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❄️ HVAC & LIGHT-COMMERCIAL · MULTI-TRUCK FLEET

HVAC Contractor — 94% Contact Rate, ~14× Projected ROI

Engagement: 48-hr deploy + 14-day pilot · Industry: HVAC / Service Trade · 6 service trucks, 11 team members
94%
Contact rate (was 57%)
50 calls
Pilot phase
14×
Projected ROI
48 hrs
To deploy

The situation

A regional HVAC contractor with 6 service trucks and 11 total team members had a chronic missed-call problem. During business hours, when all 6 trucks were on calls, the office line went to voicemail. After hours and weekends — about 30% of total weekly call volume — every call routed straight to voicemail. Average call-back time was around 4 hours, by which point a substantial share of those callers had already hired a competitor.

Pre-pilot data showed: roughly 40% of business-hours calls unanswered, ~100% of after-hours calls missed. Estimated lost revenue from missed-and-not-recovered inbound: $8,000–$15,000 per month based on average HVAC service-call value.

What we did

1. Day 1 — Kickoff & voice agent training

30-min Zoom kickoff to align on: top 14 call types (no-cool, no-heat, maintenance, estimates, parts, billing, etc.), business hours, escalation rules, AI disclosure phrasing, and tone (warm, slightly conversational, never robotic). Voice agent trained on the client's FAQ document + a sample of past customer interactions.

2. Day 2 — Deploy + 48-hour go-live

Call forwarding configured so the AI agent answered when the team didn't pick up within 3 rings. Missed-call text-back wired to fire SMS within 60 seconds of any missed call. Web-form Speed-to-Lead: any form fill on the client's site triggered an AI callback within 60 seconds. All inbound calls + leads land in the client's existing CRM via API integration. AI disclosure scripted per CA AB 2013 compliance at the start of every AI-answered call.

3. Day 3-14 — 14-day pilot phase + daily script tuning

Listened to a sample of recorded calls every morning. Logged any agent missteps. Pushed script updates daily based on what the data actually showed (3 major updates during the pilot — see “what we learned” below).

4. CRM integration + call recording / transcription

Every call recorded, transcribed, and searchable in the client's dashboard. Every captured lead lands in CRM with full transcript + recording link + sentiment score + AI-suggested next-action.

The results

14-day pilot phase, 50 inbound calls. Breakdown:

OutcomeCount% of pilot
Answered live by AI agent → captured contact info2346%
Answered live → appointment booked into calendar1530%
Missed call → recovered via SMS text-back <60 sec918%
Customer escalation to human (handled correctly)24%
Agent error / script update logged12%
TOTAL CONTACT (live + recovered)47 / 5094%

ROI math, annualized against pre-pilot baseline of ~57% contact rate:

What we learned

Three script updates the pilot data forced us to make — none of which would have been obvious without 14 days of real call recordings:

  1. Day 4 — Same-day service over-promising. The agent was confirming “we can have a tech out today” during peak heat when dispatch was already overbooked. Updated script to check dispatcher availability before quoting timing.
  2. Day 7 — “What's wrong with your AC?” was confusing customers. Most people don't know how to describe HVAC failure modes. Replaced with a triage tree: “Is it running but not cooling, not running at all, or making a noise?” Conversion improved measurably.
  3. Day 11 — Financing questions were being escalated. Three callers asked about financing during week 2. Added the client's financing partner details directly into the agent's knowledge base so it could answer in-line instead of escalating.

The pilot phase is the entire reason this works. A static voice agent shipped on day 1 will sound robotic and miss real customer language patterns. 14 days of real calls + daily tuning is what turns a generic agent into one that sounds like it works at the company. We won't ship a voice agent engagement without it.

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