A 50% staff cut bottlenecked security across 14 clouds. AI now automates policy code, boosting development efficiency by nearly 70%.
Great Wall Motor is a global automotive technology company operating multiple brands across more than 170 countries and managing 14 global cloud environments.
The organization faced significant challenges maintaining unified security governance and compliance standards following a 50% reduction in its...
“Deploying the security compliance system on AWS China using Amazon Q Developer and AWS Graviton4 enabled measurable improvements in development efficiency, cost structure, and system stability, with elastic scheduling contributing to sustained savings. A consistent architecture across overseas and China regions supported the unification of GWM’s global technical framework. During the migration, standardized managed services reduced operational complexity, allowing the team to focus on advancing security capabilities and supporting ongoing business growth.”
Great Wall Motor's Security compliance system development is part of this use case:
Global automotive manufacturer specializing in SUVs, pickup trucks, and electric vehicles.
Cloud computing platform and on-demand infrastructure services.
Related implementations across industries and use cases
Fixing 60,000 compliance errors per module bottlenecked scarce engineers. Now, three AI agents draft transparent fixes for human approval.
Sensor data overwhelmed local servers. Engineers now run natural language queries 5x faster via AI, cutting PoC timelines by 80%.
Software updates were tied to rigid vehicle production cycles. A GenAI platform now frees 5,000 developers to release code independently.
Fixing 60,000 compliance errors per module bottlenecked scarce engineers. Now, three AI agents draft transparent fixes for human approval.
Software updates were tied to rigid vehicle production cycles. A GenAI platform now frees 5,000 developers to release code independently.
Conflicting rules across 3,000 services blocked qualified drivers. AI now traverses a graph to validate dependencies in milliseconds.
Sourcing native speakers for 14 markets was costly. Now, AI handles 40% of routine queries and translates live for a central English team.
Repetitive coding slowed R&D. Now 80% of engineers use agentic tools to automate work, saving up to 2 hours weekly per person.
Manual testing consumed 20% of developer time. Now, 1,500 engineers use AI agents to auto-generate tests and prototype solutions.
A 50% staff cut bottlenecked security across 14 clouds. AI now automates policy code, boosting development efficiency by nearly 70%.
Great Wall Motor is a global automotive technology company operating multiple brands across more than 170 countries and managing 14 global cloud environments.
The organization faced significant challenges maintaining unified security governance and compliance standards following a 50% reduction in its...
“Deploying the security compliance system on AWS China using Amazon Q Developer and AWS Graviton4 enabled measurable improvements in development efficiency, cost structure, and system stability, with elastic scheduling contributing to sustained savings. A consistent architecture across overseas and China regions supported the unification of GWM’s global technical framework. During the migration, standardized managed services reduced operational complexity, allowing the team to focus on advancing security capabilities and supporting ongoing business growth.”
Great Wall Motor's Security compliance system development is part of this use case:
Global automotive manufacturer specializing in SUVs, pickup trucks, and electric vehicles.
Cloud computing platform and on-demand infrastructure services.
Related implementations across industries and use cases
Fixing 60,000 compliance errors per module bottlenecked scarce engineers. Now, three AI agents draft transparent fixes for human approval.
Sensor data overwhelmed local servers. Engineers now run natural language queries 5x faster via AI, cutting PoC timelines by 80%.
Software updates were tied to rigid vehicle production cycles. A GenAI platform now frees 5,000 developers to release code independently.
Fixing 60,000 compliance errors per module bottlenecked scarce engineers. Now, three AI agents draft transparent fixes for human approval.
Software updates were tied to rigid vehicle production cycles. A GenAI platform now frees 5,000 developers to release code independently.
Conflicting rules across 3,000 services blocked qualified drivers. AI now traverses a graph to validate dependencies in milliseconds.
Sourcing native speakers for 14 markets was costly. Now, AI handles 40% of routine queries and translates live for a central English team.
Repetitive coding slowed R&D. Now 80% of engineers use agentic tools to automate work, saving up to 2 hours weekly per person.
Manual testing consumed 20% of developer time. Now, 1,500 engineers use AI agents to auto-generate tests and prototype solutions.