What challenges did FAW Group Import & Export Co., Ltd. face before AI implementation?

Dealers and technical experts had to manually review thousands of pages of maintenance documentation. Cross-region support required significant coordination between dealers and technical maintenance engineers. Completed repair cases were not being converted into reusable digital knowledge assets, slowing future diagnosis. Technical materials were difficult to understand and interact with, making troubleshooting slow.

What was the impact of the AI solution?

The repair cycle was reduced from 7 days to 3-4 days. Technical experts and dealers can query the knowledge base to pre-analyze faults and return repair plans faster. On-site technicians can prepare tools and parts in advance, shortening repair time. After repairs, the system automatically summarizes outcomes and stores them as reusable knowledge for future cases.

Source

Evidence: Medium50/100

FAW Hongqi Overseas remote maintenance service optimized with Amazon Bedrock (multimodal RAG)

Use case typeRAG infrastructureUpdated Jun 13, 2026

FAW Group Import & Export Co., Ltd. operates Hongqi Overseas’ remote maintenance support system for global vehicle service. The team needed to turn large volumes of maintenance manuals, work orders, images, and video into reusable knowledge assets so dealers and technical experts could resolve issues faster across regions. Using AWS generative AI services, Hongqi built a multimodal RAG-based knowledge system to support natural-language Q&A, cross-language understanding, source-attributed retrieval, and automated maintenance report generation for after-repair knowledge accumulation.

Organization: FAW Group Import & Export Co., Ltd.
Industry: Automotive
Location: China
Published: May 2026

Reported outcomes

3-4 days

quantified impactTime & speed

Strategic outcomes

New product / capabilityBuilt multimodal maintenance knowledge systemBetter decisions & insightEnabled faster fault pre-analysisSpeed & agilityShortened repair turnaround processNew product / capabilityCreated reusable after-repair knowledge

Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Quantified impact: 3-4 days decrease

AWS Solutions Case StudiesMay 13, 2026Case studyInferred claimMedium evidence strength

The repair cycle was reduced from 7 days to 3-4 days.

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: FAW Group Import & Export Co., Ltd., Hongqi Overseas
Provider: AWS
Maturity: Unknown
Linked source: AWS Solutions Case Studies

No explicit deployment-stage evidence found.

Customer identity supportedSource describes one deploymentMaturity evidence evaluated

Primary read

Use case focus

Showing 3 of 4

1Knowledge management
2Remote maintenance
3Generative AI assistance

Dealers and technical experts had to manually review thousands of pages of maintenance documentation.
Cross-region support required significant coordination between dealers and technical maintenance engineers.
Completed repair cases were not being converted into reusable digital knowledge assets, slowing future diagnosis.
Technical materials were difficult to understand and interact with, making troubleshooting slow.

Hongqi Overseas used Amazon Glue for ETL across sessions, logs, maintenance cases, and electronic manuals.
Amazon OpenSearch Service provided vector retrieval for RAG and fast relevance search across large document collections.
Amazon Neptune stored knowledge graphs derived from documents to capture relationships across maintenance cases, component parameters, and fault codes.
Amazon Bedrock powered the multimodal generative AI workflow, including Amazon Nova and Amazon Nova Lite, Amazon Bedrock Knowledge Bases, and Amazon Bedrock Data Automation.
Amazon EKS provided the container platform for the remote maintenance system.
The solution supports real-time semantic search, source attribution, cross-language translation, image understanding, automated summarization, and a closed-loop process that uploads completed work orders back into the knowledge base.

Technologies

Amazon Bedrock Amazon Bedrock Knowledge Bases Amazon Bedrock Data Automation Amazon OpenSearch Service Amazon Neptune Amazon EKS Amazon Glue Amazon Nova Amazon Nova Lite

The repair cycle was reduced from 7 days to 3-4 days.
Technical experts and dealers can query the knowledge base to pre-analyze faults and return repair plans faster.
On-site technicians can prepare tools and parts in advance, shortening repair time.
After repairs, the system automatically summarizes outcomes and stores them as reusable knowledge for future cases.

Architecture

Maintenance cases, work orders, manuals, images, video, and logs are ingested and transformed with Amazon Glue. Amazon OpenSearch Service provides vector retrieval for RAG, while Amazon Neptune stores graph relationships among cases, components, and fault codes. Amazon Bedrock with Amazon Nova/Amazon Nova Lite and Amazon Bedrock Knowledge Bases handles multimodal understanding, prompt augmentation, source attribution, and summarization. Amazon Bedrock Data Automation extracts structured data from unstructured multimodal content. The application runs on Amazon EKS.

Sources & evidence1

Evidence: Medium50/100Evidence strength