AI-powered content transformation platform for scalable media automation.


DivasAI is a leading AI-first platform specializing in content transformation and media automation, enabling enterprises to generate high-resolution media assets at scale using generative AI. Built for high-throughput environments, the platform streamlines creative workflows, delivering scalable and automated solutions to marketing teams, digital agencies, and media production studios.
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AWS ECS Fargate, Amazon ECR, Amazon S3, AWS Secrets Manager, AWS IAM, Amazon CloudWatch, GitHub Actions, AWS KMS.
DivasAI needed an optimized and cost-efficient backend to support its AI workloads, specifically high-volume image generation and transformation. The infrastructure had to handle unpredictable batch sizes without service degradation, minimize cold start time for batch jobs, and simplify operational management.
The EKS-based system required manual intervention for scaling pods, frequent node group tuning, and had steep learning and maintenance overheads. To unlock agility, the company wanted a serverless container solution with automatic scaling, CI/CD deployment automation, and clean separation of development, staging, and production environments.
Security remained a top priority, with infrastructure required to support encrypted secrets, secure image storage, and least-privilege access control.

1. Operational Complexity in EKS: Managing Kubernetes resources, worker nodes, and Helm-based configurations introduced significant DevOps overhead. Node auto-scaling was delayed under burst workloads, degrading performance during media-heavy operations.
2. Cost Overruns and Overprovisioning: EKS node groups were frequently overprovisioned to avoid delays in image processing. Idle compute time and underutilized nodes led to unnecessary cost spikes.
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The entire backend infrastructure was re-platformed on AWS ECS with Fargate launch type to eliminate server management and simplify workload orchestration. Each image batch processing task runs as a stateless container task, automatically scaled per job demand.
AWS Secrets Manager was integrated with ECS task definitions to inject runtime secrets securely. A customer-managed KMS key provided encryption at rest, and IAM roles enforced least-privilege access to secrets and services.
Deployment pipelines were redesigned to use GitHub Actions to build Docker images, push them to ECR, update ECS services, and trigger blue/green deployments with minimal downtime.
Fargate's pay-per-use model reduced idle time costs. Task definitions were fine-tuned for memory and CPU allocation per image processing job, eliminating resource waste. Auto Scaling policies allowed dynamic scale-out during heavy processing cycles and scale-in during idle windows.
Amazon CloudWatch metrics, alarms, and container-level logs provided visibility into job completion times, failure rates, and resource usage. This enabled real-time optimization and faster troubleshooting.
The first step in any app development process is to have a clear understanding of what is being built, who it is being built for, how they plan to use it and what values can you bring into it.
The AWS solution delivered significant improvements across Wisemelon's key performance metrics:


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