Intelligent inspection

Transforming visual inspections through AI and synthetic data in partnership with AWS

The challenge

Manual inspections lead to inaccuracy, data gaps, integration challenges, and safety risks. In addition:

• Manual visual inspections are time-consuming, often inaccurate, and lead to missed defects.
• AI adoption is hindered by the scarcity of large volumes of defect images, particularly for rare events. 
• Organizations face IT challenges due to the use of point solutions for defect detection, causing interoperability issues and fragmentation.
• Integration of multiple applications into existing IT landscapes results in complexity and inefficiencies.

Our solution

Intelligent Inspection is an end-to-end framework that accelerates the development and deployment of computer vision (CV) models for visual inspection. It automates the detection of defects and anomalies using AI-powered analysis of visual data from drones, cameras, and edge devices—improving accuracy, speed, and compliance with safety standards.

The framework can process inspection requests, runs CV models to identify issues, and aggregates results for actionable insights, using prebuilt configurable pipelines. When real-world data is limited, models can be trained using synthetic data from simulated environments. Configurable orchestration pipelines streamline plug-and-play model integration, while cloud capabilities enable near real-time video analytics. Intelligent Inspection can also leverage IT and OT systems for contextual insights from AI.

Key features include:

• Pre-built configurable visual pipelines:  
  • Configuration of storage location for each layer  
  • Video chunking for optimal performance  
  • Define execution sequence 
• Process tracking and logging for audit
  • End-to-end workflow logging
  • Error and exception logging for restart ability
  • Compliance and audit report for inspection activities
• Scalable visual processing for CV-AI consumption
  • Integrate with model registries. Eg: SageMaker for version control
• Auto-scaling for ECS/EKS based on SQS queue depth
  • Containerized microservices for each stage to enable independent scaling
  • Scale up based on the increased workload or containers based on SQS queue length
  • Automatically scale down services during off-peak hour
• Integration with CloudWatch
  • Centralize log aggregation of each containerized task and AWS service
  • Publish custom metrices such service health and processing time etc.
  • Setup alarm and notification

Client value creation

• Accelerated AI model training: Generates hyper-realistic synthetic images to supplement or replace scarce real-world data, enabling faster and more robust training of computer vision models. Improves inspection accuracy and coverage through synthetic data, enhancing model accuracy for rare or hard-to-capture scenarios.
• Scalable and cost-effective: Real-time video analytics reduce the need for manual inspections, lowering costs while enabling scalable deployment across various environments, including drone-guided inspection; pay per use model.

In partnership with AWS

Capgemini, in collaboration with AWS, combines deep industry knowledge and AI & cloud expertise to drive innovation, optimize operations, and achieve strategic goals. As a Premier Tier Partner, Capgemini leverages its 15-year relationship with AWS, offering a robust team of 11,000 certified consultants and 3,500 certified AWS professionals, along with 10 AWS competencies. 

For more information, contact us or visit the AWS Marketplace.