What was the impact of the AI solution?

The solution reduced expert operator document analysis time by 30-50%, accelerated maintenance inspections, and promoted sustainability by increasing part reuse and enabling circular economy in aerospace. It improved operational efficiency with scalable AWS-managed services and reduced costs with a serverless design.

Source

Capgemini Lifecycle Optimization for Aerospace extends component lifespan with AWS AI

Use case typePredictive maintenanceUpdated Jun 13, 2026

Capgemini developed the Lifecycle Optimization for Aerospace solution to automate and optimize complex aircraft maintenance data analysis. The system digitizes aircraft component documents, consolidates historical data, and enables part reuse to support circular economy practices in aerospace.

Organization: Capgemini
Industry: Manufacturing
Location: France
Published: November 2023

Reported outcomes

−50%

timeTime & speed

Strategic outcomes

Sustainability & ESGIncreased part reuse for circular economySpeed & agilityAccelerated maintenance inspectionsCost efficiencyReduced costs with serverless design

Catalog median for time & speed deployments: −60% across 727 reported metrics. Compare benchmarks →

Primary read

Use case focus

Showing 3 of 3

1Document Analysis
2Predictive Maintenance
3Sustainability

Aircraft maintenance involves manual, complex analysis of large volumes of unstructured data from multiple sources, slowing down processes and limiting reuse of parts.

Capgemini built a serverless, multi-tenant architecture on AWS using Amazon Textract for OCR and data extraction, Amazon SageMaker for ML models classifying documents, AWS Step Functions for orchestration, Amazon OpenSearch Service for indexing, and other AWS managed services.
The architecture uses AWS Control Tower for multi-account deployment, Lambda for compute, and integrates secure, scalable, asynchronous, event-driven processing to handle large document sets efficiently.

Technologies

Amazon Textract Amazon SageMaker Amazon API Gateway AWS Lambda AWS Step Functions Amazon OpenSearch Service Amazon S3 AWS Control Tower AWS KMS

The solution reduced expert operator document analysis time by 30-50%, accelerated maintenance inspections, and promoted sustainability by increasing part reuse and enabling circular economy in aerospace.
It improved operational efficiency with scalable AWS-managed services and reduced costs with a serverless design.

Architecture

The solution uses a serverless multi-tenant architecture on AWS with Amazon Textract, SageMaker ML models for document classification, API Gateway, Lambda, Step Functions for orchestration, OpenSearch Service for indexing, and S3 for storage. It is deployed securely with AWS Control Tower and includes asynchronous event-driven workflows to handle large, multi-page maintenance documents efficiently.

Sources & evidence1

Groundedness: 5/5

Primary source

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Customer evidence