Reduced Error Detection Time with Custom AI Model

Amerit Fleet partnered with NineTwoThree to create a robust AI-powered error detection system for mechanic service documentation. In Phase 1, NineTwoThree built a machine learning model to predict errors in repair orders (ROs), reducing error detection time. In Phase 2, a real-time reasoning system was added to provide mechanics with actionable explanations, making it easier to resolve issues and prioritize error-prone records. This end-to-end solution streamlined error management and improved overall operational efficiency.

Concept

Amerit Fleet needed to automate the error-checking process in service tracking to cut down on manual reviews and prevent incorrect billing. NineTwoThree’s solution delivered an AI-based model that predicts the likelihood of errors and provides human-readable explanations, helping the quality team and mechanics resolve issues faster and with greater accuracy.

Challenge

The initial problem was a high volume of service orders with inconsistent data quality, making manual error detection tedious and time-consuming. The first model flagged high-risk records but lacked interpretability. In Phase 2, the challenge was to provide meaningful, detailed explanations that mechanics could understand and act on quickly.

Solution

NineTwoThree implemented a two-phase solution:

Impact

After testing, the machine learning model showed the potential to significantly improve operational efficiency and reduce costs by:

Maximizing

reduction in average error detection time

Reducing

manual review time by automatically identifying and prioritizing error-prone repair orders.

Optimizing

resource allocation by informing decisions that efficiently deploy them to address the most critical issues.

Improving

customer satisfaction by reducing errors and improving the overall quality of service.

While the exact impact may vary depending on specific implementation details and usage patterns, the report demonstrated the significant potential of this solution to drive operational improvements and cost savings for Amerit Fleet.