Case Studies

Challenge

A regional bank had operated on a monolithic core banking platform for twelve years. The platform served all product lines through a shared relational schema with no service boundaries. Test coverage was negligible — engineers were reluctant to change anything for fear of side effects that wouldn't be detected until production. Release cycles averaged four weeks, requiring coordination across the entire engineering organisation for each deployment. The bank had new digital products waiting to launch that the monolith could not support without significant rework.

Approach

AlgoDomain used a strangler fig migration pattern — identifying domain boundaries within the monolith and replacing them incrementally with separate Java microservices deployed on Kubernetes. An API gateway was introduced as the first step to route traffic and enable the parallel running of old and new components during transition. Each new service was built with comprehensive automated test coverage. Data migration used dual-write patterns to maintain consistency while cutover occurred.

Outcome

The programme ran across six phases over 26 months. By the end of phase three, the first three product domains were running on independent services with daily deployments — the first time in the bank's history that releases could happen independently per domain. At programme completion, the monolith was fully decommissioned. Release cycle time dropped from four weeks to daily. System availability in production reached 99.97% over the subsequent 12-month measurement period.

Challenge

A health network operating 14 facilities had patient data distributed across six distinct systems: an EHR platform, a legacy claims system, a pharmacy platform, a lab information system, a scheduling system, and a financial system. There was no integration layer connecting these. Clinical analytics required manual data extraction from each system, reconciliation in spreadsheets, and days of analyst time per reporting cycle. HIPAA requirements constrained how data could be moved and stored, adding compliance complexity to every integration decision.

Approach

AlgoDomain designed and built a FHIR-compliant data lake on Azure Data Lake Storage Gen2 as the unified patient data repository. HL7 and proprietary source formats were ingested via Azure Data Factory, transformed to FHIR R4 resources. A dbt transformation layer built dimensional models for clinical and operational analytics use cases. Row-level security and audit logging were implemented to meet HIPAA access controls. Power BI was connected through a semantic layer with pre-built clinical dashboards for the network's priority reporting use cases.

Outcome

The platform went live across all 14 facilities in two phases over 14 months. Monthly regulatory reporting, previously a 3-day manual process, now runs in under 4 hours from data that updates automatically. Clinical analysts have a unified patient view for the first time, enabling cross-site population health analysis that was previously impossible. The platform processes over 2 million patient records and handles all six source system feeds with automated reconciliation alerts.

Challenge

A mid-size e-commerce retailer was operating its order management, fulfilment, and inventory systems on on-premise infrastructure. The platform could not handle peak seasonal traffic — the team manually provisioned additional hardware weeks before peak periods and then ran over-capacity outside peaks. Infrastructure costs were high, provisioning lead times were long, and a major seasonal peak was 10 months away. Additionally, the on-premise platform had deployment processes that required 4-hour maintenance windows.

Approach

AlgoDomain ran a lift-and-modernize migration to AWS ECS rather than a pure lift-and-shift — containerising services and replacing synchronous inter-service calls with event-driven messaging via Amazon SQS and SNS. This made the architecture elastically scalable and eliminated the coupling that had previously required maintenance windows for deployments. Infrastructure was defined as Terraform code. A staged migration moved individual services across one at a time, validated in production before the next was migrated.

Outcome

The migration completed two months before the seasonal peak. The platform handled 11 times normal peak traffic during the holiday period with no manual intervention — auto-scaling handled the load automatically. Infrastructure cost reduced by 34% on an annualised basis compared to the on-premise estate. Deployment cycle time fell from 4-hour maintenance windows to rolling deployments with no downtime. The team's on-call burden reduced significantly as a result of operational stability improvements.