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Teracloud delivers Intelligent Image Tagging and Database Integration


Images are a powerful tool for communication, marketing, and content creation. However managing large image libraries can be a cumbersome task, especially when it comes to organizing and searching for specific visuals. Manual tagging, the traditional approach, can be time-consuming, tedious, and prone to errors.

This is where Teracloud steps in. Teracloud is a Cloud solutions provider specializing in leveraging technologies to help businesses overcome data challenges. This case study details how our Data Architects empowered a client with a unique image management requirement with the help of intelligent image tagging and seamless database integration.


A client approached Teracloud with a specific challenge: automatically recognizing image content and storing it as tags in an AWS database. Teracloud's experts designed a solution encompassing both image recognition development and the supporting cloud infrastructure. 

This solution involved secure storage for the recognition code, efficient deployment and scaling for the recognition process, and a highly available database to store the extracted tags. 

To automate the workflow, new image uploads triggered an analysis process that identified the image content and stored the generated tags. A secure storage solution housed the client's images, and the entire infrastructure was built within a secure network environment with controlled access.


Teracloud's experts designed and implemented a comprehensive solution that addressed both aspects of the client's challenge. The solution leveraged the following AWS services:

  • Amazon Elastic Container Registry (ECR): Securely stores Docker images containing the image recognition code.

  • Amazon Elastic Container Service (ECS) and ECS Service: Deploys and efficiently scale the image recognition container.

  • Amazon Relational Database Service (RDS) Aurora: Provides a highly available and scalable database to store the extracted image tags.

  • AWS Lambda: Triggered by new images uploaded to an S3 bucket, utilizes Amazon Rekognition to identify all the contents within the image and subsequently inserts the extracted tags into the RDS Aurora database.

  • Amazon Simple Storage Service (S3): Serves as the storage repository for the client's images.

  • Amazon Virtual Private Cloud (VPC), Subnets, and Endpoints: Establishes a secure network environment for the deployed resources.

  • Elastic Load Balancers and Security Groups: Ensures high availability and controlled access to the infrastructure.



By deploying the custom-built solution on AWS, Teracloud empowered the client to achieve several significant benefits:

  • Automated Image Tagging: Images uploaded to the S3 bucket are automatically analyzed, and relevant tags are generated and stored in the RDS database, eliminating manual tagging efforts.

  • Improved Image Search and Organization: The extracted tags enable efficient search and organization of images based on their content, facilitating easier retrieval and management.

  • Scalability and Cost-Effectiveness: The serverless architecture using AWS Lambda and managed services ensures scalability to accommodate future growth and optimizes costs by only paying for utilized resources.

Final thoughts

The ultimate goal of this solution was to seamlessly integrate with the client's web application. By leveraging the extracted image tags, the web application can implement a search bar functionality. Users can then search for specific keywords, and the application will efficiently retrieve all images within the database that have been identified as containing those elements based on the automated tagging process. This empowers users to navigate and explore the image library with greater ease and efficiency.

This successful project exemplifies Teracloud's commitment to delivering innovative solutions that combine cutting-edge technologies with a deep understanding of client needs. Teracloud's expertise in cloud architecture and image recognition enabled the client to unlock the full potential of their image data and streamline their image management processes.


Alan Bilsky

Data Engineer



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