LedgerOne

TAGS // React · FastAPI · Local AI · Plaid · Fintech

A local-first personal budgeting app that auto-syncs with Chase through Plaid and runs every AI feature on my own hardware. No subscription, no cloud, no data leaving my desk.

Overview

Every budgeting app I have ever used eventually wants me to either pay a subscription, ship my transaction history to their servers, or watch features I rely on get gated behind a higher tier. LedgerOne is my answer to all of that. It is a personal finance app that runs entirely on my own machine, talks directly to Chase through Plaid Production, and uses a local Ollama model for every piece of “AI” in the product. Nothing about my spending leaves the box.

The frontend is a React 19 + Vite single-page app. The backend is a FastAPI server on port 8787 with a single SQLite database file. The two talk over a proxied /api and that is the whole architecture. When I open the app I get a dashboard with monthly income, spending, savings, cash flow, budget status, recent transactions, and an AI brief. There is a transactions table with search, filters, review states, and CSV export, a budget planning screen with category progress and planned payments, an insights screen for AI prompts and detected patterns, and an accounts screen that handles Chase via Plaid Link with a CSV import fallback for anything Plaid does not cover. The CSV importer has duplicate detection and a review preview so I never double-count a transaction.

The AI side runs on qwen3:14b through Ollama on my desktop. It handles categorization, generates the daily brief, surfaces patterns, and answers free-form questions about my spending. It is doing real work, not a chat gimmick. Because the model is local, I can let it see everything without thinking twice about who else might one day read those prompts.

Key Features

Local-first architecture: SQLite database, FastAPI backend, no cloud dependency
Chase auto-sync through Plaid Production with CSV fallback for anything outside Plaid’s coverage
AI categorization, daily briefs, and pattern detection running entirely on a local Ollama model
Dashboard with monthly income, spending, savings, cash flow, budget status, and recent transactions
Transactions table with search, filters, review states, and CSV export
Budget planning with category progress, planned payments, and rule-based automation
CSV import with duplicate detection and a review preview before commit

Tech Stack

React 19 + Vite (frontend)
Python + FastAPI on port 8787 (backend)
SQLite for storage
Ollama running qwen3:14b for all AI features
Plaid Production API for Chase sync