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The system runs on a distributed architecture consisting of:
  • A web-based console for user interaction, asset management, and real-time monitoring.
  • A core API service responsible for business logic, data persistence, and job orchestration.
  • A Redis-based queue and caching layer enabling asynchronous job distribution, rate limiting, and system decoupling.
  • Distributed workers that execute high-performance scanning tasks, designed for horizontal auto-scaling and fault tolerance.
  • A PostgreSQL database for persistent storage of assets, scan results, and system state.
  • An MCP (Model Context Protocol) server that provides structured context to AI systems.
  • Integration with AI/LLM components to enable intelligent querying, analysis, and automation over collected asset data.