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Built an AI Voice Agent for Real Estate Lead Qualification š”š
Designed and deployed an end-to-end AI voice agent that handles inbound property inquiries ā qualifies leads conversationally, checks calendar availability, and books site visits, no human agent required for the first touchpoint.
How it works:
šļø Retell AI powers natural conversation ā understands property type, budget, location, and timeline preferences just by talking
š n8n orchestrates everything behind the scenes ā routes each call by outcome (Booked / Warm Follow-up / Not a Fit / Incomplete)
š
Auto-books confirmed site visits directly to Google Calendar
š Logs every lead to Google Sheets for tracking
š Sends real-time Telegram alerts to the sales team the moment a lead is qualified
The result: faster response times, zero missed leads outside business hours, and a sales team that only spends time on calls that are actually ready to convert.
Built end-to-end ā call flow design, calendar/CRM integration, and automated follow-up logic.
#AIVoiceAgents #n8n #WorkflowAutomation #RealEstateTech #AIAutomation
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ā¢ Oct 9
**Project title**
AI Real Estate Lead Qualification System
Short description:
An AI-powered workflow concept that captures property enquiries, qualifies potential buyers, organizes lead details, and notifies real estate sales teams.
The problem:
Real estate agents spend time collecting buyer requirements and organizing incoming enquiries manually.
My solution:
I designed a workflow connecting an AI assistant with lead qualification, CRM or Google Sheets storage, and sales notifications.
Workflow:
Capture enquiries from a website or WhatsApp.
Ask about budget, location, property type, and investment goals.
Structure and qualify the responses.
Store lead details in a CRM or Google Sheets.
Notify the sales team for follow-up.
Project status: Concept / prototype.
Tools planned: OpenAI API, n8n, Google Sheets/CRM, and website or WhatsApp integrations.
What caught my eye is pulling budget, location and investment goals directly from a WhatsApp chat, which should cut a lot of the manual follow up.
š 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:
ā
Scores leads as HOT/WARM/COLD automatically
ā
Sends personalized follow-ups within 60 seconds
ā
Logs everything to Google Sheets/CRM
ā
Alerts reps only for serious buyers
ā
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 ā 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.
Title: AI Powered Telegram Chatbot
Description: A fully functional Telegram bot powered by Groq AI.
Handles conversations, remembers context and logs all messages
to a Supabase database. Deployed on Railway.
Link: https://t.me/WallexNexus_bot
How do you scope the Supabase conversation memory: per user, per chat, or both? I'd keep private-chat history out of group replies if the bot supports group conversations.