Full Stack Development

The problem

Front ends drift into inconsistent state handling, accessibility gaps and slow pages under real data volumes.

What we implement

Typed React applications with server rendering where it helps, a component system, predictable data fetching, accessibility as a requirement and performance budgets.

How it works

The type system extends from the API contract into the UI, so a backend change surfaces at compile time rather than in production.

Expected outcome

Interfaces that stay fast and consistent as features accumulate and the team changes.

The problem

APIs grow endpoint by endpoint until behavior, validation and error handling differ across the surface.

What we implement

Contract-first APIs with schema validation, consistent error semantics, authentication and authorisation, pagination, idempotency and versioning.

How it works

The contract is the source of truth: types, validation and client code derive from it, and breaking changes are visible in review.

Expected outcome

Consumers can integrate from documentation, and changes to the contract are deliberate rather than accidental.

The problem

Schemas designed for the first release become the main constraint on later features.

What we implement

Domain-aligned relational modeling, migration discipline, indexing and query review, caching where measurements justify it, and clear transactional boundaries.

How it works

Migrations are versioned, reviewed and reversible, and slow queries are found through instrumentation rather than user reports.

Expected outcome

A data layer that supports new features rather than dictating awkward workarounds.

The problem

Each new third-party system adds coupling, partial failures and data that is nearly but not quite consistent.

What we implement

Adapter boundaries per external system, idempotent operations, retry with backoff, dead-letter handling, reconciliation jobs and event-driven propagation where appropriate.

How it works

External failure is treated as normal: operations are safe to retry, and mismatches are detected by reconciliation rather than discovered by finance.

Expected outcome

Integrations degrade visibly and recover automatically instead of failing silently.

The problem

A legacy system is too risky to change and too central to replace, so it slowly blocks everything.

What we implement

Incremental modernisation: characterise current behavior, place a stable interface in front of it, carve out capabilities one at a time and retire legacy paths as traffic moves.

How it works

Traffic shifts gradually with the ability to route back, and each carve-out ships value on its own rather than waiting for a final switch.

Expected outcome

The legacy system shrinks steadily while the business keeps receiving changes.

The problem

Test suites that are slow or flaky get ignored, and release becomes the moment risk is discovered.

What we implement

A test pyramid matched to real risk, deterministic test data, contract tests at integration boundaries, and pipelines that build, migrate, deploy and roll back.

How it works

Feature flags separate deploy from release, so changes reach production early and are enabled deliberately.

Expected outcome

Releases become frequent and unremarkable, and regressions are caught by the suite rather than by users.