vibencode
AI-powered development
Can we build the software itself faster?
“We should be using AI in engineering.”
AI in planning, review and test generation, with repository context, an audit trail on every suggestion, and the merge gate exactly where it was.
Typing was never the bottleneck. The time goes on finding out how the thing already works: which service owns this, why that guard exists, what broke the last time somebody touched it. That is a comprehension problem, and it is the one worth pointing a model at. It is also why buying everyone a licence changes so little: the tool has to be able to read your repository and its history, and the review path has to come through the arrangement intact. Most of the list below is those two things.
- 01Build the product: full-stack and SaaS delivery, the work we have always done, now run with agents in the loop.
- 02Agent-assisted planning, implementation, test generation and review, with repository context rather than guesswork.
- 03The merge gate stays exactly where it was. Review does not get looser because the author was a machine.
- 04An audit trail on every suggestion that reached the codebase.
- 05Set the same workflow up inside your team, so the practice does not leave when we do.
- Development workflowWhere a suggestion enters, who reviews it, and what has to be true before it can merge.
- Agent & toolchain integrationRepository context wired into the tools your engineers already have open, not a separate place to go.
- Team enablementYour engineers running it without us, and a written account of what to do the day it is confidently wrong.
The bottleneck was never typing. It was finding out how the thing already worked.
Watch
Where the time actually goes, measured by sitting with engineers rather than by asking them.
Baseline
Cycle time, review latency and rework, recorded before anything changes. Otherwise the result is an opinion.
Wire it in
Repository context, generation where it pays, an audit trail on every suggestion, and the review path untouched.
Hand over
Your team runs it. We write down what to do on the day it is confidently wrong.
That is the shape of a full engagement. If it is more than you want to commit to yet, the same people will spend two days on one piece of it first, and you are free to stop there. Start a 48-hour sprint
- A repository we can readHistory included. Comprehension is what is being accelerated, and history is where it lives: the reverts, the hotfixes, the guard nobody removes.
- Two or three engineers who will use it dailyNot a champion who demos it. The people who will hit the annoying parts in week two and say so, which is the only useful feedback.
- Your current numbersCycle time, review latency, rework. If they do not exist we measure them first; without a baseline the result is only an opinion.
- A position on what may be generatedTests, migrations, production code. Someone senior holds that line, and it wants writing down before the first suggestion lands.
What actually is AI-powered development?
Running your own software delivery with agents in the loop: planning, implementation, test generation and review, all with your repository and its history as context. It is a change to the workflow rather than to the tool list, and the part that makes it work is unglamorous: what the agent is allowed to see, where its output enters review, and a gate that does not move because the author was a machine.
Is this just buying an AI coding assistant for everyone?
No. And if that genuinely is all you need, we will say so on the first call rather than sell you eight weeks. The work here is the repository context, the review path and the measurement around them.
Does this replace engineers?
It has not in any engagement we have run. It moves where their time goes: less reconstruction of how things work, more judgement about what to change. That is exactly why comprehension is the thing we measure.
Will our code leave our environment?
That is a decision, not a default. If it cannot leave, the constraint shapes the toolchain from day one, which is a sovereign deployment question and gets answered first.
What about the code-quality argument?
It is a real argument, and it is the reason the merge gate does not move. We also measure rework after merge, because that is where quality problems actually surface rather than in review.
We build software for our customers, not for ourselves. Does this apply?
Yes. This is that work. Full-stack and SaaS delivery is where the company started; it now runs with agents in the loop. You can hire us to build the product, not only to change how you build it.
This is about how you build your own software. If what you want is an AI capability inside the product your customers use, that is a different engagement. That is GenAI implementation.
Can we build the software itself faster?
Two days in your repository. Written up at the end.