BMW Group Delivers Generative AI-Based Cloud Optimization Assistant (ICCA) Using AWS
BMW Group · Germany
BMW Group developed the In-Console Cloud Assistant (ICCA), a conversational generative AI assistant built with Amazon Bedrock, to help its 450+ DevOps teams across more than 450 AWS accounts monitor performance, identify bottlenecks, and detect optimization opportunities. Built with the AWS Generative AI Innovation Center, ICCA keeps data within the BMW Cloud Room hosted on AWS and has helped BMW scale cloud governance and reduce costs and time to market.
Overview
BMW Group developed the In-Console Cloud Assistant (ICCA), a conversational generative AI assistant built with Amazon Bedrock, to help its 450+ DevOps teams across more than 450 AWS accounts monitor performance, identify bottlenecks, and detect optimization opportunities. Built with the AWS Generative AI Innovation Center, ICCA keeps data within the BMW Cloud Room hosted on AWS and has helped BMW scale cloud governance and reduce costs and time to market.
The challenge
With over 450 DevOps teams working across more than 450 AWS accounts to produce business-critical applications, optimizing cloud infrastructure was a challenge for BMW Group.
The solution
BMW Group worked with the AWS Generative AI Innovation Center to build the In-Console Cloud Assistant (ICCA), a conversational generative AI assistant using Amazon Bedrock. With the ability to understand natural language requests, ICCA helps BMW Group DevOps teams accelerate the optimization process by monitoring performance, identifying bottlenecks, and detecting opportunities for improvement across accounts. Because BMW Group can use Amazon Bedrock like any other AWS service, no data moves outside the BMW Cloud Room, which is hosted on AWS, with data encrypted in transit and at rest.
Reported business value
Using Amazon Bedrock, BMW Group has been able to scale its cloud governance, reduce costs and time to market, and provide a better service for its customers.
Sources
Open any source and check the claim yourself — that is the point of the register.
Other automotive entries in the register.
Volvo Trucks uses proprietary AI models to power adaptive maintenance in its Blue Service Contract
Volvo Trucks North America upgraded its Blue Service Contract to offer optimized service stops based on fuel consumption, idle time and oil samples, powered by the OEM's proprietary AI models, enabling fewer stops for 2023 model year and newer trucks and reducing operating costs for customers.
BMW Group builds plant-scale digital twins with NVIDIA Omniverse, cutting vehicle aerodynamics simulation time 30x
BMW Group is building digital twins of its production facilities using NVIDIA Omniverse libraries, letting global production planning teams collaborate in real time, optimize the layout and design of complex manufacturing systems, and develop autonomous robot and vision AI applications prior to real-world deployment. Working with Siemens, BMW is also accelerating simulation of vehicle aerodynamics: tests on NVIDIA Grace Blackwell and CUDA-X-accelerated Simcenter Star-CCM+ software showed a 30x speedup for transient aerodynamics simulations of entire vehicle geometries, while reducing energy consumption and costs.
General Motors uses AI for digital twins, battery leak detection, and weld/paint inspection in manufacturing
GM builds in-house AI solutions used across its manufacturing facilities: digital twins that simulate a running production line before it's constructed, AI/machine learning that quickly and accurately pinpoints potential leaks in battery packs, and a combination of robotics and a proprietary AI tool that inspects welds and paint coats to identify irregularities and anomalies.
F.02 Contributed to the Production of 30,000 Cars at BMW
Figure deployed its Figure 02 humanoid robot at BMW Group Plant Spartanburg for an 11-month trial performing sheet-metal loading on an active assembly line, running 10-hour shifts Monday-Friday. Over the deployment, the robot loaded 90,000+ parts, logged 1,250+ hours of runtime, and contributed to the production of 30,000+ X3 vehicles, with a target cycle time of 84 seconds and placement accuracy goal of 99% per shift.
Was this helpful?
Your feedback helps us improve our use case database