---
title: "3x lead-to-booking conversion — HVAC Case Study | AI My Business Today"
description: "How one HVAC company 3x'd lead-to-booking conversion with AI text-back. 47 new after-hours bookings/month and 28-second response time."
lang: en
json-ld:
---

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HVAC

# 3x lead-to-booking conversion

Reliant HVAC (residential, 6 trucks)

3x

Lead conversion lift

47

After-hours bookings/mo

28 sec

Avg response time

112

Calls recovered/mo

## The context

Reliant was missing roughly a third of inbound calls — most after 5pm and on weekends. Those leads went straight to a competitor.

## What was broken

-   /\* PLACEHOLDER \*/ ~30% of inbound calls missed 
-   No after-hours capture 
-   Voicemail-to-booking rate was effectively 0% 

## What we shipped

Deployed AI missed-call text-back that engaged callers within 30 seconds, qualified the issue, and offered a same-week slot pulled from the live dispatch board.

"I used to lose every weekend lead. Now they're already booked when I get to the office Monday."

— /\* PLACEHOLDER \*/ Owner, Reliant HVAC

## How They Did It — The Implementation Detail

### The Starting Problem

Reliant HVAC was running roughly 35 inbound calls per day with a 30% miss rate concentrated in after-hours and weekend windows. Their on-call rotation existed but lagged 15–45 minutes behind the initial call — long enough for most callers to have already booked a competitor.

### The System They Deployed

A 3-component AI stack: (1) instant SMS text-back to any missed call within 5 seconds; (2) AI triage to determine emergency vs. scheduled service and route accordingly; (3) auto-booking for scheduled requests using their existing Housecall Pro calendar. Total setup time: 11 days. Monthly cost: under $500.

### The 90-Day Results

Within 90 days, lead-to-booking conversion 3x'd. After-hours bookings jumped from 4/month to 47/month. Average response time dropped from 22 minutes to 28 seconds. Revenue attributable to recovered calls: $84K in the first quarter. The on-call tech only got paged for true emergencies — a 70% reduction in night-call disruption.

### What Made It Work

The biggest insight wasn't the technology — it was matching the AI's tone to Reliant's existing brand voice. Generic AI greetings drove 30% lower engagement than messages that sounded like Reliant's actual dispatcher. Personality matters, even in automated systems.

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