Maple AI Consultants

AI consulting case studies by Joel & Nanz Inc.

Landscaping • 42 Crews

Case Study 28: Landscaping Company

Marketing-optimized case study for Canadian SMBs.

$127,000
Annual Savings
415%
ROI
3.2 months
Payback
Landscaping • 42 Crews
Industry Focus

Executive Summary

Route Optimization: AI plans daily routes considering job locations, crew assignments, traffic, and equipment needs. Dynamically adjusts for weather cancellations and add-on jobs.

Challenge

Solution

  • Route Optimization: AI plans daily routes considering job locations, crew assignments, traffic, and equipment needs. Dynamically adjusts for weather cancellations and add-on jobs.
  • Estimate Generation: Computer vision analyzes property photos to measure lawn area, count trees/shrubs, and identify landscaping features. Auto-generates quotes with 90% accuracy.
  • Weather-Based Scheduling: ML predicts weather impact on jobs and automatically reschedules affected appointments with customer notifications.

Technical Stack

Key Metrics

7.2
Daily stops per crew increased from 7.2 to 9.8
24%
Fuel costs reduced 24% through better routing ($42k savings)
2.3
Quote turnaround time reduced from 2.3 days to 15 minutes
28%
Quote-to-close rate improved from 28% to 41%
2
Eliminated 2 FTE estimator roles ($68k savings)

Implementation Timeline

9 weeks from kickoff to production

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