Title: Autonomous Real Estate Lead Dashboard (Full-Stack AI) Role: Full-Stack AI Engineer & Systems Architect Tools Used: React, Vercel, n8n, Python, Supabase, Google Gemini, REST APIs

Project Description:

The Problem: Real Estate agencies lose thousands of dollars because leads sit in inboxes or messy spreadsheets for hours before a human responds.

The Solution: I engineered an end-to-end, Autonomous Real Estate CRM that completely eliminates manual data entry and qualifies leads instantly.

How I Built It:

Backend & Logic: Architected a custom n8n workflow that catches incoming webhooks. I integrated the Google Gemini 1.5 Pro API to read the prospect's message, extract their budget, and score their intent (e.g., "High-Value Investor" or "Residential Buyer"). I wrote custom Python middleware to enforce strict JSON schemas to prevent AI hallucination.

Database Architecture: Migrated away from fragile spreadsheets by structuring a relational PostgreSQL database using Supabase to ensure secure, scalable state management.

Frontend UI & Deployment: Developed a sleek, dark-mode web application using React. To ensure enterprise-grade speed and reliability, the application is deployed on Vercel via continuous integration. The dashboard fetches live data from Supabase, providing business owners with real-time KPI metrics and lead visibility without refreshing.

The Impact: Reduced lead-processing time from hours to under 2 seconds, ensuring zero data leakage and providing a clean, enterprise-grade web application for the sales team.

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