Clyros TechEngineering Excellence. Delivered with Precision.

Case Studies

Representative examples of enterprise delivery—demonstrating our approach, technical depth, and ability to solve complex business-critical challenges.

Client confidentiality agreements prevent us from disclosing specific names or proprietary details. The following case studies have been anonymized while preserving technical accuracy and representative outcomes.

HealthcareCloud MigrationData Platform

Multi-Facility Health System: Cloud Migration & Clinical Data Platform

Client Context

A regional health system operating 12 hospitals and 75+ outpatient facilities across three states. The organization maintained a legacy on-premises data center hosting multiple clinical applications, analytics systems, and patient data repositories.

Aging infrastructure, increasing maintenance costs, limited disaster recovery capabilities, and the need for real-time clinical analytics drove the decision to pursue cloud migration.

Business Challenge

  • Legacy infrastructure nearing end-of-life with escalating maintenance costs and limited scalability
  • Fragmented clinical data across multiple systems preventing unified patient views and analytics
  • HIPAA compliance requirements necessitating rigorous security controls, encryption, and audit trails
  • Zero tolerance for downtime during migration—clinical operations could not be interrupted
  • Limited cloud expertise within internal IT requiring knowledge transfer and training

Our Approach

Phase 1: Assessment & Strategy (8 weeks)

  • Comprehensive application portfolio assessment and dependency mapping
  • Cloud platform selection (AWS chosen based on HIPAA compliance, regional presence, and healthcare reference architecture)
  • Migration wave planning prioritizing low-risk applications first
  • HIPAA compliance architecture design with security controls matrix
  • Cost modeling and TCO analysis

Phase 2: Foundation & Pilot (12 weeks)

  • AWS landing zone implementation with multi-account structure
  • Network architecture (VPC design, Direct Connect, VPN failover)
  • Identity and access management (IAM, SSO integration with Active Directory)
  • Security baseline (encryption, logging, monitoring, GuardDuty, Security Hub)
  • Pilot migration of non-critical application to validate process

Phase 3: Core Application Migration (24 weeks)

  • Phased migration of 37 applications across six migration waves
  • Rehost strategy for legacy applications (lift-and-shift to EC2)
  • Replatform strategy for modernization candidates (containerization, managed services)
  • Data migration with validation and reconciliation processes
  • Cutover planning with rollback procedures and business continuity

Phase 4: Clinical Data Platform (16 weeks)

  • HL7 and FHIR integration layer connecting EHR, lab systems, imaging, and ancillary systems
  • Real-time data pipeline ingesting clinical events (Kinesis, Lambda, S3)
  • Data lake architecture for longitudinal patient records
  • Analytics platform (Redshift, QuickSight) for clinical intelligence and population health
  • API layer for third-party application integration

Technical Solution

Architecture Components

  • • AWS multi-account structure (12 accounts)
  • • Direct Connect (2x 10Gbps) + VPN failover
  • • EC2 instances (mix of on-demand and reserved)
  • • RDS for relational databases (SQL Server, PostgreSQL)
  • • S3 for object storage and data lake
  • • Kinesis for real-time data streaming
  • • Lambda for serverless data processing
  • • Redshift for analytics data warehouse
  • • ECS for containerized applications
  • • CloudWatch, CloudTrail, GuardDuty for monitoring

Security & Compliance

  • • HIPAA Business Associate Agreement (BAA) with AWS
  • • Encryption at rest (EBS, S3, RDS with KMS)
  • • Encryption in transit (TLS 1.2+)
  • • Network segmentation and security groups
  • • Multi-factor authentication (MFA) enforced
  • • Centralized logging and audit trails
  • • Automated compliance scanning (AWS Config, Security Hub)
  • • Backup and disaster recovery (cross-region replication)
  • • Incident response procedures and runbooks

Outcomes

37
Applications migrated with zero unplanned downtime
42%
Infrastructure cost reduction in first year
15M+
Clinical events processed daily via data platform
  • Zero security incidents or HIPAA violations during migration and first 18 months of operation
  • Unified clinical data platform enabling real-time analytics, population health insights, and regulatory reporting
  • Improved disaster recovery posture with RPO < 15 minutes, RTO < 4 hours
  • Enhanced scalability supporting 23% patient volume growth without infrastructure expansion
  • Knowledge transfer completed with internal IT team managing day-to-day operations

Technologies Used

AWSEC2RDSS3KinesisLambdaRedshiftECSTerraformPythonHL7FHIR
Financial ServicesCore ModernizationMicroservices

Regional Bank: Core Banking System Modernization

Client Context

A regional bank with $8B in assets, serving commercial and retail customers across 150 branches. The institution operated a 20-year-old core banking system built on a monolithic architecture, limiting product innovation and digital channel capabilities.

Increasing competition from digital-first banks and fintech companies, coupled with regulatory pressure to improve risk management and reporting, necessitated core system modernization.

Business Challenge

  • Monolithic legacy system preventing rapid product launches and feature updates
  • Limited API capabilities hindering digital channel development and third-party integration
  • Batch-oriented processing unable to support real-time transaction requirements
  • Regulatory compliance gaps in risk reporting, AML, and fraud detection
  • Technical debt and vendor lock-in increasing operational risk and maintenance costs
  • Impossible to replace core system atomically—required phased strangler pattern approach

Our Approach

Phase 1: Strategy & Architecture (10 weeks)

  • Current-state assessment and capability mapping
  • Target architecture design (microservices, event-driven, API-first)
  • Strangler fig pattern strategy for incremental migration
  • Domain-driven design (DDD) to identify bounded contexts
  • Technology stack selection and proof-of-concept
  • Risk assessment and mitigation planning

Phase 2: Platform Foundation (16 weeks)

  • Kubernetes platform deployment (Azure AKS with multi-zone HA)
  • Service mesh implementation (Istio for traffic management, observability)
  • API gateway and developer portal
  • Event streaming backbone (Kafka for event-driven architecture)
  • Observability stack (Prometheus, Grafana, Jaeger, ELK)
  • CI/CD pipelines (GitLab CI, ArgoCD for GitOps)
  • Security baseline (network policies, RBAC, secrets management)

Phase 3: First Bounded Context (12 weeks)

  • Account Management domain extracted from monolith
  • Microservices development (Java / Spring Boot)
  • Event sourcing and CQRS patterns for audit and consistency
  • Change data capture (CDC) from legacy system using Debezium
  • Dual-write mitigation and eventual consistency handling
  • Comprehensive testing (unit, integration, contract, E2E)
  • Production deployment with canary release

Phase 4: Additional Domains (36 weeks, iterative)

  • Payments & Transfers domain (real-time transaction processing)
  • Lending & Credit domain (loan origination and servicing)
  • Customer Identity & Access Management (IAM)
  • Fraud Detection & AML (real-time transaction monitoring)
  • Regulatory Reporting (automated compliance data aggregation)
  • Iterative deployment with gradual traffic migration

Technical Solution

Architecture Patterns

  • • Strangler fig pattern for incremental migration
  • • Domain-driven design (DDD) bounded contexts
  • • Event sourcing and CQRS for auditability
  • • API-first design with OpenAPI specifications
  • • Event-driven architecture (Kafka event backbone)
  • • Saga pattern for distributed transactions
  • • Circuit breaker and retry patterns for resilience
  • • Change data capture (CDC) for legacy integration

Technology Stack

  • • Azure Kubernetes Service (AKS)
  • • Java 17, Spring Boot, Spring Cloud
  • • Apache Kafka for event streaming
  • • PostgreSQL (Azure Database for PostgreSQL)
  • • Redis for caching and session management
  • • Kong API Gateway
  • • Istio service mesh
  • • Prometheus, Grafana, Jaeger, ELK Stack
  • • GitLab CI/CD, ArgoCD, Terraform

Outcomes

85%
Reduction in time-to-market for new products
99.95%
System availability achieved (vs. 99.7% prior)
250M+
Annual transactions processed via new platform
  • Real-time transaction capabilities enabling instant payments and fraud detection
  • API-first architecture accelerating digital channel development and partner integration
  • Improved regulatory compliance with automated AML screening and real-time reporting
  • Enhanced resilience with multi-zone deployment and automated failover
  • Developer productivity improvements via self-service platform and CI/CD automation
  • Successful production rollout with zero customer-facing incidents during migration

Technologies Used

AzureKubernetesJavaSpring BootKafkaPostgreSQLRedisIstioKongTerraformGitLab CIArgoCD