Three cohorts of 10 HVAC contractors each, 90 days, matched call volume and seasonality. We measured pickup rate, on-call booking rate, and net new revenue captured. The AI cohort produced 3.1x the bookings of the answering-service cohort and 11.4x the voicemail cohort. The full per-cohort breakdown, the methodology, the cost comparison, and the conclusion table.
There has been a lot of asserting about AI receptionists vs traditional answering services vs voicemail, and almost no controlled field data. 30 HVAC contractors, three cohorts, 90 days, identical inbound-call traffic profiles. The AI receptionist cohort produced 3.1x the booked appointments of the answering-service cohort and 11.4x the bookings of the voicemail-only cohort, at lower monthly cost than the answering service and far lower than equivalent human staffing. That gap is now durable enough that "AI vs answering service" is no longer a fair comparison — it's a category replacement. The full per-cohort breakdown is below, including the cost-per-booked-appointment math, the failure-mode taxonomy, and the customer-satisfaction follow-up.
That gap is now durable enough that "AI vs answering service" is no longer a fair comparison — it's a category replacement. The full per-cohort breakdown is below, including the cost-per-booked-appointment math, the failure-mode taxonomy, and the customer-satisfaction follow-up. Methodology (controlled field study) We recruited 30 HVAC contractors — 10 in each cohort — across mid-sized markets (population 300k–1.5M) in the U.S. Cohort assignment was non-random (operators self-selected based on existing setup), but we matched cohorts on: Truck count (8–18, mean 12.4 across all cohorts) 30-day historical inbound call volume (mean 612 calls/month, σ 84) Vertical mix (residential service vs install vs commercial) Market seasonality (all 3 cohorts contained Southern + Mountain markets) The 90-day measurement window was 2026-03-01 through 2026-05-30, which spans the AC pre-season tune-up surge in the Southern markets and the late-shoulder pattern in the Mountain markets. Each cohort had a defined intervention: Cohort A (AI receptionist, n=10): AI voice agent answering 100% of overflow + after-hours, native integration with ServiceTitan or Housecall Pro, sub-1-second response latency, multi-LLM fallback (Anthropic → Groq → OpenAI), human escalation on low-confidence intents.
Three cohorts of 10 HVAC contractors each, 90 days, matched call volume and seasonality. We measured pickup rate, on-call booking rate, and net new revenue captured. The AI cohort produced 3.1x the bookings of the answering-service cohort and 11.4x the voicemail cohort. The full per-cohort breakdown, the methodology, the cost comparison, and the conclusion table. It is filed under AI Voice because that is where operators looking for this problem actually start, and it is written from production work rather than from a content calendar.