AI-assisted delivery that clears review, not just the first draft.
Assistants raised how much code gets written. They did not raise how much gets checked, so the queue moved from writing to reviewing and the delivery metrics stayed where they were. The constraint moved. The tooling did not follow it.
- Verification happens as the work happens, so review scales with generation instead of queueing behind it.
- Every task runs the same governed path, whichever AI coding tools your team already uses.
- Cost measured per completed task, which is the only number that survives contact with a CFO.
Book a governed AI review.
Bring what you measure delivery on today. You leave with the specific stage where your review capacity is binding, and what would have to change for the tooling spend to show up in the numbers.
The tools worked. The delivery metrics did not follow.
Adoption is high, satisfaction is high, and lead time is roughly where it was. All three can be true at once, and the reason is structural.
Generation got cheap. Review did not
Writing code was never the slow part of a mature delivery pipeline. Speeding it up loads more work onto the stage that was already the constraint, and a faster feeder into a fixed bottleneck produces a queue, not throughput.
Larger changes, less context behind them
A reviewer reading generated code cannot ask the author what they were thinking, because the author does not remember thinking. So review gets slower per line at exactly the moment there are more lines.
The before state was never captured
Most teams cannot say what a unit of work cost before the tools arrived, which makes the return unprovable in either direction. That is a measurement failure, and it is fixable in a fortnight.
Output is not delivery. Merged, verified and running is delivery.
Every task runs the same governed path.
The point is not to slow generation down. It is to move the check next to the work, so review stops being a stage that everything waits in.
The change arrives against a spec
Work starts from an agreed specification rather than from a prompt, so there is a stated intent for the change to be judged against later.
AgentChecked while it is being written
Conformance to the spec, the blast radius of the change, and the tests that matter, all evaluated as the work happens rather than in a queue at the end of it.
IndependentAn engineer clears it, or sends it back
A person still signs. What changes is what they are handed: a change with its intent, its checks and its impact attached, instead of a diff and a hope.
HumanFive stages, and a person owns three of them.
Tool agnostic by design. This wraps the AI coding tools your team already chose, and it survives you changing them.
We would rather you tested the claim than took it.
Engineering leaders discount vendor delivery statistics, correctly. So the proof here is a calculation you run on your own numbers and an argument you can disagree with.
Cost per completed task, on your figures
Arithmetic on numbers you supply, with no benchmark and no vendor pricing in it. The success rate field is the interesting one: you cannot fill it in unless something checked the outputs.
Why AI coding tools are not moving delivery metrics
The full case for the position on this page, including the part where we say what would have to be true for us to be wrong.
Five things you can hold us to
Written as commitments rather than capabilities, because a capability cannot be breached and a commitment can.
Thirty minutes, and three things you did not have before.
This is a working session, not a discovery call that ends in a proposal you did not ask for.
- Where your constraint actually isThe stage that is binding in your pipeline right now, which is usually review capacity and occasionally environment access, and almost never typing speed.
- A baseline you can defendWhat a unit of work costs you today, defined so that the same measurement still works in six months when someone asks what the tooling bought.
- A scoped next stepThe smallest change to the path that would move the binding stage, with what it takes. If your bottleneck is not something we address, we say that.
What engineering leaders ask on the first call.
No. This wraps whatever your team chose and survives you changing it, which matters because that market turns over faster than any procurement cycle. The governed path is about what happens around generation, not about which model writes the first draft.
The opposite. A person still clears every change. What changes is what they are handed: the intent it was written against, the checks that already ran, and what the change can reach. Review stops being the place where all the context has to be rebuilt from scratch.
Cost per completed task and review wait time, both captured before we change anything. If the baseline does not exist yet, establishing it is the first fortnight of the work, because a return you cannot measure is a return you cannot defend when the budget is questioned.
That is the normal starting position, and it is an argument for verification rather than against it. Where test coverage is thin, the conformance check against a stated spec is doing more of the work, not less. What we would not do is point generation at a system with no checks and call it acceleration.
You do, throughout. The path we put in place runs in your environment on your tooling, and it keeps working if we leave. That is a deliberate structure rather than a promise about our intentions.
Model it against your own numbers, then bring us the result.
Run the numbers first if you want the conversation to be concrete. Thirty minutes, and we will tell you where your constraint really is.