Loangent

Inspiration

Loangent came from the common problem many people (including me) face when dealing with loan agreements, that is, complex legal jargon that's difficult to understand. Financial institutions often present borrowers with lengthy contracts filled with technical terms, hidden fees, and unfavorable conditions that can be hard to spot without legal expertise. I wanted to create a tool that democratizes access to loan contract understanding, helping individuals make informed decisions without needing to hire expensive legal counsel for every loan agreement they encounter.

What it does

Loangent is an AI-powered loan agreement analyzer that helps users understand complex loan contracts by providing clear, structured summaries. The application allows users to upload loan agreements in PDF or Word format and receive an AI-generated breakdown of key terms, including loan amounts, interest rates, repayment schedules, collateral requirements, risks, penalties, and important clauses. The tool transforms dense legal documents into accessible, easy-to-understand summaries that highlight critical information and potential red flags, empowering users to make informed decisions about their financial commitments.

How we built it

I am using a modern React and TypeScript stack with Vite for fast development:

• The frontend is built with React components that handle file uploads and a responsive UI design.
• For document processing, we integrated pdf-parse for PDF files and mammoth for Word documents, allowing us to extract text content client-side.
• The AI analysis is powered by OpenAI's GPT model through their JavaScript SDK (it can be any OpenAI SDK-compatible service), which processes the extracted text and generates structured summaries in JSON format.
• I used react-router-dom for navigation between the landing page and analysis page, and implemented comprehensive error handling and loading states for a smooth user experience.

Challenges we ran into

The main challenges were handling different document formats reliably, as PDF parsing can be inconsistent depending on how documents were created. I also faced challenges with file size limitations and ensuring the application worked efficiently with large documents. Another challenge was structuring the AI prompts effectively to consistently extract the right information from diverse loan agreements. I had to iterate on the prompt engineering to ensure the AI correctly identifies and categorizes different elements of loan contracts. Additionally, managing the asynchronous operations for file processing and API calls required careful state management to provide a seamless user experience.

Accomplishments that we're proud of

Creating a user-friendly interface that makes complex legal documents accessible to everyone. The two-page design with a clear landing page explaining the service and a dedicated analysis page creates an intuitive user journey. I successfully implemented robust file validation and processing that handles both PDF and Word documents securely on the client-side. The structured summary output presents complex information in an organized, easy-to-digest format. I also implemented proper security measures including environment variable handling for API keys and client-side processing to protect user documents.

What we learned

About the complexities of document parsing and the importance of handling various file formats gracefully. I also learned about the importance of proper error handling and loading states in applications that depend on external APIs. Working with file uploads and text extraction provided valuable insights into client-side document processing techniques.

What's next for Loangent

Future plans for Loangent include expanding document format support to include more types of financial agreements and improving the accuracy of the AI analysis through better prompt engineering and potentially fine-tuning models on legal documents. I plan to add features like comparison tools to compare multiple loan offers side-by-side, notification systems for important deadlines, and integration with financial planning tools. I also want to enhance the security features with end-to-end encryption for document processing and explore offline capabilities for sensitive documents. Additionally, I plan to develop industry-specific analysis templates for mortgages, student loans, auto loans, and business loans to provide even more targeted insights.

Built With

• openai
• react
• vite