Applied AI / Apr 2026
Drift.ai
Privacy-conscious lifestyle-spending analysis
Analyzes spending drift, runs AI-guided behavioral interviews, and generates personalized recovery plans while keeping raw transactions local.
2transaction ingestion paths
30-daypersonalized recovery plans
What I built
- Built a full-stack AI personal finance platform that replaced a fragmented 7-step manual analysis workflow with a unified product flow across 2 transaction ingestion paths (CSV + Plaid), automated drift detection, what-if simulation, and PDF/email report delivery.
- Engineered a hybrid deterministic + LLM pipeline that combined BLS CPI-U inflation-adjusted spending analysis with local Qwen via Ollama to run multi-turn behavioral interviews, classify spending into 6 behavior tags, and generate personalized 30-day recovery plans backed by 8 cited financial evidence fields.
- Implemented a production-style, privacy-first architecture with Auth0, Stripe Checkout, Resend, Vercel, and a 4-service Dockerized stack; kept raw transaction rows local while persisting only 6 summary data entities in Supabase, enabling monetized report delivery with minimal sensitive-data retention.
Explore the implementation
Source code, setup instructions, and project documentation are available in the repository.