Selen AI
  • Executive Summary
  • Introduction
    • Background & Motivation
  • Market Opportunity
  • Problem Statement
    • Fragmented Capabilities Across Platforms
  • Lack of AI-Driven Risk Intelligence
  • Poor Usability and Compliance Standards
  • Selen AI Architecture
    • AI / ML Core
  • Data Layers & Connectors
  • Execution Layer
  • Security Mesh
  • Feature Suite
    • Volume-Sniping Engine
  • New-Token Sniper — AI Pattern Recognition
  • Zeta-Market Perpetual Copy-Trading
  • Volume-Booster (AI-Optimised Market Making)
  • AI Pools-Filter for Liquidity Provision
  • Risk Engine & Portfolio Orchestration
  • Automated Buys & Sells
  • Low Funds Detection
  • Security, Compliance & Data-Governance
    • Key Management
  • Access Control
  • Data Security
  • Compliance Toolkit
  • System Monitoring
  • Token Utility & Economic Design
    • Token Utility
  • Roadmap
    • Phase 1: Launch (v 1.0)
  • Phase 2: Expansion & AI Enhancements
  • Phase 3: Multi-Chain Expansion & Advanced Features
  • Phase 4: Customization & Community Governance
  • Links
  • CONCLUSION
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  1. Selen AI Architecture

AI / ML Core

PreviousPoor Usability and Compliance StandardsNextData Layers & Connectors

Last updated 12 days ago

At the heart of Selen AI lies a sophisticated artificial intelligence engine that drives decision-making across all features. This core comprises:

  • Time-Series Transformers: Analyze historical token price movements and liquidity inflows to anticipate short-term trends and market reactions.

  • Gradient-Boosted Decision Trees (GBDTs): Evaluate structured data such as token metadata, pool statistics, and developer behavior for predictive scoring.

  • Graph Neural Networks (GNNs): Model relationships between wallets, token contracts, and liquidity pools to detect hidden risk vectors and identify influential actors.

These models work together to generate real-time scores for:

  • Pool Quality

  • Liquidity Shock Impact

  • Trader Skill-Level & Alpha Potential