Solutions Designed Around Business Problems

Business-critical applications are slow to change, expensive to run and depend on a shrinking pool of people who understand them.

• Assess the portfolio and score each application by value and risk
• Choose rehost, re-platform or re-architect per application
• Modernise incrementally behind stable APIs
• Retire legacy components once traffic moves

• Java / Spring Boot
• .NET
• Microservices
• Containers
• API gateways

Data-centre contracts are ending, infrastructure is hard to scale and cloud adoption so far has been ad hoc and costly.

• Build a migration roadmap from a discovery of workloads and dependencies
• Establish a secure landing zone with guardrails
• Migrate in waves with automated testing
• Introduce FinOps and operational runbooks

• AWS
• Azure
• Google Cloud
• Terraform
• Kubernetes

Legacy warehouses and nightly batch jobs cannot keep up with data volumes, and reports arrive too late to act on.

• Map current sources, pipelines and report consumers
• Design a lakehouse or cloud warehouse target
• Rebuild pipelines with testing and lineage
• Migrate reports and decommission legacy jobs

• Snowflake
• Databricks
• Spark
• dbt
• Airflow

Leadership wants value from AI, but pilots stall over data quality, accuracy concerns and unclear return.

• Identify and rank use cases by value and feasibility
• Ground models in enterprise data with retrieval
• Build evaluation, guardrails and human review
• Deploy with monitoring and feedback loops

• LLMs
• RAG pipelines
• Vector databases
• Python
• MLOps tooling

Customers move between web, mobile and support channels, and each one shows a different, incomplete picture.

• Research journeys and pain points with real users
• Design a consistent component system
• Build web and mobile front ends on shared APIs
• Measure behaviour and iterate

• React
• Next.js
• React Native
• Headless CMS
• Analytics

Teams spend hours re-keying data between systems and chasing approvals over email.

• Map processes and quantify manual effort
• Automate rules-based steps and integrate systems
• Apply document AI to unstructured inputs
• Add dashboards for throughput and exceptions

• Workflow engines
• Integration platforms
• Document AI
• APIs

Every department builds its own reports, metrics disagree and nobody is sure which dashboard to trust.

• Define shared business metrics and ownership
• Model a governed semantic layer
• Build self-service dashboards on top
• Set up data quality checks and alerting

• Power BI
• Tableau
• dbt
• Snowflake
• Data catalogues

Releases are manual, infrequent and risky, and environments drift apart over time.

• Baseline delivery metrics and bottlenecks
• Automate build, test and deployment pipelines
• Codify environments as infrastructure
• Introduce observability and incident practices

• GitHub Actions
• Jenkins
• Terraform
• Docker
• Kubernetes

Decades-old systems hold critical business rules, but they are undocumented and hard to integrate.

• Extract and document embedded business rules
• Wrap legacy functions with APIs
• Replace modules progressively (strangler pattern)
• Migrate data with reconciliation and parallel runs

• API layers
• Event streaming
• Java
• .NET
• Cloud databases

Point-to-point integrations have multiplied until a single change breaks systems no one expected.

• Inventory interfaces and data flows
• Define an API-led and event-driven integration model
• Build reusable integration services
• Add monitoring, versioning and governance

• REST & GraphQL
• Kafka
• MuleSoft
• API management
• iPaaS