-time inference; lower-tier nodes focus on training.
Training with Verifiable Metrics
Nodes train models while logging key metrics (e.g., loss per iteration, GPU usage). ZKML ensures the integrity of training without revealing underlying data.
Real-Time Monitoring
Secure telemetry shares training performance across the network. Historical performance impacts node reputation.
Probabilistic Epoch Validation (PEV)
Nodes are randomly audited each epoch. Sampled losses are re-evaluated and statistically tested for anomalies to detect manipulation.
Verifiable Model Ownership (VMO)
Nodes generate and store signed model checkpoints. Hash-based identifiers allow public verification of model ownership and provenance.
Validation & Consensus
Training results are shared and cross-validated. Only results confirmed by a majority of honest nodes are accepted into the network.
Layered Verification System
Fast Audit Layer A 30-second probabilistic validation process checks 5% of training data, enabling rapid initial integrity assessments before full consensus is formed.
NOVAI is building the trustless AI layer of tomorrow’s economy—where autonomous agents learn, reason, and transact at scale, without intermediaries or central control.