Code Generation

Only if it's set up to — we configure the generation tooling with your actual style guides, existing code patterns, and architectural conventions as context, rather than letting the model generate generic code that then needs heavy rework. Without that setup, teams usually end up spending more time fixing generated code than they saved.

Generated code goes through the same review process as human-written code — linting, tests, and human code review — we don't treat AI output as exempt from your existing quality gates. We also scope which tasks are good fits for generation (boilerplate, tests, scaffolding) versus which need a human writing the core logic from the start.

Repetitive, pattern-based work — test scaffolding, boilerplate, API clients, straightforward migrations — sees the best return, because the AI has clear examples to follow and low ambiguity. Core business logic and architecture decisions are where we recommend keeping AI in an assistive role rather than a generative one.

Both, depending on your needs — sometimes the right move is configuring and rolling out an existing tool properly with your codebase context and team guidelines, and sometimes you need custom tooling for a specific repetitive task that off-the-shelf tools don't handle well. We'll tell you honestly which situation you're in during scoping.