AI-Powered Real Estate Lead Qualification System

Built an AI-powered lead qualification system that automatically captures real estate buyer inquiries, extracts key buyer information, assigns a qualification score from 0–100, routes qualified leads to the sales team, and records every inquiry in Airtable.

The system replaces manual lead review with an event-driven qualification and routing process.

The client needed to process a growing volume of buyer inquiries submitted through web forms.

Without automated qualification:

Sales staff had to manually review every inquiry.

Low-quality leads consumed valuable sales time.

High-intent buyers could be delayed during manual review.

Lead information was not consistently structured.

There was no centralized system for tracking qualification status and follow-up.

The goal was to automatically identify qualified buyers while keeping every submitted lead available for reporting and follow-up.

I designed an n8n workflow combining webhooks, OpenAI, Groq, Gmail, Airtable, and JavaScript.

The workflow uses AI to transform unstructured buyer inquiries into structured lead data and apply a consistent qualification model.

A webhook captures each incoming buyer inquiry in real time.

Required fields such as name, email, budget, and location are validated.

OpenAI extracts structured buyer information and generates a qualification score from 0–100.

Groq provides a fallback AI provider when required.

Leads scoring 80 or higher are classified as Qualified.

Qualified leads trigger a formatted Gmail notification for the sales team.

Every lead is stored in Airtable regardless of qualification status.

The qualification score considers several buyer signals:

Budget alignment

Preferred location

Purchase timeline

Property type relevance

This converts free-form inquiry data into a consistent qualification framework that can be adjusted as the business changes.

The workflow was designed so that an AI extraction failure does not cause the lead to disappear.

Fallback handling allows incomplete records to still be stored in Airtable while the error is logged for later review.

This ensures that every inquiry remains traceable even when an individual automation step fails.

Sales receives a structured Gmail notification containing:

Buyer name

Email

Budget

Location

Qualification score

Airtable record

Airtable maintains a centralized record of every inquiry with:

Contact details

Budget

Location

Timeline

Property type

AI summary

Qualification score

Lead status

Timestamp

Saved 6+ hours per week of manual lead review

Reduced qualified-lead response time by approximately 60%

Achieved 95% qualification accuracy against human decisions

Created a centralized lead tracking system

Enabled real-time routing of qualified inquiries

Demonstrated 2× higher conversion among qualified leads compared with the previous process

The workflow processes approximately 100–300 leads per month in real time and provides a foundation that can be extended with CRM synchronization, automated follow-ups, lead nurturing, and additional qualification rules.