A RAG-driven intelligence engine built with Python & Cursor. Features automated Playwright ingestion and Agentic workflows, mastering AI SDLC from architecture to risk mitigation.
Autonomous Data Ingestion (The Pipeline)
Concept: Zero-manual-entry data ecosystem.
Description: Engineered a robust, automated collection layer using Python (Playwright & FastAPI) to aggregate transactions from bank statements, JDG accounting exports, and B2B contract milestones. This ensures real-time financial visibility without the risk of human error or manual overhead.
Contextual RAG Engine (The Memory)
Concept: High-integrity Retrieval-Augmented Generation.
Description: Implemented a RAG (Retrieval-Augmented Generation) architecture that indexes historical financial data, tax liabilities, and strategic business goals into a vector database. This allows the system to provide answers based on actual, private financial context rather than generic LLM assumptions.
Natural Language Financial Querying (The UX)
Concept: Conversational Intelligence.
Description: An LLM-powered interface that transforms complex financial analysis into a natural dialogue. Users can interact with their data as they would with a CFO.
Query Example: “Calculate my projected net income for Q2, accounting for the VAT forecast and upcoming accounts receivable from the ‘KC’ project.”
Predictive Anomaly Detection
Concept: Proactive Risk Mitigation.
Description: A specialized AI agent that continuously monitors budget deviations and triggers early warnings for cash-flow risks (R6). By detecting patterns before they impact liquidity, the system ensures business resilience and informed decision-making.
AI-Native Architecture & Tech Stack
Core Intelligence
- RAG Architecture: Context-aware financial data retrieval.
- Python & Playwright: High-performance automated ingestion.
- LLM-as-a-Judge: Automated validation of data integrity & insights.
- Vector Database: Semantic search for transactional patterns.
Development Ecosystem
- Cursor AI-Native IDE: Next-gen development workflow.
- FastAPI Framework: Robust and scalable API orchestration.
- Agentic Workflows: Independent AI agents for risk monitoring.
- Continuous ETL: Scheduled Cron-jobs for real-time sync.
The Objective: Beyond simple tracking, the project serves as a Personal Financial Intelligence Engine. It utilizes RAG to bridge the gap between raw banking data and proactive business decision-making, effectively mitigating cash-flow risks through predictive AI-driven insights.