RAG-Based Vehicle Diagnosis System
Technicians at a dealership network spent hours searching through thousands of pages of repair manuals and technical service bulletins to diagnose complex vehicle issues.
- 70%
- Faster Diagnosis
- 50k+
- Documents Indexed
- 96%
- Answer Relevance
- 4 mo
- To Delivery
The dealership network maintained over 50,000 pages of technical documentation — repair manuals, technical service bulletins, recall notices, and wiring diagrams across multiple vehicle makes and model years. Technicians often couldn't find the right information, leading to misdiagnoses and repeat visits.
They needed a RAG-powered diagnostic assistant built on AWS Bedrock that could ingest their entire technical knowledge base and let technicians ask natural-language questions to get accurate, source-cited answers instantly — grounded in their own documentation, not generic AI responses.
We built a RAG diagnostic system on AWS Bedrock. The pipeline ingests repair manuals, TSBs, and recall notices into a vector store on S3, with semantic chunking optimized for technical content. Technicians ask questions in plain language and receive answers with exact source citations, relevant diagrams, and step-by-step repair procedures.
Diagnosis time dropped 70%, the system indexes 50,000+ technical documents with 96% answer relevance, and technicians now resolve complex issues that previously required escalation to manufacturer support.
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