FinBuddy is a world-class Finance SaaS directed at bridging the gap between messy, real-world physical data and structured financial intelligence. Unlike traditional expense trackers, FinBuddy utilizes an Advanced AI Intelligence Pipeline to automate extraction, predict budget burnout, and provide proactive wealth strategies based on longitudinal spending patterns.
We moved beyond legacy OCR to a native GPT-4o Vision architecture.
- Granular Extraction: Captures individual line items, quantities, and merchant-specific metadata.
- Deterministic Parsing: Enforced JSON schema outputs with
temperature: 0for reliable, reproducible financial data.
Powered by Supabase pgvector, our search goes beyond keywords.
- Embeddings: Uses
text-embedding-3-smallto generate 1536-dimensional vector representations. - Intent-Based Discovery: Find transactions by "vibes" or "categories" (e.g., searching for "morning routine" surfacing coffee shops and gym sessions).
Every transaction is an interactive knowledge base.
- Contextual Chat: Grounded RAG allows users to ask specific questions like "Is this a business write-off?" or "Break down the tax on this lunch."
- GPT-4o-mini Orchestration: Optimized for 100ms-range response times while maintaining high analytical accuracy.
- Budget Shield: A velocity-based predictor that projects month-end burn rates against user-defined limits.
- Smart Switch: An optimization engine analyzing recurring receipt items and manual bills to suggest bulk-buy or annual-plan savings.
To scale efficiently, we implemented a Vector-Fingerprint Cache:
- Input Hashing: Hashing transaction snapshots ensures we only call the LLM when data has meaningfully changed.
- 90% Cost Reduction: Re-serving cached insights for static financial states optimizes token usage without sacrificing UX.
| Layer | Technologies |
|---|---|
| Frontend | Next.js 15 (App Router), React 19, Framer Motion |
| Intelligence | GPT-4o Vision, GPT-4o-mini, text-embedding-3-small |
| Data Layer | Supabase (PostgreSQL) + pgvector |
| Security | RLS (Row Level Security) + JWT Authentication |
| Architecture | Secure Dual-Client Logic (Admin Role Verified by UID) |
Solution: Implemented a Non-Blocking Background Worker. The frontend receives a 201 Created immediately, and the full-process edge function updates the record via a secure Admin Client once GPT-4o Vision completes the analysis.
Solution: Architected a Context-Injection Guardrail. The LLM is NEVER given raw database handles. It is provided with a sanitized, strictly-filtered JSON projection derived from a JWT-verified Supabase session, ensuring zero data leakage between users.
We don't guess accuracy; we measure it.
node evals/scripts/run_eval.jsThe specialized eval suite benchmarks our Vision extraction and Intelligence endpoints against ground-truth datasets to maintain a 95%+ accuracy floor.
FinBuddy: Turning physical receipts into proactive financial power.