AI Image & Video Tagging Automation

We Enhanced Resume Processing Speed and Accuracy Using AWS Lambda and Textract.

Customer challenges

Our client, an AI company focused on intelligent image and video analysis, faced several key obstacles in scaling their custom object detection capabilities. Their workflow relied heavily on manual image annotation and model testing, which created bottlenecks in delivery and innovation. As their media processing demands increased, the lack of an automated pipeline for uploading, tagging, metadata management, and model deployment became a significant limitation. Additionally, they required a scalable infrastructure that could handle both real-time requests and batch workloads efficiently. Security, speed, and operational scalability were top priorities, particularly as they prepared to onboard more customers and process thousands of media assets daily.

Solutions

To overcome these challenges, we implemented a fully automated, end-to-end image tagging solution built on AWS. Using custom-trained YOLO v8 models deployed in Docker containers via Amazon ECS and AWS Fargate, we created a scalable architecture capable of parallel image processing. A web-based interface allowed users to upload images, which triggered a serverless event pipeline using Amazon S3 and Amazon SQS. These events were orchestrated by AWS Lambda and EventBridge to execute tagging tasks dynamically. Tagged image data and metadata were stored securely in Amazon S3 and Amazon DynamoDB. Real-time access was made possible through a RESTful API built with Amazon API Gateway. To support agile development and deployment, we used AWS CodePipeline and CodeBuild for CI/CD, while AWS CloudFormation enabled infrastructure as code for consistent and scalable environment provisioning.

AWS services used

Amazon S3Amazon SQSAWS LambdaAmazon EventBridgeAmazon ECSAWS FargateAmazon DynamoDBAmazon API GatewayAWS CodePipelineAWS CodeBuildAWS CloudFormationAWS IAMAmazon CloudWatch

Results

The solution delivered intelligent, scalable, and production-grade object detection automation:

Achieved 4x faster image processing using dynamic scaling on AWS Fargate.
Fully eliminated manual processing with automated image-to-metadata pipelines.
Enabled real-time tagging and metadata retrieval with low latency.
Reduced operational costs by leveraging serverless and containerized infrastructure.
Processed over 500 images in under four hours with the ability to scale further.
Strengthened security and operational control using IAM and CloudWatch.
Delivered a future-ready, production-grade platform aligned with long-term growth goals.