AWS Case Study · Hospitality Technology / SaaS

A scalable AWS platform with automated delivery.

BimPos needed its e-menu application to scale with demand while keeping deployment cost and operational effort under control. The development team also wanted a CI/CD process that could release changes consistently without manual production deployment work.

The challenge

What had to change.

The application needed secure private networking, scalable compute, a highly available MySQL database and automated releases. The architecture had to support demand peaks while avoiding unnecessary always-on capacity.

Our solution

How Ghaim approached it.

Ghaim designed a secure AWS architecture using private subnets, Elastic Beanstalk, Application Load Balancer, Auto Scaling, RDS MySQL, CodePipeline and CloudFormation.

  1. Built a VPC with application and database resources isolated in private subnets.
  2. Deployed the application through AWS Elastic Beanstalk with an Application Load Balancer and Auto Scaling.
  3. Implemented RDS MySQL across two Availability Zones with automated backups.
  4. Built AWS CodePipeline delivery automation connected to the application source workflow.
  5. Defined infrastructure using AWS CloudFormation for repeatable provisioning.
Auto ScalingCapacity follows demand
Multi-AZHigher availability architecture
CI/CDAutomated application releases

AWS services used

AWS Elastic BeanstalkApplication Load BalancerAuto ScalingAmazon RDS for MySQLAWS CodePipelineAWS CloudFormationAmazon VPC
What this demonstrates

AWS architecture tied to operating outcomes.

Each case study reflects the same Ghaim approach: secure foundations, automation, resilience, cost awareness and a clear operating model after deployment.

01

Architecture

Choose AWS services and deployment patterns around the workload’s real availability, scalability and security requirements.

02

Automation

Use Infrastructure as Code and CI/CD to reduce manual operational risk and make changes repeatable.

03

Operations

Design monitoring, recovery and ongoing optimization into the platform instead of adding them after go-live.

Your AWS workload

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