40% Less Driving, Same Number of Jobs
Smart scheduling that actually works
Guy runs a mobile vehicle inspection service across Greater Sydney. Before this system, he was zigzagging across the city, spending 3+ hours just driving between jobs. Now the AI clusters appointments geographically and validates travel times in real-time.
AI Smart Scheduling
Pink Slips NSW Case Study
40% less driving, same number of jobs
Pink Slips NSW Series
The Problem: Random Booking Chaos
The old booking process was reactive. Customer calls, staff checks the calendar, guesses at a time. No consideration of where the technician will be before or after. The result? Days that looked like this:
Monday Before Optimization
AI clusters jobs geographically in real-time
The Solution: AI That Thinks About Geography
We built a scheduling engine that checks every potential time slot against real travel times. Before accepting a booking, it calculates: How long to get there from the previous job? How long to get to the next job? Both must be under 35 minutes or the slot gets rejected.
Load Context
Pull all existing calendar events for the next 14 days, plus the customer's address and availability preferences.
Check Each Slot
For every potential time: skip weekends, skip full days (8+ jobs), skip conflicts, then calculate travel times via Google Maps API.
Validate or Fallback
If travel is under 35 mins both ways, book it. If no slot fits, find the closest existing job and schedule nearby for clustering.
Before vs After
Random bookings, zigzag routes, 3+ hours driving
Geographic clusters, 10-15 min between jobs
Scheduling Flow
How It Works
- 1
Load calendar + customer address
- 2
Calculate travel FROM previous job
- 3
Calculate travel TO next job
- 4
Both under 35 mins? Accept slot
Real Test: Natasha from Woronora
Woronora is isolated in the Sutherland Shire. Most of the week's jobs were in the northwest—over an hour away. The old system would have just picked the first available slot. Here's what the smart system did instead:
Customer Location
Woronora, NSW (Sutherland Shire)
The Challenge
- From Wahroonga (Tue): 1 hr 4 mins
- From North Ryde (Tue): 45 mins
System Decision
Found Beata at 10 AM Thursday in Gymea (also Sutherland Shire)
Travel Woronora to Gymea: 10 minutes
Generated Response:
"Hi Natasha, we can schedule you in next at Thu 4th at 9:00 AM"
From optimized geographic clustering
Thursday After Optimization
The system doesn't find available slots - it finds EFFICIENT slots. Every booking validated against real travel times.
— Smart Scheduling Logic
Business Impact
Results After Implementation
API Costs Under Control
Tech Stack
Tech Stack
- 1
Google Distance Matrix API
- 2
PostgreSQL + Drizzle ORM
- 3
React + TypeScript
- 4
Twilio SMS Integration
What's Next
Current System
- Single technician support
- Human confirms each booking
- Real-time travel calculation
- Geographic clustering
- Customer availability respect
Future Roadmap
- Multi-technician routing
- Fully automated booking
- Predictive clustering
- Route caching
- Customer area suggestions
Build Smart Systems
AI scheduling that pays for itself in week one
Explore More Pink Slips NSW
This is part of our ongoing case study series documenting the Pink Slips NSW project.
More From This Series:
- Multi-Technician Calendar - Scaling to 6 regions
- Smart Availability Picker - UX iteration story
- The Excellent Score Trap - Google Ads lessons
Related Services:
- Web Development - Custom applications
- Google Ads - Performance marketing
- SEO Services - Organic growth
The Bottom Line
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AI Smart Scheduling
Pink Slips NSW Case Study
40% less driving, same number of jobs
AI clusters jobs geographically in real-time
Before vs After
Random bookings, zigzag routes, 3+ hours driving
Geographic clusters, 10-15 min between jobs
How It Works
- 1
Load calendar + customer address
- 2
Calculate travel FROM previous job
- 3
Calculate travel TO next job
- 4
Both under 35 mins? Accept slot
From optimized geographic clustering
The system doesn't find available slots - it finds EFFICIENT slots. Every booking validated against real travel times.
— Smart Scheduling Logic
Tech Stack
- 1
Google Distance Matrix API
- 2
PostgreSQL + Drizzle ORM
- 3
React + TypeScript
- 4
Twilio SMS Integration
Build Smart Systems
AI scheduling that pays for itself in week one
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