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Built an AI-powered lead qualification and follow-up automation using Make.com.
The system automatically captures incoming leads, analyzes their information, assigns priority levels, and triggers personalized follow-up workflows.
How it works:
Lead Capture: Collects new inquiries through Tally forms.
AI Qualification: An AI agent evaluates each lead and generates a structured qualification scorecard.
Lead Segmentation: Automatically categorizes leads into Priority A, B, or C based on their qualification.
Instant Notifications: Sends internal email alerts based on each lead's priority.
Automated Follow-Ups: Creates priority-specific follow-up sequences to keep leads engaged.
Lead Tracking: Records categorized leads in Google Sheets for organized tracking and management.
What this system helps businesses achieve:
Reduce time spent manually qualifying leads.
Identify high-priority prospects faster.
Organize incoming inquiries automatically.
Standardize lead evaluation.
Maintain consistent follow-ups.
Tools: Make.com, Tally, AI Agent, Google Sheets, Email Automation.
A complete lead qualification workflow designed to turn incoming inquiries into organized, actionable sales opportunities.
Using Google Sheets for lead tracking is a practical choice for small teams, but it often becomes a bottleneck when sales reps need to sync with a CRM later. I usually see this setup hit a wall once the volume exceeds what a simple grid can handle.
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This week, I have been working on automating my boss's inbox management, delegating email triage to AI while I handle reviews and approvals that require sound judgment. Because one wrong response to a client can cost a business revenue, I wanted to build a system that saves time without handing over critical decisions to AI.
How I built it:
I created two connected automation scenarios in Make.com.
Scenario 1: Intelligent Inbox Triage:
AI analyzes incoming emails, categorizes them by priority, summarizes their content, and recommends the next action. This makes it easier to identify urgent messages and decide what needs attention first.
Scenario 2: Human Review & Approval:
The workflow supports the review and approval process before an email is sent. Slack notifications help bring relevant updates to my attention, so I can review the proposed action and approve the response before it goes out.
Below are snippets of the two scenarios, including a Slack notification, showing how the workflow operates.
My takeaway? AI can help manage the inbox, but when a response could affect revenue or a client relationship, the final decision should remain human.
I'd test the triage with urgent emails that sound casual, such as 'I still can't access the account.' Do you review a sample of low-priority messages to catch urgent ones the AI missed?
🚀 Just Built an AI Lead Nurturing Agent for B2B Sales Teams!
The problem: Most B2B companies lose leads because follow-ups are slow. By the time a rep reaches out, the lead has already talked to competitors.
The solution: An AI agent that handles the entire lead nurturing workflow:
✅ Scores leads as HOT/WARM/COLD automatically
✅ Sends personalized follow-ups within 60 seconds
✅ Logs everything to Google Sheets/CRM
✅ Alerts reps only for serious buyers
✅ Books meetings directly into calendars
Tech Stack: n8n | OpenAI | Google Sheets | Gmail | Telegram
Result: 60% higher conversion rate, 15+ hours/week saved per rep.
This is like having an AI SDR that never sleeps — for $300/month.
Want this for your sales team? Let's talk.
#AIAutomation #LeadNurturing #B2BSales #SalesOps #n8n #PropTech
The bit about auto booking meetings into calendars looks like it will eliminate a lot of scheduling back and forth.
Automated Review Requests for Local Service Businesses
The Problem
Google reviews help local businesses get found, and every HVAC company, salon, cleaner and contractor knows it. Most of them ask for reviews inconsistently. The reason isn’t that they don’t care. Asking every customer, right after every job, depends on someone remembering at the exact moment the business is busiest. Happy customers never get asked, and the review count grows slower than it should.
The Solution
I built an n8n system that sends the review request automatically, once, at the right time, and tracks what happens next. Nobody gets asked twice and nobody gets forgotten.
How the System Works
1. Checks every 15 minutes. The workflow reads a Google Sheet that lists each job and what has already been sent. The sheet is the system’s memory, so nothing depends on timers that can fire twice.
2. Sends the request. Two hours after a job is marked complete, the customer gets an email with a review link.
3. Tracks the click. The link goes through a tracking step that records “clicked” in the sheet, then sends the customer to Google.
4. Sends one follow-up. If there’s no click after three days, one reminder goes out. Customers who clicked or opted out are skipped.
5. Alerts me on errors. A separate error workflow emails me if any step fails.
Why this design: every step re-checks the sheet before sending, so the system can’t double-send and a customer who already responded is never contacted again.
Tools: n8n, Gmail, Google Sheets
I'd test the tracking link with email security scanners before using clicks to suppress reminders. How do you distinguish an automated link check from a customer opening the review page?