Service

Cloud you can reason about under pressure

Cloud architecture, API contracts and the pipelines between them — designed so the bill, the blast radius and the recovery path are understood before the first customer arrives, not at three in the morning.

The Problem

The seams are where it breaks

Cloud trouble is rarely the platform itself. It is the joins — what one service promises another, what happens when that promise breaks, and who is watching the meter.

  1. 01

    Cost that arrives as a surprise

    Resources provisioned during a launch push and never revisited. The invoice is one number nobody can attribute to a team, an environment or a feature, so nothing ever gets turned off.

  2. 02

    Interfaces with no owner

    A required field appears on an endpoint one morning. There is no versioning policy and no contract test, so the first people to notice the break are customers.

  3. 03

    Failure paths nobody has walked

    Retries quietly swallow the error, the alert fires for something harmless, and the restore procedure has never been run end to end. The first rehearsal happens during the incident.

The Approach

Boundaries first, then build

We treat the boundary between two systems as a deliverable in its own right — owned, documented and tested — and we model the running cost while the architecture is still on paper.

  1. 01

    Contracts before code

    The interface is agreed, versioned and testable first. Schemas live in the repository, and a breaking change fails the pipeline instead of a customer's integration.

  2. 02

    Environments rebuilt from source

    Infrastructure defined in code and applied by automation, so staging is production at a smaller size and every change to either one is a pull request somebody read.

  3. 03

    Decide how it degrades

    Timeouts, retry budgets and circuit breakers are design decisions, not patches applied after the first outage. We agree in advance what the product should still do while a dependency is down.

Capabilities

What we build

Most of this arrives as one engagement rather than three vendors, which is why the integration work lands inside the team that built both sides of it.

  • Cloud architecture

    Compute, storage, networking and identity chosen for the workload in front of us rather than for the diagram. Managed services where they earn their margin, plain servers where they do not.

    • Workload design
    • Networking
    • Identity
  • API platforms

    REST and GraphQL surfaces with versioning, authentication, rate limits, pagination and errors that say what went wrong. Documentation generated from the schema, so it cannot drift away from the code.

    • REST & GraphQL
    • Versioning
    • Auth & limits
  • Event pipelines

    Queues, streams and webhooks with the delivery guarantee stated out loud — at-least-once with idempotent consumers, ordering only where it is genuinely required, dead letters someone reads.

    • Queues & streams
    • Idempotency
    • Dead letters
  • Delivery pipelines

    Build the artefact once, then promote that same artefact through each environment. Migrations are versioned with the code, and rolling back is a command rather than a rescue operation.

    • CI/CD
    • Environments
    • Rollback
  • Systems integration

    Connecting the systems you already pay for — an ERP, a CRM, a payment provider, and the legacy endpoint with no sandbox and a vendor who answers by email.

    • Third-party APIs
    • Legacy adapters
    • Data mapping
  • Observability and spend control

    Logs, metrics and traces tied to things a user would actually notice, so alerts describe symptoms rather than servers. Resources tagged and budgeted, so cost can be traced to a team or a feature.

    • Tracing
    • Alerting
    • Cost attribution

Technologies

Tools we reach for

Chosen per workload rather than per habit. If your team already runs something well, keeping it is usually cheaper than the migration we could sell you.

Cloud & runtime

  • AWS
  • Azure
  • Google Cloud
  • Kubernetes
  • Docker
  • Cloudflare

Infrastructure as code

  • Terraform
  • Pulumi
  • AWS CDK
  • Ansible
  • Helm

APIs & messaging

  • OpenAPI
  • GraphQL
  • gRPC
  • Kafka
  • RabbitMQ
  • Redis

Delivery & observability

  • GitHub Actions
  • GitLab CI
  • OpenTelemetry
  • Prometheus
  • Grafana
  • Sentry

How we work

From estate to environment

  1. Step 01

    Map the estate

    We inventory what is already running: services, data stores, third-party accounts, credentials, scheduled jobs and the integrations nobody documented. Surprises are far cheaper here than later.

  2. Step 02

    Pin the interfaces

    Payloads, error semantics, versioning policy and ownership are written down while both teams can still argue about them cheaply. After that, the two halves proceed in parallel.

  3. Step 03

    Lay the ground

    Accounts, networks, secrets management and a deployment that runs end to end on day one, while it is still boring. Every change after that is small enough to review.

  4. Step 04

    Break it before launch

    Load, dependency failure and a full restore run against the real topology. Alerts are tuned until each one means something, and the runbook is written by the people who will carry the pager.

  5. Step 05

    Hand over the keys

    Diagrams that match what is deployed, credentials assigned properly, and a walkthrough of the pipelines and dashboards with your engineers. You should be able to run this without us.

Tell us what is already running

Bring the architecture diagram, the invoice, or just the integration that keeps breaking. We will tell you which of the three is the real problem.