The AWS Expert

Case Studies

Representative AWS consulting engagements.

See how we approach AWS cost optimization, cloud security, and AI architecture with practical recommendations and clear implementation roadmaps.

Example Engagements

How we solve complex AWS problems.

Each case study demonstrates the challenge, consulting approach, technical scope, and expected business outcomes.

Cost OptimizationRepresentative engagement

AWS Cost Optimization Assessment

A structured review focused on reducing unnecessary cloud spend without weakening reliability or performance.

Challenge

AWS costs were increasing without clear visibility into resource utilization, idle infrastructure, or purchasing efficiency.

Approach

Reviewed EC2 sizing, RDS usage, EBS volumes, snapshots, storage classes, scheduling opportunities, and Savings Plan options.

Scope

  • Compute and database utilization
  • Storage and snapshot cleanup
  • Savings Plans and purchasing strategy
  • Development environment scheduling

Outcome

  • Identified concrete cost-reduction opportunities
  • Improved visibility into major AWS cost drivers
  • Delivered a prioritized optimization roadmap
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Cloud SecurityRepresentative engagement

AWS Security Assessment

A focused review of identity, exposure, encryption, logging, and threat-detection controls.

Challenge

The AWS environment required stronger IAM controls, better security visibility, and clearer governance across accounts and workloads.

Approach

Reviewed IAM policies, public exposure, security groups, encryption, CloudTrail, GuardDuty, Security Hub, and logging coverage.

Scope

  • IAM and least-privilege access
  • Network and public exposure review
  • Encryption and secrets management
  • Logging, monitoring, and threat detection

Outcome

  • Identified high-priority security risks
  • Reduced uncertainty around identity and access controls
  • Delivered a prioritized remediation roadmap
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AI on AWSRepresentative engagement

Secure AI Architecture Review

A production-focused architecture review for AI workloads built with Amazon Bedrock and AWS-native services.

Challenge

The organization needed a secure foundation for enterprise AI workloads involving sensitive data, RAG, and external model access.

Approach

Designed an Amazon Bedrock architecture using serverless services, retrieval-augmented generation, least-privilege access, encryption, and audit logging.

Scope

  • Amazon Bedrock integration
  • RAG and vector search architecture
  • Identity and data isolation
  • Monitoring, logging, and governance

Outcome

  • Defined a production-ready AI architecture
  • Improved security and governance controls
  • Created a scalable AWS-native implementation path
View case study