A working catalogue of what the studio can be asked to do.

What it includes

• —Discovery, domain modelling and architectural design.
• —Backend services, APIs and internal tooling.
• —Data storage, queues, background workers and integrations.
• —Deployment pipelines and observability from day one.

Problem addressed

Off-the-shelf products stop fitting once an organisation's operations mature. A custom system removes the accumulated workarounds and returns control of the workflow to the people using it.

How we deliver

Every project begins with a written framing document and a working code repository within the first two weeks. From that point onwards the client sees the system evolve in reviewable increments, with weekly demos and an environment they can use at any time.

Typical outcome

A system whose behaviour, data model and operational cost are understood by the team responsible for it — with the tests, documentation and monitoring needed to keep it that way.

What it includes

• —Marketing and editorial sites with content operations behind them.
• —Product web applications and complex interactive interfaces.
• —Performance, accessibility and search-engine fundamentals.
• —Content models designed with editors, not for them.

Problem addressed

Many websites are built once, launched, and then quietly decay because no-one owns the underlying content system. We build sites that can be maintained by the people who write for them without engineering assistance.

How we deliver

Structure and content model first, then design, then implementation. The site launches with documented editorial workflows and monitoring, not with a handover PDF.

Typical outcome

A site that loads quickly, ranks fairly, is accessible, and can be updated confidently by the team responsible for its content.

What it includes

• —Product discovery, user research and problem framing.
• —Information architecture and interaction design.
• —Visual design systems, typography and component libraries.
• —Design tokens and design-engineering handoff.

Problem addressed

Interfaces get harder to change as they grow. Without a coherent structure, small additions compound into inconsistency, which is expensive to unpick later.

How we deliver

We treat the design system as a working piece of software rather than a Figma library. Components, tokens and patterns live where the engineers can use them, and the design and engineering roles share ownership of them.

Typical outcome

A coherent interface that stays coherent as it grows, with a documented system that new contributors can extend without breaking the rest.

What it includes

• —Infrastructure as code on AWS, GCP or comparable providers.
• —Container orchestration, queues and managed data services.
• —Observability: logs, metrics, traces and alerting.
• —Cost review and right-sizing of existing environments.

Problem addressed

Cloud environments quietly accumulate complexity and cost. Without a deliberate approach they become both expensive to run and difficult to change safely.

How we deliver

Everything is defined in code, reviewed like code, and rolled out through reproducible pipelines. Observability is a starting requirement, not something added after an incident.

Typical outcome

An environment the team understands, can reproduce from scratch if necessary, and can operate at a predictable monthly cost.

What it includes

• —API design and documentation for external and internal consumers.
• —Integration with SaaS vendors, payment providers and internal systems.
• —Data synchronisation, event streams and reconciliation.
• —Rate limiting, retries, idempotency and audit logging.

Problem addressed

Integrations tend to be treated as glue and written accordingly, which is why so many of them fail silently. We treat them as first-class systems with their own tests, monitoring and failure modes.

How we deliver

Each integration is documented as a contract with clear inputs, outputs, error responses and retry semantics, with automated tests that exercise the real behaviour, not only happy paths.

Typical outcome

Integrations that fail loudly when they fail, recover automatically where possible, and can be reasoned about by anyone who inherits them.

What it includes

• —Workflow automation across internal tools and third-party services.
• —Scheduled jobs, event-driven pipelines and background processing.
• —Human-in-the-loop steps where discretion is required.
• —Audit trails and monitoring for every automated action.

Problem addressed

Automation that is poorly built creates a new class of problem: it hides mistakes at scale. Good automation reduces work without reducing visibility into what happened and why.

How we deliver

Every automated action is logged, every failure is surfaced, and every process is documented so the team using it understands how it behaves at the edges.

Typical outcome

A measurable reduction in manual, repetitive work — with a clear audit trail that regulators, auditors and colleagues can all follow.

What it includes

• —Architectural review of an existing or proposed system.
• —Vendor and platform selection reviews.
• —Hiring support: role definition, interviewing and calibration.
• —Fractional engineering leadership for defined periods.

Problem addressed

Internal teams sometimes need an outside view — not to replace their judgement, but to test it. Consulting engagements are structured to give that perspective without disrupting the work already in flight.

How we deliver

Consulting engagements produce written artefacts: reviews, recommendations, and follow-up questions. The client is left with something they can act on independently once the engagement ends.

Typical outcome

A clearer picture of the options in front of the team, with the reasoning behind each recommendation documented and defensible.

What it includes

• —Automated testing at meaningful boundaries, from unit to end-to-end.
• —Manual exploratory testing where judgement outperforms scripts.
• —Load and performance testing before major launches.
• —Regression suites for critical revenue and safety paths.

Problem addressed

Quality assurance is often bolted on late, which is when it is most expensive and least effective. Building it into the delivery process from the start is meaningfully cheaper over the life of a system.

How we deliver

We help teams choose the smallest set of tests that would actually catch the failures they care about, and we build the tooling required to make those tests fast enough to run on every change.

Typical outcome

A codebase that can be changed with confidence, with a test suite that is trusted by the people running it rather than routinely ignored.

What it includes

• —Bug triage, patching and release management.
• —Dependency and framework upgrades on a defined schedule.
• —Incident response and post-incident reviews.
• —Ongoing observability, cost and performance tuning.

Problem addressed

Software rots when left alone. Dependencies drift, security advisories accumulate, and small performance regressions become large ones. Maintenance is the discipline of preventing this quietly.

How we deliver

Maintenance engagements run on defined cadences with a shared backlog. Nothing is done in secret; the client sees the same list of work the team does.

Typical outcome

A product that stays as reliable, secure and fast in year three as it was on the day it launched.

What it includes

• —Assessment of the existing system and its constraints.
• —Strangler-pattern migrations, service by service.
• —Data migration with reconciliation and rollback plans.
• —Cutover strategies designed to be reversible.

Problem addressed

Full rewrites are the highest-risk change an organisation can make to its software. Most of them are avoidable when the system is modernised in reviewable increments instead.

How we deliver

We prefer to keep the existing system running while new capabilities are built alongside it, moving traffic across in small, reversible steps. Every migration ends with a documented cutover plan and a well-rehearsed rollback.

Typical outcome

A modern system in production with the legacy system decommissioned deliberately, not abandoned — with data migrated, verified and understood.