AI-Assisted Development

AI-assisted development is genuinely the largest change to how software gets built in a decade. Working prototypes in hours, feature branches in an afternoon, test suites and documentation that no longer get skipped for lack of time. We use it throughout our work and we are open about that, because the alternative — quietly shipping generated code while implying it was all hand-written — is what has given the practice its reputation.

What AI has not changed is what makes software survive: architecture that holds under growth, code somebody has actually read, tests that assert real behaviour, and a security review before it reaches the internet. Generated code without that layer is not faster development. It is technical debt arriving sooner.

What AI genuinely accelerates

• Prototyping. Several approaches built and compared in the time one used to take.
• MVP development. First versions in front of real users in weeks.
• UI generation. Interfaces from a description, then refined against a real design system.
• Feature development. Boilerplate, CRUD and wiring produced in minutes.
• Test generation. Coverage written alongside the code rather than deferred indefinitely.
• Debugging. Stack traces and unfamiliar code explained far faster than reading cold.
• Documentation. Kept current because updating it is no longer an afternoon.
• Refactoring. Mechanical changes applied consistently across a large codebase.
• Migration. Version upgrades and framework moves with the tedium removed.

What still requires engineers

• Architecture. A model will happily generate a design that cannot scale past its first thousand users.
• Code review. Every diff read by a person who understands the system it is joining.
• Security. Generated code reproduces the vulnerabilities in its training data — injection, broken access control, leaked secrets.
• Testing. Tests that assert real behaviour, not tests that assert the implementation back at itself.
• Scalability. Query patterns, indexing and caching under load the prototype never saw.
• Performance. Profiling and optimisation against measurements rather than intuition.
• DevOps. Pipelines, environments, rollback and monitoring.
• Production judgement. Knowing which shortcuts are survivable and which are not.

Who this is for

Founders who need to move at startup speed without accumulating a codebase that has to be thrown away. Companies who have had something vibe-coded and now need it made production-safe. Teams adopting AI-assisted development internally who want the review practices that keep it honest.

Problems we solve

• Prototypes shipped as products. No tests, no migrations, no auth model, already taking payments.
• Code nobody understands. Generated, merged, never read — and now unmaintainable.
• Security holes at scale. The same vulnerability replicated across thirty generated endpoints.
• Inconsistent architecture. Four different patterns for the same problem in one codebase.
• Tests that prove nothing. High coverage numbers asserting the implementation rather than the requirement.

How we work

We set the architecture first, because that is what AI is worst at and what costs most to change later. Generation happens inside those boundaries. Every diff is reviewed by an engineer who can explain it. Tests are written against requirements, not generated from the implementation. Security review, dependency scanning and CI run on every change. The result is startup speed with the engineering discipline that lets version two build on version one instead of replacing it.

Technologies we use

Claude and Claude Code, GPT and comparable assistants, inside a normal professional toolchain: Git with reviewed pull requests, automated test suites, GitHub Actions for CI, static analysis, dependency and secret scanning, containerised environments and staged deployment with rollback.

Business benefits

• Substantially shorter time from idea to something real users can try
• Lower build cost without the maintenance bill that usually follows cheap builds
• Test coverage and documentation that actually get written
• Faster iteration once live, because the codebase stayed comprehensible

Common questions

Is AI writing our software?

AI generates a meaningful share of the code, and engineers design, review, test and are accountable for all of it. We would rather state that plainly than let you find out later.

Is generated code lower quality?

Unreviewed generated code is poor. Reviewed generated code, held to the same standards as anything hand-written, is not — and it arrives sooner. The review layer is the whole difference, and it is what you are paying for.

Can you fix something we vibe-coded ourselves?

Often, and it is common work. We audit architecture, security and test coverage, give you a written assessment of what is salvageable and what is not, and stabilise from there. Sometimes the honest answer is that rebuilding the core is cheaper than repairing it, and we will say so.

Does using AI make it cheaper?

It reduces build time, which reduces cost. It does not remove the review, testing and infrastructure work, and anyone pricing as though it does is selling you the prototype.

Need to move fast without building something disposable? That balance is the entire point of how we work.