Real Autonomy. Real Impact.

Company A, which runs small and mid-sized franchise repair shops, wanted to offer a differentiated maintenance service to fleet operators with large numbers of vehicles. For fleet operators, minimizing downtime from breakdowns was critical, but under the conventional process, the cause of a fault and the parts needed could only be identified after the vehicle arrived at the shop.

Diagnosis, quote approval, parts sourcing and scheduling all took considerable time, prolonging vehicle downtime. Company A also faced a heavy inventory burden if it stocked parts in advance for every model and fault.

The customer wanted a system that would solve these problems and offer fleet operators fast, predictable maintenance.

Modapl used AI to analyze fault codes and driving data collected from vehicles, identifying warning signs and the parts likely to fail. Diagnostic results were linked to the repair shop's ERP system to generate expected maintenance items and a quote, and an approval request was sent to the fleet manager.

Once the manager approves, the system checks available shops and time slots and sends the booking details to the driver or fleet operator by SMS. When the booking is confirmed, it checks parts inventory and automatically orders any missing parts, so the shop is ready before the vehicle arrives.

Instead of starting diagnosis and parts preparation after a vehicle arrives, issues are now identified in advance and the shop is ready before the vehicle comes in. Fleet operators have shortened downtime, and the repair shop has reduced unnecessary parts inventory and customer-handling work.