The latest articles on DEV Community by Abhishek Chauhan (@abhishek_chauhan_dev). https://dev.to/abhishek_chauhan_dev https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F4179972%2F027805a5-9ecf-474a-a0cb-4b08b330182c.png https://dev.to/abhishek_chauhan_dev en Abhishek Chauhan Fri, 16 Oct 2026 06:57:51 +0000 https://dev.to/abhishek_chauhan_dev/i-built-a-smart-invoice-assistant-using-local-ai-3l1i https://dev.to/abhishek_chauhan_dev/i-built-a-smart-invoice-assistant-using-local-ai-3l1i <h1> I Built a Smart Invoice Assistant Using Local AI </h1> <h2> The idea </h2> <p>Invoices contain important information such as client names, invoice numbers, amounts, and due dates. Extracting these details manually can take time, especially when handling multiple invoices.</p> <p>For Hacktoberfest DEV Challenge Week 2, I built <strong>Smart Invoice Assistant</strong>, a small web application that uses a locally running AI model to extract invoice details and generate payment reminder drafts.</p> <h2> What does it do? </h2> <p>The application currently has two main features:</p> <ul> <li> <strong>Invoice analysis:</strong> Paste invoice text and let AI extract the client name, invoice number, amount, currency, and due date.</li> <li> <strong>Payment reminder generator:</strong> Generate a polite payment reminder using the invoice text and copy it for later use.</li> </ul> <h2> Tech stack </h2> <p>I built this project using:</p> <ul> <li>HTML for the structure</li> <li>CSS for the user interface</li> <li>JavaScript for frontend interactions</li> <li>Node.js for the local server</li> <li>Ollama to run the AI model locally</li> <li>Qwen 2.5 3B as the open-weight AI model</li> </ul> <h2> Why local AI? </h2> <p>I wanted to experiment with running an AI model on my own machine instead of depending on a paid external AI API.</p> <p>The application sends invoice text to the Ollama service running locally on my computer. This also gave me an opportunity to learn how a frontend communicates with a backend and how a backend can call a local AI model.</p> <p>Local processing can help reduce reliance on external AI services, although users should still protect sensitive invoice information and secure their local environment.</p> <h2> What I learned </h2> <p>While building this project, I practised connecting HTML, CSS, and JavaScript with a Node.js server, handling API requests, displaying AI responses, and using Git and GitHub to manage and publish my code.</p> <p>I also learned that a useful AI application does not need to be complicated to start with. A focused workflow can be a good foundation for future improvements.</p> <h2> Try the project </h2> <p><strong>GitHub repository:</strong> <a href="https://github.com/Abhishek-gitty/smart-invoice-assistant" rel="noopener noreferrer">https://github.com/Abhishek-gitty/smart-invoice-assistant</a></p> <p>The current version runs locally on a computer with Node.js, Ollama, and the required model installed. It is not a hosted web demo yet.</p> <h2> Current limitations and future plans </h2> <p>This is an early version, and there is plenty of room to improve it.</p> <p>Some features I would like to explore next are:</p> <ul> <li>PDF invoice upload and text extraction</li> <li>Saving invoice history</li> <li>Better input validation and error handling</li> <li>More structured invoice summaries</li> <li>Improved accessibility and responsive design</li> </ul> <h2> Final thoughts </h2> <p>This project was a hands-on way for me to explore local AI and connect it to a practical web application.</p> <p>I would love to hear suggestions on what feature I should build next and how I can make the application more useful for people who manage invoices.</p> <p>Thanks for reading!</p> devchallenge hf26challenge javascript