Services
What I do, and what you actually get
Every engagement below is scoped to a concrete outcome with deliverables you can point at. If what you need sits between two of these, that's normal — say so and we'll shape it.
Product & Application Development
Full-stack delivery of web applications and the services behind them — from a greenfield build to adding a feature to a codebase you already own.
Typical deliverables
- Web applications built with TypeScript, React, and Next.js
- APIs, integrations, and data models to support them
- Incremental delivery against a backlog you can see and reprioritise
- Code review and handover so your team can carry the work forward
QA & Test Automation
Automated test suites that catch real regressions and stay green — built to be maintained by your team, not abandoned after handover.
Typical deliverables
- End-to-end suites in Playwright for web, Appium for mobile
- Page-object and fixture structure that survives UI churn
- Flake triage: quarantine, root-cause, and fix rather than retry
- Coverage mapped to the user journeys that actually matter
CI/CD & Release Engineering
The pipeline between a merged pull request and production — fast, repeatable, and trustworthy enough that shipping stops being an event.
Typical deliverables
- GitHub Actions pipelines: build, test, and deploy gates
- Preview environments per pull request
- Parallelised and sharded test runs to keep feedback under ten minutes
- Release checks, rollback paths, and post-deploy verification
Quality Strategy & Fractional QA Leadership
For teams with no QA function, or one that has outgrown its process. An honest assessment of where quality actually breaks down, and a plan to fix it.
Typical deliverables
- Audit of current testing, tooling, and release practice
- A prioritised roadmap with effort and impact against each item
- Test strategy: what to automate, what to leave manual, and why
- Ongoing fractional leadership as your team builds the capability
AI-Leveraged Engineering & Automation
Using AI where it measurably speeds delivery — drafting code, generating and triaging tests, removing manual steps — with the review gates, guardrails, and honesty about its limits that keep the output trustworthy.
Typical deliverables
- AI-assisted development and code review wired into your existing workflow
- Test generation, failure triage, and flake clustering at a scale manual QA can't reach
- Agent and MCP tooling built around your codebase, docs, and internal systems
- Evaluation harnesses for AI features you ship, so changes are measured rather than guessed at
- A clear map of where AI belongs in your pipeline — and where it costs more than it saves
Engagement models
How these get packaged
The same services, shaped to fit the size of the problem and how long you need someone around.
Assessment
1–2 weeks
A focused review of your codebase, test suite, and release process, ending in a written report and a prioritised roadmap. A good starting point if you know something is wrong but not what.
Project delivery
Typically 4–12 weeks
A defined scope with a clear finish line — a test suite built, a pipeline rebuilt, a product feature delivered end to end. Fixed scope, regular demos, working software at each step.
Fractional / ongoing
Monthly, rolling
Part-time senior capacity embedded with your team — reviewing, building, and raising the engineering baseline over months rather than weeks. Cancel or pause with notice.
Not sure which of these you need?
That's what the first conversation is for. Describe the symptom — flaky releases, slow delivery, a test suite nobody trusts — and I'll tell you honestly whether it's something I can help with.