FinTrack
Intentionally built as a proof-of-concept for systems-thinking capability, not to solve a personal problem. Deployed live in production as proof of execution rigor.
The Problem & Constraint-First Thinking
Expert accountants expensive & slow. SMK/magang-level admin do data entry. Management needs accurate P&L visibility.
Operator (SMK/magang) unfamiliar with laptop/complex software. Solution: Telegram bot interface (mobile-first, familiar, low friction). Result: zero IT training needed, day-1 usable.
Prevent duplicate entry (retried webhook deliveries), unauthorized changes, fraud. Solution: an idempotency key on every Telegram delivery plus an API logic layer hidden behind the bot. Result: system enforces rules, operator cannot bypass, every change attributed to an actor.
Key Metrics
Manual Cycle Time
Anomaly Detection
Update Frequency
Solution Architecture
- Frontend
- Vite + React dashboard (analytics, account search, real-time P&L view)
- Backend
- Supabase PostgreSQL (single-entry running balance, anomaly detection)
- Input Layer
- Telegram Bot (Python) connected to the API
- Deployment
- Vercel (frontend), Railway (bot + backend logic)
- Methodology
- Vibe coding with Claude — AI-augmented development, not manual coding
Lessons Learned
Initially assumed Supabase's free tier was sufficient for anomaly-detection queries. At 1,000+ transactions, query latency increased. Solution: incremental Z-score calculation with a caching layer. Learning: production-level accounting systems must pre-optimize for scale, not react to it after the fact.