6.2 System Architecture & Components

Layrz AI is architected as a modular, event-driven microservice system designed for ultra-low latency and horizontal scalability. The platform’s backend stack is built using a hybrid of Node.js (TypeScript) for WebSocket/messaging orchestration and Python for ML inference, contract generation, and code audits. Each component is containerized via Docker and orchestrated using Kubernetes (K8s) with a CI/CD pipeline integrated via GitHub Actions and ArgoCD for zero-downtime deploys.

At the heart of the Layrz infrastructure is the AI IDE engine, which communicates via gRPC with our PyTorch inference servers hosted across GPU-optimized nodes (NVIDIA A100 or H100) on a multi-region GCP + Azure deployment. Inference jobs are queued and load-balanced via Redis-backed Celery workers, with a fallback autoscaler that dynamically spins up inference pods based on demand surges (e.g., token launch windows).

The platform maintains live chain sync using Web3.js and Ethers.js clients, paired with Alchemy, QuickNode, and BloxRoute RPC relays to maximize uptime and ensure transaction propagation speed. This node relay layer allows the Layrz backend to push transactions within milliseconds of user confirmation, even across multi-chain deployments (ETH, BSC, BASE, etc.). For Solana compatibility, we utilize @solana/web3.js and run our own solana-validator cluster for real-time ledger access.

A key architectural layer is our event router and memory bus, which tracks:

• User intent & prompts

• IDE session data

• Contract snapshots

• Deployment status

• Telegram bot commands

This is handled by a stateful Redis pub/sub pipeline + PostgreSQL for persistent contract logs, user settings, and API token management. Each Telegram bot is its own stateless service, subscribing to this bus to perform isolated operations like deployContract(), simulateVolume(), or generateLandingPage() using real-time websocket streams.

We’ve also implemented audit and linting services using Slither, Mythril, and custom rule-based AST scanners built in Python. This ensures every AI-generated contract passes structural, syntactic, and security validations before deployment.

// User-to-IDE message handler

bot.onText(/\/deploy/, async (msg) => {
  const contract = await generateSolidityFromPrompt(userPrompt);
  const lintReport = await runContractAudit(contract);
  if (lintReport.isSafe) deployToChain(contract, selectedNetwork);
});

Each layer is built with fail-safe logging, rate limiting, and chain-aware fallback logic, meaning if an RPC endpoint fails or gas spikes are detected, we can reroute or queue until optimal execution resumes. This makes Layrz not just powerful, but robust and production-ready.