Ruvi AI's Public Beta is now Live!
Try it Now πŸš€
Ruvi AI
  • πŸ“„Abstract
  • 🧭Introduction
  • 🚧Problem Statement
  • πŸ’‘The Solution: Ruvi
  • βš™οΈCore Features
    • πŸ€–AI Tool Suite
  • 🧩Ready-Made Templates
  • 🧠User-Guided Model Training
  • πŸͺ™Tokenomics
    • 🧾Token Utility
  • πŸ“ŠToken Distribution
  • πŸš€Presale Structure
  • 🎁Presale Bonuses
  • πŸͺEcosystem
    • πŸ›οΈEcosystem & Governance
  • πŸ–₯️Technical Architecture
  • πŸ—ΊοΈRoadmap
  • πŸ‘¨β€πŸ’ΌTeam
  • πŸ”Links
    • Website
    • Ruvi AI Public Beta
  • Telegram
  • Twitter / X
Powered by GitBook
LogoLogo

Β© 2025 | Ruvi AI - All Rights Reserved

On this page
  • βš™οΈ AI Infrastructure
  • 🧠 Model Training & Feedback Loop
  • πŸ“± Cross-Platform Support
  • πŸ”— Blockchain & Web3 Integration
  • πŸ” Security & Privacy
Export as PDF

Technical Architecture

PreviousEcosystem & GovernanceNextRoadmap

Last updated 2 months ago

Ruvi is built with a modular and scalable architecture that fuses state-of-the-art AI infrastructure with decentralized technologies. This hybrid approach enables a fast, flexible, and community-driven platform while maintaining high standards for performance, security, and data integrity.


βš™οΈ AI Infrastructure

Ruvi leverages a multi-model AI stack that integrates various specialized systems for different media types:

  • Text Generation: Powered by transformer-based language models (e.g., GPT variants), fine-tuned for tone, structure, and prompt responsiveness.

  • Image Generation: Uses diffusion-based models for prompt-to-image synthesis, style transfer, and concept visualization.

  • Video Generation: Combines pretrained generative video models with Ruvi’s script-driven sequencing engine to produce short-form videos.

  • Audio/Sound: Employs text-to-speech, speech synthesis, and music generation models to enable lifelike voiceovers and soundscapes.

All models are optimized for real-time or near real-time generation, with scalable backend infrastructure hosted on GPU clusters.


🧠 Model Training & Feedback Loop

Ruvi incorporates user-in-the-loop learning, where user feedback, annotations, and prompt performance are used to improve model performance over time.

  • RLHF (Reinforcement Learning from Human Feedback) for text tools

  • Labeling queues for image and audio training datasets

  • Prompt success tracking to refine prompt-engineering heuristics

User contributions are tokenized and logged on-chain to ensure transparency and fair reward distribution.


πŸ“± Cross-Platform Support

Ruvi is available as:

  • A web platform (desktop-first but mobile responsive)

  • A native mobile app for iOS and Android, with:

    • Wallet integration for $RUVI balance, staking, and rewards

    • AI access optimized for mobile (voice input, camera-based prompts, etc.)

    • Push notifications for rewards, voting, and updates

All user data and interactions are synced across devices using a secure cloud backend.


πŸ”— Blockchain & Web3 Integration

Ruvi is built on an EVM-compatible blockchain (e.g., Ethereum or Layer 2 like Arbitrum or Polygon) to enable fast, secure, and low-cost transactions.

Key Web3 integrations include:

  • $RUVI Token Smart Contract: ERC-20 token with staking, vesting, and governance modules.

  • Wallet Support: MetaMask, WalletConnect, and integrated in-app wallet.

  • Decentralized Identity (DID) (future feature): To allow reputation-based voting and contribution tracking.

  • On-Chain Governance Module: Voting, proposal creation, and treasury management.

All critical interactions (staking, voting, rewards) are recorded on-chain, while AI data and content remain off-chain for performance and privacy.


πŸ” Security & Privacy

  • End-to-end encryption of user inputs and generated outputs

  • Secure token vaults for user wallets and staking contracts

  • Periodic third-party audits for smart contracts and platform infrastructure

  • User-controlled data: No data is sold or used without opt-in for training purposes

πŸ–₯️