Tata Motors Accelerates Vehicle Diagnostics with LLM-Based Chat Assistant
ServiceSage App Boosts Service Speed and Accuracy
With MATLAB, Tata Motors built a GenAI-based app that streamlines diagnostics and empowers even new technicians.
Key Outcomes
- Reduced vehicle service time by enabling faster AI-assisted troubleshooting using RAG-enhanced LLMs
- Improved diagnostic accuracy and consistency across technicians, regardless of experience level
- Accelerated development of the prototype by leveraging local LLMs and using MATLAB low-code tools and App Designer
Tata Motors’ service teams support a growing number of increasingly complex vehicle systems, making diagnostic accuracy and speed critical to maintaining customer satisfaction. However, technicians often struggle with navigating lengthy manuals, identifying root causes, and keeping up with evolving diagnostic procedures. These challenges lead to increased service time, inconsistent troubleshooting, and longer vehicle downtime.
To address these issues, Tata Motors developed ServiceSage, an app utilizing generative AI and large language models (LLMs). Using MATLAB® and Text Analytics Toolbox™, the team implemented a retrieval-augmented generation (RAG) workflow that enables the app to search technical manuals and deliver troubleshooting guidance in response to technician queries. The context-aware troubleshooting solution was built using App Designer and a combination of low-code preprocessing and scripting tools. ServiceSage improves diagnostic accuracy, reduces repair times, and elevates the overall service experience for both technicians and vehicle owners.
