There's a version of the AI coding conversation that goes like this: AI generates sloppy code, engineers spend half their time fixing hallucinations, and the net effect is roughly zero. We've heard it from sceptical CTOs. We understand why they're sceptical.
That version isn't wrong about bad AI usage. It's wrong about what good AI usage looks like.
The mechanical work problem
Traditional software development is slow for a reason that has nothing to do with how hard the problems are. Most of the time, engineers aren't solving hard problems. They're writing boilerplate, looking up API documentation, wiring together components they've wired together before, and fixing the same categories of bugs they've fixed dozens of times.
AI is exceptional at the mechanical work. It's fast, consistent, and increasingly reliable at producing code that works. What it isn't — and what we don't expect it to be — is a substitute for architectural judgment, domain knowledge, or the ability to understand what a customer actually needs.
What changes when you combine both
When you give a senior engineer access to good AI tools, something interesting happens: the ceiling of what one person can deliver in a week moves dramatically higher. Not because the thinking gets easier, but because the time spent on mechanical work drops towards zero.
The engineer makes all the same important decisions. They review, evaluate, and take responsibility for the output. But they spend their time on those decisions rather than on the typing.
What enterprise teams should actually expect
We're not going to promise that AI development delivers ten times the output at one tenth of the cost for every project. That depends on what you're building.
What we can say, based on what we've built:
- Greenfield projects on modern stacks benefit enormously. There's very little prior art to navigate and a lot of boilerplate to skip.
- Integration work — connecting systems, building data pipelines, wiring APIs — is a sweet spot. The patterns are well-understood; the volume is high.
- Legacy modernisation is harder, because understanding the existing system takes time regardless of tooling. AI helps with the output but not the archaeology.
The honest version of the pitch is: for the right type of project, we can deliver faster and cheaper than the alternative. And we'll tell you upfront if your project is the right type.
The quality question
This matters. Code that ships quickly but fails in production isn't a win.
Our development process includes the same practices that any responsible engineering team would apply: code review, automated testing, structured architecture decisions, documentation. AI accelerates the writing; it doesn't replace the thinking.
If you're evaluating AI-assisted development for your organisation, the question to ask isn't "can AI write code?" It's "do the people using it have the expertise to know when it's right?"
We think they do.