Case Study: AI-Assisted Mortgage Qualification System for Loan Officers at Edge Home Finance

Client

Independent Loan Officers (Edge Home Finance)

This project was implemented for multiple independent loan officers operating under Edge Home Finance, along with affiliated real estate agents using the same system on their websites.

Loan officers operating under Edge Home Finance rely on inbound interest from websites and agent referrals. Like many mortgage professionals, they face a common challenge:

• Website traffic existed, but engagement was low

• Prospects were hesitant to reach out directly

• Lead data was inconsistent and difficult to reuse

• Qualification conversations required manual follow-up

They needed a low-friction way to build trust, qualify prospects, and capture structured data — without increasing manual workload.

Context

The Challenge

The loan officers needed a system that could:

• Help prospects self-qualify without pressure

• Build trust before human contact

• Capture structured, reusable lead data

• Store all information directly inside the CRM

• Scale across multiple loan officers and real estate agents

• Operate securely without introducing operational risk

Manual follow-up and disconnected tools were limiting consistency and growth.

The Solution

WebQuench designed an AI-assisted mortgage qualification system that could be embedded directly on loan officer and real estate agent websites.

The system:

• Asks structured qualification questions

• Generates a personalized qualification or refinance report

• Sends that report to the prospect automatically

• Stores all responses and insights directly in the CRM

• Allows future retargeting and follow-up campaigns

• Runs securely behind Cloudflare infrastructure

The system is designed to support human decision-making, not replace it.

Implementation

The system was deployed individually for:

• Multiple loan officers

• At least 7–10 affiliated real estate agent websites

Each deployment used the same underlying automation framework with:

• Secure hosting

• Separate CRM instances

• Consistent data structure

• Minimal setup time per site

This allowed the system to scale without shared data, broken workflows, or manual overhead.

Results (Early Signals)

Without any paid advertising:

• One loan officer captured 34 qualified leads in the first month

• All leads were automatically stored in the CRM

• Each lead included structured qualification data

• No manual data entry or follow-up setup was required

This averaged approximately one new lead per day from organic website traffic alone.

While revenue attribution requires advertising and longer tracking windows, the system proved reliable in capturing intent, building trust, and preserving data for future use.

Why This System Scales

This system works because it:

• Reduces friction for prospects

• Builds trust before human interaction

• Captures clean, structured data from day one

• Integrates directly with existing CRMs

• Can be deployed consistently across independent operators

As more loan officers and agents adopt the system, the same infrastructure supports growth without added complexity.

Where This Fits in Our Work

This case study reflects our approach to:

• Sales Automation Systems

• Business Process & Operations Automation

• AI-assisted systems with guardrails and ownership

It demonstrates how automation can support revenue without sacrificing reliability, data integrity, or trust.

Next Step

If your team relies on manual qualification, fragmented lead capture, or inconsistent follow-up, a systems diagnostic is usually the fastest place to start.