Action-based Email Labeling System

Using n8n with local #AI and #Ollama to categorise emails is proving to be really valuable for me. I've been meaning to sort out my email inbox for, well, let's just say it's been on the to-do list for years. We're talking about a backlog of around 11k emails that needed sorting. I'd been keen on using AI for this task for ages, but the idea of paying API providers didn't sit well with these numbers. So today, I invested a bit of time in building an automation to do the heavy lifting. It reads my emails, tags them with predefined categories, and moves them to the appropriate folders. Invoices, receipts, junk, newsletters, action items - all sorted automatically. For this setup, I'm using the Qwen 2.5 14b model. It's working well with the right prompt engineering. I'm putting together a YouTube video with a template, which should be up shortly. The whole system is set up to run in Docker on Windows using the Windows Subsystem for Linux. If you're interested, I posted a precursor video on YouTube yesterday showing how to get n8n running locally on Windows in preparation: https://lnkd.in/euZUiK9N 😉

How I built an AI email assistant that organizes, triages, and drafts replies. (without losing my brand voice). Last Friday, we ran a session on designing and building an AI agent for inbox management. Here’s what we covered: (and how you can follow the same steps): Step 1: Map your current process. Before you build anything, understand what you're already doing. → How do you currently handle email? → Where do things fall through the cracks? → What decisions do you make over and over? Most founders skip this step. But if you automate a broken system, you create chaos faster. Step 2: Fill out the Agent Canvas. We used our 5-part framework to map the full logic of the system: 1 - Triggers: What sets the process in motion? (e.g., new email, daily schedule) 2 - Decisions: What logic drives next steps? (e.g., is this urgent?) 3 - Actions: What should the agent do? (e.g., apply labels, draft reply) 4 - Tools: What platforms does it need? (e.g., Gmail, Slack, Claude) 5 - Guardrails: Where do humans stay in control? (e.g. drafts only, escalate via Slack) Step 3: Build your agent using natural language. Once the canvas was mapped, we used Lindy’s builder to create a real working agent (no code required). Example: → An assistant that runs 3x/day. → Checks for priority senders. → Applies labels. → Pulls answers from the knowledge base. → Drafts replies. → Pings Slack for anything urgent. No pre-built workflows. Just clear logic, explained in plain English. Step 4: Iterate. Most builds won’t work perfectly on the first try. That’s part of the process. We shared broken versions in the community, refined the templates, and got live feedback. The takeaway? You don’t need AI to answer everything. You need a system that understands how you triage, reply, and escalate. Then builds around that. And that’s exactly what we help founders do inside the Mighty AI Lab. Ready to build an AI email assistant? Join the Lab: https://lnkd.in/gjah4Yen