Chat with client In -> Invoice Out

A developer built Chat2Invoice, an open-source tool that converts pasted client chat conversations into ready-to-send invoice PDFs using Google's Gemma 3 model running locally through Ollama. The app extracts invoice fields from the chat, flags anything it cannot find for human review, and generates an A4 PDF, with an optional email-sending step via Resend; all processing stays on the user's machine so sensitive client conversations never reach a cloud API.

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle https://dev.to/challenges/hacktoberfest-week2-2026-10-12 Every freelancer knows this moment. The client says "Great work, can you send the invoice?" and suddenly you're digging through 200 WhatsApp messages to find the rate, the hours, the email address, and that one message where the price changed from $250 to $300. Then you open a template, retype everything, export a PDF, and email it. Ten minutes gone, for something that's already written down in the chat. Chat2Invoice fixes that. You paste the conversation, and a local AI turns it into a ready-to-send invoice PDF. Who would pay for this? Anyone who gets paid directly, outside platforms that auto-generate invoices: freelance designers, developers, writers, consultants, and small agencies who negotiate in DMs and then have to turn those DMs into paperwork. The best part is that the chat never has to leave your machine. Step 1: Paste the chat Step 2: Review what the AI found The AI shows what it extracted and highlights anything it couldn't find. Here it caught one field was not found in chat. Step 3: Preview, download, send Struggling to learn Git? Learn Git by solving problems on GitMission.com https://www.gitmission.com/ Paste a client chat → local AI Ollama extracts invoice details → review/edit → generate an A4 PDF → download or email it. 1. Install Ollama, then pull the model one time, ~3 GB ollama pull gemma3:latest 2. Install dependencies cd chat2invoice npm install 3. Environment cp .env.example .env.local edit if needed .env.local : OLLAMA AI MODEL=gemma3:latest OLLAMA HOST=http://127.0.0.1:11434 Optional – the "Send Invoice via Email" section only appears when BOTH are set: RESEND API KEY=re xxx RESEND SENDER EMAIL=invoices@your-verified-domain.com Shell environment variables work too. RESEND SENDER EMAIL must… The open-source AI part: Gemma 3 https://ai.google.dev/gemma running locally through Ollama https://ollama.com . No API key, no per-request bill, no server in the middle. The app talks to Ollama on 127.0.0.1:11434 , and that's where the "understanding" happens. The stack: The flow: INV-2026-OCT-13-JD2N and issue date are auto-generated, and "Due Now" defaults to amount minus discount. I intentionally put a human checkpoint in the middle. The AI proposes, and you approve. A small local model will occasionally misread a message, and an invoice is a document with money on it, so the review step is a feature rather than a limitation. Missing details get flagged and asked for instead of invented. Running it yourself takes a minute. Pull a model with Ollama, then create .env.local : OLLAMA AI MODEL=gemma3:latest OLLAMA HOST=http://127.0.0.1:11434 Optional: the "Send Invoice via Email" section only appears when BOTH are set RESEND API KEY=re xxx RESEND SENDER EMAIL=invoices@your-verified-domain.com RESEND SENDER EMAIL must be on a domain verified in your Resend account. Skip these two lines and the app simply hides the email section, so everything else works fully offline. Client chats are some of the most sensitive text a freelancer owns. They contain real names, email addresses, rates, project details, and sometimes things covered by an NDA. Pasting that into a closed cloud API just to get a PDF is a bad trade. Open-weight models changed the deal: OLLAMA AI MODEL . If a better open model shows up next month, swapping it in is a one-line change, with no pricing changes or deprecations to worry about. A closed API could do the extraction, but it couldn't make the promise that matters most to the person using this tool: your client's conversation never left your laptop. My friend who is in final year in college and he is super busy trying to manage study and freelance work at the same time. So this will save him a few hours monthly.