AI-native software development is now a claim nearly every vendor makes. It answers one question, whether AI gets used, and that stopped being a useful question a while ago. In Stack Overflow’s 2025 developer survey, 84% of respondents were using or planning to use AI tools. In the same survey, more developers distrusted the accuracy of those tools (46%) than trusted it (33%).
So ask a different question on your next vendor call. At what point is a person required to stop this, and what has to be true before the work moves on? A vendor with an answer will name the role and the moment. A vendor without one will talk about speed.
There is research behind being picky. Google’s 2024 DORA report estimated that as AI adoption rose, delivery throughput fell 1.5% and delivery stability fell 7.2%. Its conclusion was that a better development process does not automatically improve delivery unless the basics hold, such as small batch sizes and robust testing.
This post names our stops: three inside the workflow, and one before it that belongs to the client. It covers what each one is for and what they do not prove. It says very little about AI, because the AI is the less interesting part.
The project lead approves the technical plan before implementation. After execution, work goes through senior code review, build verification and testing. No code ships until every stage passes. Before any of that, the client approves the fixed price.
The workflow, with the stops marked
Every task moves through six phases: scope assessment, research, planning, execution, validation and documentation. AI is an execution and research layer under senior engineering supervision, never a replacement for engineering judgment. We have run it in production for over four years.
Stop 1: the plan, before anything is built
The project lead approves the technical plan before implementation begins. This is the cheapest stop in the process and the easiest one to skip. Caught here, a wrong approach costs a conversation. Caught after execution, it costs the conversation plus all the work built on it.
AI makes that trap worse. The faster a plan can be carried out, the more finished-looking work can pile up on a bad decision before anyone questions it.
Approval only means something if the approver can refuse. A plan worth signing should say what is changing, what it touches, what could break and how anyone will know it worked. Ask any vendor who approves the plan, by role, and what happened the last time that person said no.
Stop 2: senior code review
Every project is led by a senior engineer, and every release passes senior code review. Generated code makes a reviewer’s job harder than it looks. It tends to be tidy and consistent, and it reads as if someone thought about it. There is also nobody to ask why a particular line exists.
A lazy review waves that through on appearance. A real one reads for behavior: whether the code does what the plan said, what happens on bad input, and what else it quietly changed. A test can tell you the code does what the test says. Somebody still has to judge whether that was the right test.
Stop 3: nothing ships until every stage passes
All work passes build verification, code review and testing, and no code ships until every stage passes. Build verification and testing are checks a machine can run, and we are not claiming a person runs each one by hand. The human part of this stop is the rule. A failed stage holds the release, and a close deadline does not change that.
Here is the test for any vendor. What happens when validation fails two days before a launch, and who is allowed to override it? If the honest answer is “the account manager,” you have found out how the pipeline really works.
What it looks like without the stops
HeartMed’s case study has an instructive example. Before the engagement, its billing process relied on five disconnected spreadsheets and a homegrown tracker built with AI assistance. The tracker was collapsing under volume. Carry-forward formulas broke, trigger dates were not captured and performance degraded with real patient loads.
That is no knock on HeartMed, which had a prototype running when it needed one. It shows what AI assistance gets you by default: something that runs on the data it has seen so far. Whether it survives 4,000+ patients across 50 facilities is a separate question, and validation exists to ask it. The published result of the system that replaced it is $200K+ in monthly revenue recovered, as the HeartMed case study sets out.
The stop that belongs to you
One more stop comes before all of these. Every build starts with a written spec, a clickable prototype and a fixed price you approve before anyone writes production code. Nothing runs on your product until you have said yes to what it is going to build.
We also work inside your GitHub, Jira and CI/CD from day one, so the work sits in tools you can open yourself. That is the check to apply to any vendor. Can you see the stops, or do you only hear about them?
What the stops do not prove
Stops lower risk. They do not make software free of defects, which is why every build also includes a guarantee period where defects in the delivered scope are fixed at no charge. See the Software Development page for how that works.
They also say nothing about how good the reviewer is. A stop is only as strong as the person holding it, so ask who that person is and what they have built.
Five questions for any vendor who says AI-native
Our answer to the last one: all AI tooling and usage is included in our rates, never billed separately.
Frequently asked questions
What does AI actually do in your workflow?
It is an execution and research layer under senior engineering supervision. It does not replace engineering judgment, and every project is led by a senior engineer.
How long have you worked this way?
We have run AI-assisted engineering in production for over four years.
Do these stops mean there will be no bugs?
No. They are there to catch problems before release. Defects in the delivered scope that surface afterward are fixed at no charge during the guarantee period.
Our product was built quickly with AI tools. Where do we start?
Get an outside read. The free technical audit is a scored review of your existing product across seven areas, with a written findings report and severity levels. It takes one to five days and costs nothing.
If you want your own code checked
If nobody impartial has ever reviewed your code, that is where to begin. The free technical audit covers security, architecture, code quality, performance, test coverage, database and backend, and scalability. If you are planning a build, a call is the place to start.
Take the free technical audit of your existing product, or book a call about a new one.
Workflow details come from the Touch Support Software Development page. Survey figures are from Stack Overflow’s 2025 Developer Survey (33,244 responses on the trust question). Delivery figures are from Google Cloud’s announcement of the 2024 DORA report. HeartMed details are as published.
