CFO Copilot | Muhammad Ryanrahmadifa (Ryan)

CFO Copilot

AI-Powered Personal Finance Management Platform

An AI-powered financial advisory platform using multi-agent orchestration to analyze financial documents and generate actionable insights. The system processes natural language queries, executes dynamic Python code for analysis, and delivers HTML infographics and React-compatible Plotly charts through conversational interfaces.

Multi-agent AI orchestration with semantic processing and dynamic analysis capabilities.

Technical Stack

  • Backend: FastAPI with async/await, Cloud SQL PostgreSQL
  • AI/ML: OpenAI GPT-4o, LangGraph for multi-agent workflows
  • Vector Database: ChromaDB with text-embedding-3-large (1024 dimensions)
  • Frontend: React 18 with TypeScript, Vite, Tailwind CSS
  • Visualization: Plotly for interactive charts
  • Infrastructure: Google Cloud Run, Cloud Storage, Vercel

Multi-Agent Architecture

LangGraph Workflow: Five specialized agents orchestrate query processing:

  • Query Analyzer: Intent classification and routing
  • Data Retriever: Semantic search across financial documents
  • Code Generator: Dynamic Python code creation for analysis
  • Code Executor: Sandboxed execution environment
  • Response Formatter: Output formatting with visualizations

Smart Caching: Similarity-based response caching with 10-minute TTL and 85% threshold, achieving 65% cache hit rate

Key Features

Natural Language Processing: Conversational interface for financial queries with intent classification and entity extraction

Document Processing: Multi-format ingestion (PDF, Excel, CSV, JSON) with OCR fallback for scanned documents

Dynamic Code Execution: Secure sandboxed Python environment (pandas, numpy, plotly) with AST validation and memory/time limits

Interactive Visualizations: Generates HTML infographics and React-compatible Plotly charts embedded directly in conversational responses

Subscription Management: AI-powered recognition of subscription renewals with lifecycle monitoring and cost analysis

Performance Metrics

  • <1 seconds for similarity-based cached queries, <30 seconds for new deep analyses
  • 50+ simultaneous users supported with horizontal scaling
  • 95%+ code execution and user query success rate