// capabilities

Four disciplines, one standard.

We keep the practice deliberately narrow. Everything below has been built, run and repaired in production — usually more than once.

01 · cloud architecture

Design the failure modes before the features

A reference architecture is a decision record. Ours start with how the system fails — region, availability zone, dependency — and end with a cost model you can hold us to.

Landing zones & foundations

AWS Control Tower or Azure Lighthouse foundations with guardrails, centralised logging and a cost baseline — so every subsequent workload inherits policy instead of improvising it.

Network & identity design

Multi-region topologies, private connectivity (Direct Connect, ExpressRoute) and IAM/Entra models that are auditable end to end — least privilege without least usability.

Cost architecture

Right-sizing, committed-use strategy and per-service cost attribution from day one. We report the same numbers your finance team sees, and we flag drift early.

02 · distributed systems

Consistency, replication, and the art of the controlled failure

Our founding discipline. We design for partitioned networks and partial failure, then rehearse both until they stop being scary.

Consistency & replication

Eventual consistency as a deliberate choice, strong consistency where it matters — chosen per workload, never default-configured.

  • Event sourcing for extremely high resilience
  • Cross-region topologies with explicit RPO/RTO targets
  • Backpressure and degradation strategies that survive traffic spikes

Event-driven & messaging

Azure Service Bus, SNS/SQS, RabbitMQ and Kinesis architectures with ordered, idempotent, replayable pipelines — the backbone behind our ingestion, settlement and telemetry work.

  • Exactly-once semantics where it matters, best-effort where it doesn't
  • Integration strategies with existing systems you can't rip out
  • Replay and recovery runbooks, written before they're needed

Chaos & failure rehearsal

We don't hope for resilience, we exercise it. Zone kills, dependency black-holes and failover drills in staging — then in production at low blast radius.

  • Game days with defined SLOs and pass/fail criteria
  • Automated failover tests in CI, not just in the runbook
  • Incident retrospectives that change the system, not just the wiki

03 · migration & modernisation

Move the load, not the risk

Big-bang migrations are where projects die. Ours are incremental by construction: small reversible steps, dual-run verification, and a tested rollback for every cutover.

Assessment & sequencing

Dependency mapping, blast-radius scoring and a migration order that keeps the business running while the platform changes under it. You get a sequenced plan with cost and risk per step — not a gantt chart of hope.

Rehost, refactor, or both

We're honest about where each approach wins: lift-and-shift for the boring parts, strangler-fig decomposition where it earns the cost. Most engagements use both, in a deliberate order.

  • Strangler-fig decoupling of monoliths still serving traffic
  • Live data migration with dual-run and verification windows
  • Feature-flagged hot switching and branch by abstraction

Cutover & hypercare

Cutover is an engineering event, not a ceremony. We rehearse the cutover plan, define the abort criteria, and staff hypercare through the first incidents — because that's where the real bugs show up.

04 · platform & SRE

Make the right thing the easy thing

Platforms are for developers, and SRE is a measurement discipline. We build both around the same idea: reliability you can see, ship speed you can protect.

Internal developer platforms

Golden paths on Kubernetes or Azure AKS: one command to a preview environment, sane defaults for logging, tracing and alerts, and self-service that doesn't hand out root.

  • Environment-as-code with per-branch previews
  • Build and deploy pipelines with progressive delivery
  • Service templates that encode your standards, not your regrets

Observability

Metrics, traces and logs wired together from the start — Datadog, Application Insights or the ELK stack, with SLOs driving the alerts. If a page fires, a human should be able to name the service and the user impact within minutes.

SLOs, error budgets & incident response

Reliability as a number, not a vibe. We define SLOs with your product owners, manage error budgets as a real trade-off, and run incident response that produces fixes, not blame.

// how we work

A process with teeth

Four phases, each with a defined exit. You decide whether we've earned the next one.

Diagnose

Read the systems, the metrics and the incidents. We map the real architecture (not the wiki one) and name the three risks that actually matter.

1–2 weeks

Design

A written architecture decision record: targets, failure modes, cost model and a sequenced plan. Reviewed with your team until it survives argument.

1–3 weeks

Deliver

Small reversible increments, shipped to production behind flags. Every step has a rollback plan, and we don't declare done until the numbers say so.

ongoing

Operate

On-call coverage, SLO reporting and continuous failure rehearsal. We stay through hypercare and the first incidents — that's where the work is proven.

ongoing

// start here

Start with a diagnosis, not a commitment.

A paid, fixed-scope diagnostic gives you a written risk assessment and an architecture opinion. It's yours either way — and it's credited if you continue.

Request a diagnostic

· response within one business day