The Challenge
The customer is a regional financial services and investment firm operating in a highly competitive and regulated market. Their core business includes customer-facing CRM platforms and real-time trading applications, which were hosted on a VMware-based private cloud.
As customer volumes increased and digital engagement became critical, the firm needed a more agile, scalable, and analytics-driven platform to support business growth while maintaining performance, security, and cost efficiency.
The existing private cloud environment had become a bottleneck for both business and technology teams:
Limited Scalability:
The VMware-based infrastructure struggled to handle peak trading loads and sudden spikes in customer activity, especially during market volatility.
Lack of Advanced Analytics & AI Capabilities:
The platform lacked native support for AI/ML-driven insights, making it difficult to analyze customer behavior, sentiment, and engagement trends in real time.
High Infrastructure & Licensing Costs:
Rising VMware licensing fees, database costs, and hardware refresh cycles significantly increased operational expenditure (OPEX).
Slow Release Cycles:
Application updates followed a weekly release cadence due to rigid infrastructure provisioning and manual deployment processes.
The Solution
Celestibia Solutions acted as the cloud modernization and migration partner, responsible for:
Designing a scalable and secure AWS cloud architecture
Migrating legacy workloads from private cloud to AWS
Introducing serverless and AI-driven capabilities
Optimizing costs and improving release velocity
Enabling long-term innovation through cloud-native services
We designed and implemented a modern, cloud-native AWS architecture tailored for financial services workloads.
Application Modernization & Migration
CRM and trading applications were migrated to Amazon EC2 for compute scalability.
Databases were modernized and moved to Amazon RDS, improving availability and reducing operational overhead.
Auto Scaling groups ensured the applications could dynamically scale during high trading volumes.
Automation & Serverless Enablement
Introduced AWS Lambda to automate backend workflows such as trade notifications, customer alerts, and data processing tasks.
Reduced dependency on always-on servers, improving efficiency and responsiveness.
AI-Driven Analytics
Integrated AWS Comprehend to analyze customer communications, support tickets, and interaction data.
Enabled sentiment analysis and behavioral insights, helping the firm better understand customer needs and improve engagement strategies.
Operational Visibility & Cost Governance
Implemented centralized monitoring using Amazon CloudWatch.
Enabled cost visibility and optimization using AWS Cost Explorer, helping finance and IT teams track and control cloud spend.
How We Did It (Technical Approach)
Used AWS Migration Hub to centrally track application migration progress and dependencies.
Leveraged AWS Application Migration Service (MGN) for lift-and-shift migration of VMware workloads to Amazon EC2 with minimal downtime.
Migrated databases securely using AWS Database Migration Service (DMS) with continuous replication and data validation.
Designed serverless workflows using AWS Lambda and Amazon API Gateway for event-driven processing.
Implemented CloudWatch dashboards and alarms for performance monitoring and operational alerts.
Enabled cost controls and optimization insights using AWS Cost Explorer and usage reports.
The Results
20% reduction in operational expenditure (OPEX) through optimized infrastructure and reduced licensing costs
32% improvement in customer engagement driven by AI-powered sentiment analysis and faster response times
Release cycles accelerated from weekly to daily, enabling faster feature delivery and innovation
Improved application scalability, resilience, and observability
Future-ready cloud foundation supporting advanced analytics and digital growth