The solution we built
The implementation centred on a small number of high-leverage automations rather than a sprawling platform. For the auto body shop, the core pieces were as follows.
- 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.
- Parts Receiving and Stock Control: MapleInventory receives every parts order into a receiving queue fed by purchases logged in MapleExpense. Maple-AI sorts each line into job parts, shop tools and equipment, and freight/duty, and suggests a rack, shelf and bin so parts are found when the bay is ready. Low-stock alerts cover the consumables the shop uses every day.