
Financial Services
Systems built for environments where auditability, resilience and regulatory reporting are the design constraints.
Service
We migrate workloads to the cloud, and — more often — fix the cloud estate that a previous migration left behind: unbounded spend, manual deployments and no way to reproduce production.
01Overview
A lift-and-shift migration usually produces the same architecture at higher cost. The savings people expect come from changing how the workload runs, not where it runs.
We start with the two things that make everything else possible: infrastructure defined as reviewable code, and a deployment pipeline anyone on the team can run. Once production can be recreated from a repository, cost optimisation, scaling and disaster recovery stop being projects and become configuration.
We are deliberately conservative about complexity. Kubernetes is the right answer for some workloads and expensive overhead for many others, and we will say which one you have.
02Capabilities
Assessment, re-architecture and phased migration to AWS, Azure or Google Cloud with a rollback path at every stage.
Terraform-managed environments that are reviewable, reproducible and identical from staging to production.
Pipelines with automated testing, progressive rollout and one-command rollback.
Metrics, tracing and alerting that point at causes, alongside cost attribution per team and service.
03Business benefits
Teams that ship daily accumulate less risk per release than teams that ship quarterly. The pipeline is what makes that safe.
Tagged, attributed cost per service and environment — most estates find double-digit waste in the first month.
If production is defined in code, restoring it is a rehearsed procedure rather than an improvisation under pressure.
Capacity follows demand automatically instead of being provisioned for a peak that happens twice a year.
04Technology
AWS
Broadest managed-service catalogue; our default unless an estate dictates otherwise.
Azure
Natural fit alongside Microsoft identity, Dynamics and on-prem AD.
Google Cloud
Data and ML workloads, particularly BigQuery-centred analytics.
Kubernetes
Where workloads genuinely need orchestration — not by default.
Docker
Reproducible environments from a developer laptop to production.
Terraform
Infrastructure as reviewable code. No console-clicked production.
Redis
Caching, queues and rate limiting where latency budgets are tight.
Apache Kafka
Event backbone for systems that must decouple producers from consumers.
05Process
Current architecture, spend, dependencies and compliance constraints documented — including what should not move.
A migration plan with projected cost, sequencing and the specific risks at each stage.
Accounts, networking, identity, IaC baseline and the deployment pipeline, before any workload moves.
Workload by workload, lowest risk first, each with a tested rollback and a parallel-run period.
Right-sizing, autoscaling policy, cost review cadence and enablement for your platform team.
06Industries
All industries
Systems built for environments where auditability, resilience and regulatory reporting are the design constraints.

Commerce platforms that hold up under peak load, with the inventory accuracy that makes omnichannel promises deliverable.

Clinical and administrative systems built to the standards the sector requires — interoperable, auditable and designed for clinical workflow.

Engineering capacity for product companies — extending your team without diluting the standards it works to.
Want this scoped against your systems rather than described?
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Next step
Start with a fixed-price technical assessment. You get an architecture decision record, a delivery plan and a real estimate — and you are free to take them elsewhere.