Maple AI Consultants

AI consulting case studies by Joel & Nanz Inc.

Automotive • 14 Bays

Case Study 44: Auto Body Shop

Marketing-optimized case study for Canadian SMBs.

$116,000
Annual Savings
425%
ROI
3.0 months
Payback
Automotive • 14 Bays
Industry Focus

Executive Summary

Damage Assessment: Computer vision analyzes customer-submitted photos to identify damage, estimate repair complexity, and generate preliminary quotes. 88% accuracy vs. in-person estimates.

Challenge

Solution

  • Damage Assessment: Computer vision analyzes customer-submitted photos to identify damage, estimate repair complexity, and generate preliminary quotes. 88% accuracy vs. in-person estimates.
  • Parts Ordering: AI identifies required parts from damage photos, checks inventory, and auto-orders from suppliers with best pricing and availability.
  • Job Scheduling: Optimization algorithm schedules jobs considering part availability, technician skills, bay availability, and insurance approval timelines.

Technical Stack

Key Metrics

48
Quote turnaround reduced from 48 hours to 2 hours
72%
In-person estimates reduced 72% (saves estimator time)
84%
Parts ordering errors reduced 84%
68%
Bay utilization increased from 68% to 87%
2
Eliminated 2 FTE estimator roles ($78k savings)

Implementation Timeline

10 weeks including CV model training

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