Case Study
Finsight AI
AI expense manager that automates transaction handling and budget guidance with async backend orchestration.
The Problem
Manual expense tracking is high-friction. Users abandon workflows when categorization and analysis require too much repeated effort.
The Solution
Finsight AI combines natural-language transaction capture with async processing so users get instant feedback while AI tasks run in the background.
Gemini converts natural language expense input into structured transaction data.
Celery + Redis move AI operations off request paths to keep APIs responsive under load.
MongoDB persistence for users, transactions, budgets, and derived analytics workflows.
JWT auth with Redis-based token controls for safer session lifecycle management.
Stack
Flask · Celery · Redis · Gemini API · MongoDB · Docker · Next.js · DigitalOcean · Vercel
Architecture
Backend and workers deployed on DigitalOcean, frontend on Vercel, APIs documented with Swagger/OpenAPI.
My Role
Built async AI request handling with Celery workers and Redis queueing around Flask service endpoints.
Integrated Gemini-powered automation to reduce manual expense categorization and enable intelligent suggestions.
Implemented secure API authentication with JWT and token invalidation strategy.
Shipped full-stack architecture from backend APIs and workers to frontend UX and production deployment.
Documented and tested core flows with OpenAPI specs and repeatable development workflows.
Technical Challenge
Challenge: AI latency blocking request flow
Running AI calls synchronously in request handlers caused avoidable wait times and degraded UX during concurrent usage.
Solution: Queue-backed async orchestration
- 01Accept and validate request in Flask
- 02Queue AI tasks via Celery + Redis
- 03Process AI jobs asynchronously in workers
- 04Persist results and surface updates in UI
Faster perceived interactions and a more resilient backend under concurrent real-world usage.
Explore Finsight AI