AUTOMATING REI LEAD QUALIFICATION WITH AN AI VOICE AGENT

Designed an AI-powered phone agent that qualifies inbound leads in real time — reducing manual effort and filtering out ~80% of low-quality inquiries.

Year

2025

Field

Real Estate

Client :

Pebblerei CRM

Category

Feature

Featured Project Cover Image

PROBLEM

The team relied on manual calls to qualify inbound leads, which was time-consuming and inconsistent.

Designed an AI voice agent integrated with Retell AI that:

  • conducts real-time conversations with leads

  • captures structured data during calls

  • routes qualified leads to the team



KEY CONVERSATION DESIGN DECISIONS

Designing the AI agent required balancing natural conversation with the business need to collect structured qualification data. The challenge was not only defining what the AI should ask, but also how it should handle uncertainty, build trust, and guide users toward meaningful outcomes.

Challenge

Design Decision

Unpredictable responses

Introduced fallback paths and clarification prompts

Low user trust

Clear AI introduction and transparent messaging

Missing qualification data

Progressive follow-up questions

Ambiguous answers

Confidence scoring and routing logic


AGENT TESTING AND QUALIFICATION FLOW

UX flow defining how users configure, test, validate, and review AI-generated lead qualification results before deploying the agent.


Step 1 - Configure the agents test prompt


Step 2- Agent Connects

Step 3 - Start AI conversation and generates responses


Step 4 -
Review qualification results -> Refine prompt & repeat

PRODUCT OUTCOMES


Challenge

Product Outcome

Manual qualification

Automated AI qualification

Inconsistent notes

Structured CRM data

Sales reviewed every lead

AI filtered ~80% of non-relevant leads

Time-consuming handoff

Faster routing to Sales

Difficult to scale

Reusable AI qualification framework

WHAT I DID

Designed an AI voice agent integrated with Retell AI that:

  • conducts real-time conversations with leads

  • captures structured data during calls

  • routes qualified leads to the team



KEY CONVERSATION DESIGN DECISIONS

Designing the AI agent required balancing natural conversation with the business need to collect structured qualification data. The challenge was not only defining what the AI should ask, but also how it should handle uncertainty, build trust, and guide users toward meaningful outcomes.

Challenge

Design Decision

Unpredictable responses

Introduced fallback paths and clarification prompts

Low user trust

Clear AI introduction and transparent messaging

Missing qualification data

Progressive follow-up questions

Ambiguous answers

Confidence scoring and routing logic


AGENT TESTING AND QUALIFICATION FLOW

UX flow defining how users configure, test, validate, and review AI-generated lead qualification results before deploying the agent.


Step 1 - Configure the agents test prompt


Step 2- Agent Connects

Step 3 - Start AI conversation and generates responses


Step 4 -
Review qualification results -> Refine prompt & repeat

PRODUCT OUTCOMES


Challenge

Product Outcome

Manual qualification

Automated AI qualification

Inconsistent notes

Structured CRM data

Sales reviewed every lead

AI filtered ~80% of non-relevant leads

Time-consuming handoff

Faster routing to Sales

Difficult to scale

Reusable AI qualification framework

More Projects

More Projects

AUTOMATING REI LEAD QUALIFICATION WITH AN AI VOICE AGENT

Designed an AI-powered phone agent that qualifies inbound leads in real time — reducing manual effort and filtering out ~80% of low-quality inquiries.

Year

2025

Field

Real Estate

Client :

Pebblerei CRM

Category

Feature

Featured Project Cover Image

PROBLEM

The team relied on manual calls to qualify inbound leads, which was time-consuming and inconsistent.

Designed an AI voice agent integrated with Retell AI that:

  • conducts real-time conversations with leads

  • captures structured data during calls

  • routes qualified leads to the team



KEY CONVERSATION DESIGN DECISIONS

Designing the AI agent required balancing natural conversation with the business need to collect structured qualification data. The challenge was not only defining what the AI should ask, but also how it should handle uncertainty, build trust, and guide users toward meaningful outcomes.

Challenge

Design Decision

Unpredictable responses

Introduced fallback paths and clarification prompts

Low user trust

Clear AI introduction and transparent messaging

Missing qualification data

Progressive follow-up questions

Ambiguous answers

Confidence scoring and routing logic


AGENT TESTING AND QUALIFICATION FLOW

UX flow defining how users configure, test, validate, and review AI-generated lead qualification results before deploying the agent.


Step 1 - Configure the agents test prompt


Step 2- Agent Connects

Step 3 - Start AI conversation and generates responses


Step 4 -
Review qualification results -> Refine prompt & repeat

PRODUCT OUTCOMES


Challenge

Product Outcome

Manual qualification

Automated AI qualification

Inconsistent notes

Structured CRM data

Sales reviewed every lead

AI filtered ~80% of non-relevant leads

Time-consuming handoff

Faster routing to Sales

Difficult to scale

Reusable AI qualification framework

WHAT I DID

Designed an AI voice agent integrated with Retell AI that:

  • conducts real-time conversations with leads

  • captures structured data during calls

  • routes qualified leads to the team



KEY CONVERSATION DESIGN DECISIONS

Designing the AI agent required balancing natural conversation with the business need to collect structured qualification data. The challenge was not only defining what the AI should ask, but also how it should handle uncertainty, build trust, and guide users toward meaningful outcomes.

Challenge

Design Decision

Unpredictable responses

Introduced fallback paths and clarification prompts

Low user trust

Clear AI introduction and transparent messaging

Missing qualification data

Progressive follow-up questions

Ambiguous answers

Confidence scoring and routing logic


AGENT TESTING AND QUALIFICATION FLOW

UX flow defining how users configure, test, validate, and review AI-generated lead qualification results before deploying the agent.


Step 1 - Configure the agents test prompt


Step 2- Agent Connects

Step 3 - Start AI conversation and generates responses


Step 4 -
Review qualification results -> Refine prompt & repeat

PRODUCT OUTCOMES


Challenge

Product Outcome

Manual qualification

Automated AI qualification

Inconsistent notes

Structured CRM data

Sales reviewed every lead

AI filtered ~80% of non-relevant leads

Time-consuming handoff

Faster routing to Sales

Difficult to scale

Reusable AI qualification framework

More Projects

More Projects

AUTOMATING REI LEAD QUALIFICATION WITH AN AI VOICE AGENT

Designed an AI-powered phone agent that qualifies inbound leads in real time — reducing manual effort and filtering out ~80% of low-quality inquiries.

Year

2025

Field

Real Estate

Client :

Pebblerei CRM

Category

Feature

Featured Project Cover Image

PROBLEM

The team relied on manual calls to qualify inbound leads, which was time-consuming and inconsistent.

Designed an AI voice agent integrated with Retell AI that:

  • conducts real-time conversations with leads

  • captures structured data during calls

  • routes qualified leads to the team



KEY CONVERSATION DESIGN DECISIONS

Designing the AI agent required balancing natural conversation with the business need to collect structured qualification data. The challenge was not only defining what the AI should ask, but also how it should handle uncertainty, build trust, and guide users toward meaningful outcomes.

Challenge

Design Decision

Unpredictable responses

Introduced fallback paths and clarification prompts

Low user trust

Clear AI introduction and transparent messaging

Missing qualification data

Progressive follow-up questions

Ambiguous answers

Confidence scoring and routing logic


AGENT TESTING AND QUALIFICATION FLOW

UX flow defining how users configure, test, validate, and review AI-generated lead qualification results before deploying the agent.


Step 1 - Configure the agents test prompt


Step 2- Agent Connects

Step 3 - Start AI conversation and generates responses


Step 4 -
Review qualification results -> Refine prompt & repeat

PRODUCT OUTCOMES


Challenge

Product Outcome

Manual qualification

Automated AI qualification

Inconsistent notes

Structured CRM data

Sales reviewed every lead

AI filtered ~80% of non-relevant leads

Time-consuming handoff

Faster routing to Sales

Difficult to scale

Reusable AI qualification framework

WHAT I DID

Designed an AI voice agent integrated with Retell AI that:

  • conducts real-time conversations with leads

  • captures structured data during calls

  • routes qualified leads to the team



KEY CONVERSATION DESIGN DECISIONS

Designing the AI agent required balancing natural conversation with the business need to collect structured qualification data. The challenge was not only defining what the AI should ask, but also how it should handle uncertainty, build trust, and guide users toward meaningful outcomes.

Challenge

Design Decision

Unpredictable responses

Introduced fallback paths and clarification prompts

Low user trust

Clear AI introduction and transparent messaging

Missing qualification data

Progressive follow-up questions

Ambiguous answers

Confidence scoring and routing logic


AGENT TESTING AND QUALIFICATION FLOW

UX flow defining how users configure, test, validate, and review AI-generated lead qualification results before deploying the agent.


Step 1 - Configure the agents test prompt


Step 2- Agent Connects

Step 3 - Start AI conversation and generates responses


Step 4 -
Review qualification results -> Refine prompt & repeat

PRODUCT OUTCOMES


Challenge

Product Outcome

Manual qualification

Automated AI qualification

Inconsistent notes

Structured CRM data

Sales reviewed every lead

AI filtered ~80% of non-relevant leads

Time-consuming handoff

Faster routing to Sales

Difficult to scale

Reusable AI qualification framework

More Projects

More Projects