Scalable, secure infrastructure research data and complex processing tasks.
Wisemelon is an AI research company focused on developing advanced machine learning models and natural language processing tools for various industries. Their solutions empower businesses to leverage artificial intelligence for innovation and automation.
AI research , SaaS
Enterprise RAG architecture, Custom AI solutions, Infrastructure, DevOps, Cloud Security.
Designing a cloud infrastructure to support AI research involves addressing several critical challenges. Scalability is a key priority, ensuring the system can handle continuous growth in AI workloads while maintaining seamless performance and adapting to increasing computational demands without bottlenecks. Security is equally important, requiring robust measures to protect sensitive research data and intellectual property while adhering to compliance standards such as GDPR and HIPAA, if applicable. This ensures the integrity and privacy of data in a highly regulated environment.
Cost optimization adds another layer of complexity, necessitating a cost-effective architecture that minimizes unnecessary overhead while leveraging AWS’s advanced capabilities to deliver high performance sustainably. High availability is also a critical requirement, as AI research demands 24/7 computational access. Even minimal downtime can disrupt workflows and impede progress, making a resilient architecture essential to maintain continuous operations. Automation further enhances efficiency, with streamlined pipelines for deploying and managing AI models. Automating data processing, model deployment, and storage management reduces operational overhead, enabling researchers to focus on innovation rather than infrastructure.
These challenges—scalability, security, cost-efficiency, high availability, and automation —must be addressed holistically to create a cloud-native solution capable of supporting the rigorous and evolving demands of AI research effectively and reliably.
Data Growth: The exponential growth of data generated by AI models necessitated an infrastructure that could scale dynamically.
Security and Compliance: Protecting proprietary research data from unauthorized access and ensuring compliance with data protection regulations.
Cost Efficiency: Balancing the computational power required for AI models with the need to optimize costs.
Continuous Deployment: Automating the deployment of AI models and research pipelines with zero downtime was critical for research productivity.
CloudZenia supports you at every stage, delivering reliable, high-quality services at competitive prices.
Our company designed a custom AWS architecture for Wisemelon, meeting all their technical and business requirements for scalability, security, and cost-effectiveness. The architecture follows a multi-tier structure, integrating several AWS services
Managed containerized AI research tasks, with AWS Fargate’s serverless containers enabling Wisemelon to scale automatically without managing underlying infrastructure.
SQS managed document processing and parsing queues, decoupling the architecture and allowing AI research tasks to run asynchronously. This enabled higher throughput without system overload.
S3 buckets provided long-term storage for research data, ensuring durability and scalability. Research papers, processed results, and AI model outputs were stored in S3 for retrieval and processing as needed.
Multiple Lambda functions automated specific tasks within the AI pipeline: A custom Lambda agent loads large datasets and caches them using ElastiCache (Redis) to improve performance
ALB distributed traffic across ECS Fargate containers, ensuring high availability and operational continuity during high workloads.
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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