FinBuddy:AI-Powered Personal Finance Tracker
Full-stack AI platform with OpenAI GPT-4, OCR, and predictive analytics.
Core Impact
“Transformed raw financial visual data into semantically searchable assets with automated spending pattern analysis.”
Architecture Breakdown
Built a GPT-4o Vision receipt intelligence pipeline at temperature=0 for deterministic extraction of amount, category, date, and itemized line items; ran a separate GPT-4o-mini pass on raw OCR text to generate per-receipt insights (store name, unusual spend flag, category guess) stored in `expenses.insights_json` for zero-latency display.
Engineered semantic search using 1536-dim embeddings via `text-embedding-3-small` stored in pgvector, with a `match_expenses` cosine similarity RPC and 600ms client-side debounce — enabling natural language retrieval ("coffee last week", "anything from Whole Foods") ranked by intent rather than keyword.
Reduced LLM API costs ~90% via SHA-256 hashing of the current expense snapshot, short-circuiting summary generation on cache hits before any OpenAI call.
Designed Budget Shield (velocity-based spend/day × days_in_month projection, zero LLM, instant response) and Smart Switch (50-transaction GPT-4o-mini pass returning one high-impact savings recommendation, e.g. "switch Disney+ to annual — saves $24/year").
Architected multi-tenant row-level security enforced at the Supabase database layer via RLS policies, and secured receipt storage with server-generated signed URLs (1-hour expiry) via an ownership-verifying API route — preventing unauthorized access regardless of client-side logic.
Systems Analysis Concluded