This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend
What I Built
For this challenge, I wanted to build something that could actually be useful to someone I know instead of making another general-purpose AI chatbot.
The person I had in mind works with business transactions and often has to go through Excel files to understand where money is coming from and where it is being spent.
The problem is simple, but it takes a lot of time.
You have a spreadsheet with hundreds of transactions, and then you start asking questions like:
"Why did my expenses increase this month?"
"Where am I spending the most money?"
"Which transactions should I take a closer look at?"
So I built LocalLedger AI.
It is a small-business finance assistant where you can upload a CSV or Excel file and get useful financial insights from it.
It can show:
- Total income
- Total expenses
- Net cash flow
- Monthly trends
- Expense categories
- Large or unusual transactions
- Month-to-month changes But the part I was most interested in building was "Ask My Business." Instead of manually going through the spreadsheet, you can simply ask: "Why did my expenses increase this month?"
The application analyzes the actual transaction data and explains what changed.
Demo
https://drive.google.com/file/d/1_2WqyBjR_yyFeZ21ZKznKt6t6KWZ3YKx/view?usp=drive_link
Code
https://github.com/piyushkr1155/Finance-Assistant
How I Built It
The project is built using:
- Next.js + TypeScript for the frontend
- Tailwind CSS for the interface
- Recharts for charts
- FastAPI for the backend
- Python + Pandas for processing financial data
- OpenPyXL for Excel files
- Ollama for running the AI locally
- An open-weight LLM for understanding and explaining the financial data The basic flow looks like this: Excel / CSV ↓ Next.js ↓ FastAPI ↓ Pandas ↓ Financial Analysis ↓ AI Context Builder ↓ Local Open-Weight LLM ↓ AI Explanation ↓ Dashboard One thing I was careful about was not letting the AI calculate everything itself.
Why Does Open Innovation Matter?
This was one of the most interesting parts of the project for me.
Financial data is sensitive.
If someone is uploading their business transactions to an AI assistant, they may not want that information automatically sent to an external AI service.
That's why I wanted the AI part of LocalLedger to run locally using an open-weight model through Ollama.
This means the project can use local AI without depending on a closed AI API for its main intelligence.
It also means developers can experiment with different models and improve the application without rebuilding the whole project.
For me, open innovation isn't just about using something because it's open source.
It actually fits the problem.
The more control the user has over where their financial data goes, the better.
My Agent Session
I used AI-assisted development throughout the project to help with planning the architecture, implementing features, debugging issues, writing tests, and improving the overall user experience.
I built LocalLedger AI step by step with the help of Antigravity, using AI assistance as part of the development workflow rather than simply generating a finished application in one go.
Prize Categories
I am submitting LocalLedger AI for the Overall Winner category.
The project uses Ollama with open-weight local models such as Llama 3.2 1B and Qwen 2.5 1.5B for local financial analysis and grounded natural-language explanations.
I am not claiming a partner category that requires a technology that is not actually used in the project.
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