Overview

Myridius analyzed, redesigned, and deployed a high-performance database back-end to address performance, scalability, reliability, and cost issues—resulting in a dramatically improved customer experience.

What was the Problem?
The client’s existing Amazon AuroraDB struggled with high read/write transaction volumes during peak booking periods, causing performance degradation. Manual vertical scaling increased operational complexity, while rising storage and compute costs added financial strain.

Our Solution
Myridius migrated the database from AuroraDB to Amazon DynamoDB, leveraging:

Seamless auto-scaling

Single-digit millisecond latency

On-demand pricing model

Multi-region replication with global tables

Outcomes

95% improvement in processing speed

Support for millions of concurrent users

40% reduction in debugging time with centralized logging

30% faster engineer response times

Lessons Learned
Migrating to serverless architecture and integrating automated monitoring significantly improved performance, scalability, and cost efficiency.

 

Overview
Myridius transformed the member experience for program research and enrollment from a time-consuming (and sometimes manual) process to a seamless conversational approach that took minutes instead of days using a Generative AI solution and agentic flows.

What was the Problem?
The customer’s member enrollment process was inconsistent and outdated across locations, with prospective members often downloading, filling out, and mailing lengthy forms, leading to delays of several days.

Our Solution
Myridius implemented a Generative AI solution, utilizing agentic flows that streamlined member research and enrollment, providing a seamless digital experience.

Outcomes
Seamless, conversational member experience
Reduced enrollment process time from days to minutes
Improved stakeholder engagement and satisfaction

Lessons Learned
Early integration with existing enterprise capabilities was key to delivering immediate value and engaging stakeholders. Frequent feedback helped prioritize key features for future releases.

Overview
Myridius conceived, designed, and delivered a vastly improved analyst experience via a single pane of glass for all enterprise data and analytics assets, while honoring regulatory constraints and security protocols, leading to significant improvements in productivity.

What was the Problem?
Thousands of business and data analysts accessed disparate data sources to perform analyses, leading to a proliferation of data assets and thousands of service tickets for system access every year.

Our Solution
Myridius implemented a centralized analytics platform, streamlining data access and enhancing collaboration across departments while adhering to regulatory and security requirements.

Outcomes

• Improved analyst productivity with centralized data access
• Reduced system access requests by 50%
• Enhanced regulatory compliance through secure data protocols

Lessons Learned
Centralizing analytics in a single platform was crucial for improving productivity and reducing manual processes. Ongoing integration with business feedback was vital for continuous optimization.

Overview
The dining booking and reservation system required a significant overhaul to handle the growing demand of millions of orders and reservations efficiently. The primary goal was to scale operations, improve processing speed, and enhance the guest booking experience.

What was the Problem?
With millions of guests booking dining reservations, the existing reservation system struggled with scalability issues, slow troubleshooting, and inefficient monitoring, leading to delays and a poor user experience.

Our Solution
Myridius migrated the system to a serverless, cloud-native architecture leveraging Amazon EKS, AWS Lambda, and DynamoDB, enabling seamless auto-scaling, faster query times, and real-time data synchronization.

Outcomes

• 95% improvement in processing speed
• Support for millions of concurrent reservations
• 40% reduction in debugging time
• Improved guest booking experience with real-time updates

Lessons Learned
Migrating to a cloud-native, serverless architecture enabled seamless scaling and operational efficiency, significantly enhancing the guest experience during peak demand periods.

Overview

The Configuration Management Database (CMDB) system required a migration to AWS to enhance performance, scalability, and overall system administration efficiency. The goal was to eliminate performance bottlenecks, improve query execution speed, and implement a robust observability framework for better anomaly detection and compliance.

What was the Problem?

The existing CMDB faced several challenges, including:

• Performance bottlenecks and slow query execution.

• Inefficient handling of infrastructure metadata at scale.

• Limited monitoring capabilities, making it difficult to detect anomalies, track system changes, and ensure compliance.

Our Solution

Myridius migrated the CMDB to AWS and implemented a comprehensive monitoring and observability framework using Amazon CloudWatch, Datadog, and BigPanda. The solution included:

• Scalable architecture: Built on Amazon Elastic Container Service (ECS) for improved scalability and reliability.

• Automated server management: Utilized AWS Systems Manager (SSM) for server builds and OS patching within ECS.

• Monitoring and observability stack:

  • Amazon CloudWatch: Infrastructure monitoring, log collection, and alarm management.

  • Datadog: Application performance monitoring (APM), real-time infrastructure monitoring, and log analysis.

  • BigPanda: AI-driven alert correlation and incident management.

• Monitored key metrics such as latency, error rates, CPU and memory usage, disk space, and IOPS.

Outcomes

• 99.95% system availability through proactive monitoring and automated alert correlation.

• 50%+ reduction in Mean Time to Detect (MTTD) and Mean Time to Resolution (MTTR) using BigPanda and Datadog.

• Improved application performance monitoring with real-time insights into latency, errors, and resource utilization.

• Enhanced system reliability, reduced operational overhead, and improved monitoring and observability capabilities, delivering a seamless experience for both users and IT teams.

Lessons Learned

Migrating the CMDB to AWS with a robust monitoring and observability framework significantly improved system performance, reduced incident resolution time, and enhanced operational efficiency. Leveraging AI-driven alert correlation and real-time monitoring enabled faster issue detection and resolution, ensuring better reliability and compliance.