top of page

Identifying the Issues

Reducing Hours of Diagnosis to Mere Seconds ---->

Copy of Logo_1_Logo_TnsprtBckgd_edited_edited.jpg

Traditional Diagnostics

  1. Initial Diagnosis: Technicians connect a scanner to the vehicle's diagnostic port to read Diagnostic Trouble Codes (DTCs). This is a standardized process across various vehicles.

  2. Manual Analysis: The technician manually interprets the DTCs, often consulting repair manuals or databases.

  3. Experience-Based Insights: The diagnosis and subsequent repair plan largely depend on the technician's experience and expertise.

  4. Time-Consuming: The process of diagnosing, especially for complex problems, can be time-consuming, as it may require multiple test drives, manual testing of individual components, and referencing past cases or technical forums.

  5. Error Prone: Due to reliance on manual interpretation and experience, there's a higher risk of misdiagnosis, especially in complex or rare cases.

  6. Limited Predictive Maintenance: Traditional methods primarily focus on current issues rather than predictive analysis, potentially missing early signs of future problems.

AI-Driven Diagnostics:

  1. Initial Connection: Similar to traditional methods, the process begins with connecting a scanner to the vehicle's diagnostic port.

  2. AI-Enhanced Data Interpretation: The AI tool automatically references vehicle manufacturer documents, interprets DTCs, and other vehicle data (like VIN, mileage, vehicle profile) to provide a more comprehensive diagnosis.

  3. Generative Diagnostic Prediction: Utilizing a generative AI model, the system predicts potential error conditions beyond the basic DTC analysis.

  4. Efficient and Comprehensive Analysis: AI logic analyzes not just DTCs, but also live data, historical vehicle data, and information from community forums to identify probable causes and suggest repairs quickly.

  5. Interaction with Technicians: The AI tool can interact with technicians, providing them with detailed analysis, part information, and repair steps.

  6. Predictive Maintenance Insights: The AI tool can analyze historical data and patterns for similar vehicles, advising on potential future issues and maintenance needs.

  7. Learning and Adaptation: The AI system continuously learns from each diagnostic session, improving its accuracy and efficiency over time.

©2024 by OBD-AI

bottom of page