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GeckoGuard-AI — Distributed Risk Intelligence Engine & Three-Tier Audit Pipeline

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Overview

A production-grade distributed system designed to evaluate cryptocurrency assets. It utilizes a microservice architecture to bridge high-performance web engineering with LLM-driven financial risk analysis.

Tech Stack

Ruby on RailsNext.js 15 & Tailwind CSSPython (FastAPI / Asyncio)PostgreSQLGoogle Gemini 2.5 FlashTypeScript

Features

  • Microservice Architecture: Decoupled Rails backend and Python AI service for optimized scalability.
  • Interactive Risk Matrix: Real-time data visualization using Recharts and Tailwind CSS.
  • Automated Audit Persistence: Relational database modeling in PostgreSQL to track historical risk scores.
  • Resilient Ingestion: Strict semaphore rate-limiting to interface safely with high-throughput market APIs.

Key Decisions

01

Migrated from a monolithic script to a Rails-API and Python microservice pattern to demonstrate professional separation of concerns.

02

Implemented Cross-Origin Resource Sharing (CORS) and JSON-API standards to ensure secure communication between the three localized servers.

03

Replaced Streamlit with a Next.js frontend to meet production-level web standards and Core Web Vitals optimization.

Learnings

→Mastered the integration of Ruby on Rails with external AI microservices via asynchronous HTTP protocols.
→Refined the ability to build responsive, data-heavy dashboards using React Hooks and TypeScript for strict type-safety.
→Learned to manage complex local development environments involving multiple concurrent server ports (3000, 3001, 8000).
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