What was the impact of the AI solution?

Reduced case resolution times by 40% and increased first contact resolution by 20%. Handled over 10,000 cases with AI assistance since deployment. Enabled scalable and intelligent customer support for complex automotive product portfolios.

Source

ProductionEvidence: Medium50/100

smart Europe Revolutionizes Automotive Customer Support with Amazon Bedrock

Use case typeCustomer personalizationUpdated Jun 13, 2026

smart Europe GmbH transformed its automotive customer support with smart. AI Case Handler, a generative AI-powered tool that automates case tagging and provides AI-generated analysis and response suggestions. The solution uses a serverless architecture comprising Amazon Bedrock, AWS Lambda, Amazon SQS, and AWS Step Functions to handle high volumes of support cases efficiently. Two AI-driven workflows were implemented: Intelligent Case Tagger for real-time case classification and Case Insights Generator for dynamic AI-generated support insights. This architecture improved support agent efficiency and service quality, achieving significant reductions in case resolution times and increased first contact resolution rates.

Organization: smart Europe GmbH
Industry: Automotive
Location: Germany
Published: December 2025

Reported outcomes

Time: −40%

Time & speed

Time: −20%

Catalog median for time & speed deployments: −50% across 295 reported metrics. Compare benchmarks →

Planned next steps

The source says the organization aims to achieve Cost: −30%.

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

Normalized claim

Time: 40% decrease

AWS Machine Learning BlogDec 10, 2025Blog postInferred claimMedium evidence strength

Reduced case resolution times by 40% and increased first contact resolution by 20%.

Normalized claim

Time: 20% decrease

AWS Machine Learning BlogDec 10, 2025Blog postInferred claimMedium evidence strength

Reduced case resolution times by 40% and increased first contact resolution by 20%.

Normalized claim

Cost: 30% decrease

AWS Machine Learning BlogDec 10, 2025Blog postInferred claimMedium evidence strength

Projected 30% budget savings in 2025 due to operational efficiencies.

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

Customer: smart Europe GmbH
Provider: AWS
Maturity: Production
Linked source: AWS Machine Learning Blog

Manual case handling caused bottlenecks, increased operational costs, and inconsistent service quality across agents

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 2 of 2

1Generative AI for Customer Support
2AI-Powered Workflow Automation

Developed smart.AI Case Handler using Amazon Bedrock GenAI models integrated via AWS Lambda and Step Functions.
Implemented event-driven workflows with Amazon SQS for buffering and concurrency control to prevent API throttling.
Enabled proactive background processing to cache AI-generated insights, minimizing agent wait times.
Integrated with Salesforce CRM to provide real-time AI summaries and response suggestions to support agents.

Technologies

Amazon Bedrock AWS Lambda Amazon SQS AWS Step Functions Salesforce CRM

Reduced case resolution times by 40% and increased first contact resolution by 20%.
Handled over 10,000 cases with AI assistance since deployment.
Enabled scalable and intelligent customer support for complex automotive product portfolios.

Architecture

Serverless architecture with AWS Lambda, Amazon SQS, AWS Step Functions orchestrating GenAI workflows using Amazon Bedrock endpoints. Real-time event-driven case tagging and AI insight generation integrated with Salesforce CRM.

Sources & evidence1

Evidence: Medium50/100Evidence strength