Every client conversation this year has included some version of the same question: "Are you using AI to build this?" The honest answer is yes — but probably not in the way most people picture it. AI hasn't replaced engineering judgment on our projects; it's changed which parts of the job take an hour instead of a day.
Here's a straightforward look at where AI tooling has actually earned a place in our workflow, and where it hasn't.
Where it genuinely helps
Scaffolding and boilerplate
Setting up a new project's folder structure, config files, and repetitive component patterns used to eat the first day of every engagement. AI-assisted tooling now handles most of that groundwork in minutes, which means our engineers start writing the parts of the code that actually differentiate the product on day one instead of day three.
First-pass code review
Before a pull request reaches a senior engineer, an AI pass catches obvious issues — unused variables, missing error handling, inconsistent naming. It's not a replacement for human review, but it means the humans spend their time on architecture and logic instead of style nits.
Documentation
Writing clear internal documentation is the task every engineering team says they'll get to and rarely does. AI-generated first drafts, edited by the engineer who wrote the code, have meaningfully improved how well-documented our handoffs are.
The gain isn't that AI writes better code than our engineers. It's that it removes the tedious 20% of the job so engineers spend more time on the 80% that requires actual judgment.
Where it doesn't help — and where it can hurt
Architecture decisions
Choosing how a system should be structured requires understanding a client's business, not just their codebase. This is where senior engineering experience still does the heavy lifting, and where we've seen AI-generated suggestions confidently recommend patterns that don't fit the actual constraints of a project.
Security-sensitive code
Authentication flows, payment handling, and access control get written and reviewed entirely by senior engineers. The cost of a subtle mistake here is too high to delegate, even partially.
Client-facing product decisions
What a feature should do, how it should feel to use, and whether it's worth building at all — these are conversations with humans, not prompts. No tooling changes that.
Our actual rule of thumb
If a task is mechanical and its output is easy to verify at a glance, AI tooling probably has a role in it. If a task requires weighing trade-offs specific to a client's business, it doesn't. That distinction has kept our delivery timelines shorter without lowering the bar on the work itself.
We'll keep updating how we use these tools as they improve — but the standard stays the same: every line of code that ships to a client gets reviewed by a senior engineer who understands why it's there.