Maple AI Consultants

AI consulting case studies by Joel & Nanz Inc.

Home Services • 32 Technicians

Case Study 21: HVAC Service Company

Marketing-optimized case study for Canadian SMBs.

$101,000
Annual Savings
435%
ROI
2.9 months
Payback
Home Services • 32 Technicians
Industry Focus

Executive Summary

Call Triage: AI phone system captures customer issue description, equipment details, and urgency. Routes to appropriate technician skill level and provides estimated arrival time.

Challenge

Solution

  • Call Triage: AI phone system captures customer issue description, equipment details, and urgency. Routes to appropriate technician skill level and provides estimated arrival time.
  • Route Optimization: Dynamic routing adjusts technician schedules in real-time based on traffic, job duration, and emergency calls. Maximizes daily service calls per technician.
  • Parts Inventory: ML predicts parts needed based on call description and equipment age. Pre-loads technician trucks with high-probability parts. Auto-orders replacement stock.

Technical Stack

Key Metrics

4.2
Average daily calls per technician increased from 4.2 to 6.1
76%
First-time fix rate improved from 76% to 89%
76%
Parts ordering errors reduced 76%
58%
Customer callback rate decreased 58%
2
Eliminated 2 FTE dispatcher roles ($64k savings)

Implementation Timeline

8 weeks including phone system integration and technician training

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