I used to be skeptical about AI in development. The early outputs felt rushed, fast, but fragile. Over time, though, I started to see a different path: one where AI isn’t cutting corners, but helping us move with structure.
At OrbiQ, we’ve gradually integrated AI into about 60% of our development flow. Not everything, not all at once, and never without safeguards.
Here’s what’s worked for us:
- AI supports the repetitive; humans guide the architecture
We lean on AI for things like boilerplate, test scaffolding, and CRUD logic, the kind of tasks that follow a pattern. But the decisions that shape systems? Those still sit with us.
- Verification is everything
No AI code hits production untested. We run linters, static analysis, and aim for 95%+ test coverage. Then a human reviews it, with a focus on context and edge cases.
- Start small, learn fast
We began with low-risk components, tracked results, and only expanded where the data made sense. Today, our defect rate is actually lower than it was pre-AI.
The main takeaway: AI isn’t a substitute for engineering discipline. It can be a powerful partner, if you bring the right structure.
In our new guide, we’ve documented:
- Where AI helps most, and where it struggles
- How to review, test, and deploy AI code responsibly
- Practical workflows for solo devs and scaling teams alike
