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AI-Powered Outreach Automation (n8n)
Built a workflow that turns outreach into a structured pipeline.
The system:
• Pulls verified leads from Supabase
• Uses AI to generate personalized emails
• Sends outreach via Gmail
• Monitors replies and analyzes sentiment
• Routes responses automatically (book meeting, review, or archive)
• Sends follow-ups if there’s no reply after 48 hours
Result: a fully automated pipeline that moves leads → conversations → booked meetings.
Designed for creators, agencies, and teams running outbound outreach at scale.
Nice workflow. Using AI to personalize outreach and track replies in one pipeline is really useful.
Thank you so much. Yes it is really helpful
Totally!!!
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Shopify Abandoned Cart Recovery Automation 🛒
Ever wondered how many potential orders an online store could miss when customers add products to their cart but leave without completing checkout?
I built an abandoned cart automation workflow using Activepieces, Shopify, Google Gemini, Gmail, and WhatsApp Business.
⚙️ How It Works
🛒 Cart Trigger: Detects an abandoned checkout event from Shopify.
🔀 Conditional Routing: Routes the workflow based on configured conditions.
🤖 AI-Powered Messaging: Gemini generates a personalized reminder message.
💬 WhatsApp Reminder: Sends a cart reminder through WhatsApp Business.
📧 Email Reminder: Sends a follow-up email to encourage the customer to complete checkout.
💡 How It Can Help E-commerce Businesses
Remind customers about products left in their carts.
Reduce repetitive manual follow-up work.
Make customer communication more consistent.
Create an opportunity to recover potentially lost sales.
Tools Used: Activepieces | Shopify | Google Gemini | Gmail | WhatsApp Business
Project Type: Personal Automation Project
I built this workflow to demonstrate how automation can support abandoned cart recovery for Shopify and D2C businesses.
Interested in automating your store's customer follow-ups? Let's connect.
The 30–60 minute delay then 24-hour follow-up is a sensible cadence. The part I would harden next is the stop conditions: once the cart converts or the customer replies, every channel should cancel, and WhatsApp especially needs a clear opt-in trail before the first send....
AI-Powered Facebook Messenger Follow-Up Automation | n8n
I built an AI-powered Facebook Messenger Follow-up Automation workflow using n8n to help businesses reconnect with potential customers and reduce missed sales opportunities.
When a customer asks about a product but stops replying, businesses often lose track of the conversation. This workflow helps automate timely, personalized follow-ups while keeping customer experience in mind.
What the Workflow Does
Automated Follow-ups: Sends follow-up messages at the right time.
AI-Powered Personalization: Uses AI to generate personalized messages based on customer conversations.
Order Verification: Checks order status before sending reminders.
Communication Scheduling: Avoids sending messages during late-night hours.
Sales Support: Helps businesses reconnect with interested buyers and reduce manual follow-up tasks.
Tools & Technologies
n8n | AI | Facebook Messenger | Workflow Automation | Order Verification
Project Goal
Demonstrate how AI-powered automation can improve customer communication, streamline sales follow-ups, and create more opportunities for businesses to convert interested buyers.
This is a demo workflow showcasing the potential of AI automation for eCommerce businesses.
Services
I help businesses build AI-powered workflows, sales automation systems, customer support automations, and custom n8n integrations.
#n8n #AIAutomation #FacebookMessenger #eCommerce #SalesAutomation #WorkflowAutomation #Contra
The late-night quiet hours are a nice touch. When a follow-up gets held for the morning, does the workflow re-check the order status at send time, or only when the reminder is first queued?
I’ve been working on a small skill called Manual Orchestrator to make longer projects with AI agents easier to manage.
The problem started with a familiar situation: one chat would accumulate plans, implementation details, corrections, and test results. By the time I was ready for the next phase, I wanted a fresh conversation without losing the decisions we’d already made.
Manual Orchestrator gives that process some structure. One chat acts as the orchestrator: it breaks the work into stages and writes a prompt for the next one. I run that prompt in a new session, then bring the result back. The orchestrator reviews it against the stage’s criteria before preparing the next prompt. If something needs fixing, I go back to the same worker session.
The skill also keeps a record in the project with the current stage, decisions, evidence, and next steps. That makes it easier to pause, resume, or hand the work to another session.
The handoff is manual. I choose when to open a session and carry the result back. The orchestrator and worker sessions can also use different agent CLIs, with the prompt adapted to the capabilities available.
It’s open source and still a work in progress. I’m using it to learn what helps in practice and where the workflow needs to improve. Installation has been checked for Codex, Claude Code, and Gemini CLI, but the full workflow hasn’t been tested across every CLI or version.
If you try it, I’d be interested in hearing what works and where it gets in your way.
