ARVI — Agentic Regeneration Via Intelligence

## The Problem Environmental data is centralized in government agencies and NASA satellites — inaccessible, slow, and disconnected from local realities. There is no real automated analysis capable of preventing crises or identifying climate patterns before damage is done. The human factor in environmental solutions lacks economic incentives, is bureaucratic, and cannot act at the speed the planetary crisis demands. ## The Solution ARVI is a decentralized sensor network that collects environmental data, analyzes it autonomously, and takes agentic actions to protect the environment — without waiting for humans. Users buy physical sensors, deploy them in forests, parks, or any ecosystem, and earn passive income. Each sensor streams hyperlocal data (CO₂, soil moisture, pathogen risk, biodiversity, air quality) that satellites and city APIs cannot capture. Venice AI (llama-3.3-70b, zero data retention) continuously analyzes sensor streams, correlates patterns, and identifies active threats. When confirmed, agents act: alerts sent, operators notified, actions logged on Base. ## The One Real Loop (no humans required) ``` curl -X POST https://arvi-eight.vercel.app/api/analyze body: {node_id: node-01, email_alert: [email protected]} ``` 1. Venice AI analyzes privately — simulated: false 2. Anomaly confirmed → email alert to operator 3. On-chain event on Base Mainnet 4. Logged to /alert-log.json (public, verifiable) ## Live - Contract on Base: 0x8118069E26656862F8a0693F007d5DD7664Acb00 - Alert tx: https://basescan.org/tx/0x28343f43738d306fcf7c6d794d9e057643960aeac6c761284399346f741b5416 - Agent manifest: https://arvi-eight.vercel.app/arvi.skill.md - ERC-8004 tx: https://basescan.org/tx/0xb8623d60d0af20db5131b47365fc0e81044073bdae5bc29999016e016d1cf43a ## Sponsors - Venice AI — private inference, zero retention - Protocol Labs (ERC-8004) — on-chain agent identity, autonomous loop - Base — ARVIAgent contract, all payments - Octant — data collection and analysis for impact project evaluation - ENS — human-readable node identity