62% of business calls to small businesses go unanswered. I just shipped the fix.

Every missed call is a lead one tap away from calling your competitor instead. So I built an n8n system that closes that gap automatically:

⚡ The instant a call is missed, it looks the caller up in your CRM 🤖 AI drafts a personalized recovery SMS — not a generic "sorry we missed you" 🧠 If they reply, AI reads it and tells a hot lead from a cold one 📞 Hot leads get escalated straight to a callback task, assigned to your team, in seconds 📋 Every touchpoint logs to your CRM automatically — no spreadsheets, no gaps

16 nodes. Two entry points. Built on n8n + Twilio + JustCall + OpenAI, wired for Less Annoying CRM (or repoint the CRM steps at whatever you run — they're plain HTTP requests).

No monthly subscription. No vendor lock-in. Buy the template once, connect four credentials, and it's running on your own infrastructure — not another SaaS charging you forever for your own data.

Live on Contra now — $59, with setup notes built right into the canvas and a full README walking you through every credential.

📩 DM me if you want this customized for your exact stack.

#Automation #n8n #AIAutomation #Twilio #NoCode #LeadGen #SalesOps #Contra #Freelance

I recently built this workflow in n8n to automate the first stage of handling customer support requests.

The idea was to take an incoming ticket and let the system handle the repetitive work:

→ Receive the customer request through a webhook

→ Process and structure the incoming data

→ Use an AI Agent to understand the ticket

→ Classify it as Genuine, Spam, or Unclear

→ Route it based on the classification

→ Trigger the appropriate email action

→ Log the interaction in Google Sheets

I also added an Unclear path so that tickets the AI isn’t confident about don’t get blindly processed.

The goal isn’t to remove humans from customer support.

It’s to reduce the repetitive sorting and routing work, so the team can spend more time on the requests that actually need their attention.

Would you automate customer support differently? I’d love to hear how you’d approach it.

The Unclear lane is exactly the right guardrail. I’d add two production checks: redact PII before sending ticket text to the model, and measure false-positive/false-negative rates by category rather than a single accuracy score. Logging resolutions and human overrides back into...