AI-native development, with engineering rigor
We build software with AI in the loop from the first commit — and put senior review, tests, and security gates around it so speed does not cost you correctness.
Every AI-assisted change passes a person, a test suite, and a security gate before it ships.
- AI-assisted implementation
- Human review gates
- Generated test coverage
- Best when
- You need to ship faster without adding headcount, and you want the guardrails that make that safe.
- Not a fit when
- You want code generated without review, or a prototype shipped as it stands.
- First thing you get
- A working slice of your product, reviewed and tested, inside the first two-week cycle.
What is AI-native development?
AI-native development means building software with AI coding tools in the loop from the first commit, generating code, tests and documentation, while engineers own the design and review every change. Done carelessly, it is vibe coding. We do it with senior review, automated tests and a security gate on every change, whoever wrote it.
Vibe coding — describing what you want and letting an AI write it — genuinely works. It is also how a lot of unmaintainable software is being shipped right now. Both things are true, and the difference is entirely in what surrounds the AI.
We use AI across the whole delivery cycle: exploring approaches during discovery, generating implementation, writing test coverage, drafting migrations, reviewing diffs. What we do not do is ship what it produces unexamined. Every change passes senior human review, an automated test suite, and a security pass before it reaches your branch.
The honest pitch is this: AI makes the first eighty percent dramatically faster. The last twenty — the correctness, the edge cases, the things that break at 2am — still takes experienced engineers. We charge you for the second part and pass on the savings from the first.
What this includes
- 01
AI-assisted implementation
Feature work built with AI pair-programming under a named senior engineer who owns the outcome.
- 02
Human review gates
No AI-generated change reaches your codebase without a human reviewing it for correctness and maintainability.
- 03
Generated test coverage
Tests written alongside the code, so regressions surface in CI rather than in production.
- 04
Automated security review
Auth, data handling, and dependency checks run on every change, not once before launch.
- 05
Architecture you can read
We keep boundaries explicit and documented, because AI-written code that nobody understands is technical debt with extra steps.
- 06
Prototype to production
If you have a working AI-generated prototype, we can harden it into something maintainable rather than starting over.
Situations we see most often
You need to move faster without hiring
A small team with a long roadmap. AI-assisted delivery lets a senior engineer cover meaningfully more ground per sprint without dropping the review standard.
You have a prototype that works but should not ship
Someone vibe-coded a working demo and now the business wants it live. We audit it, restructure what needs restructuring, and add the tests and security work it never had.
You want AI in your process, not just your product
We set up the tooling, review conventions, and CI gates so your own engineers get the speed benefit safely — then hand it over.
You are rebuilding something legacy
AI is unusually good at reading unfamiliar code and drafting migrations. Under supervision, it turns a six-month rewrite into something considerably shorter.
What to know before you start
How is AI-native development different from vibe coding?
Vibe coding means describing what you want, letting an AI write it, and shipping whatever comes back. It is genuinely fast, and it is how a lot of software nobody can maintain is being produced right now. AI-native development uses the same tools, with engineers owning the design and every change passing review, tests and a security gate before it reaches your branch.
The speed comes from AI writing the routine parts. The quality comes from people deciding the architecture and checking the result.
Can a working prototype be turned into production software?
Often, yes, and without starting over. We review the prototype for architecture, security and data handling, keep what is sound, and rebuild the parts that would not survive real users: authentication, error handling, data validation and tests.
The prototype is valuable as a specification of what you want. It just needs the engineering around it that production requires.
What does a two-week cycle look like with AI in the loop?
Engineers plan the cycle's work and design each change. AI assistants draft implementation, tests and documentation; engineers review, correct and refine. Every change runs through automated tests, linting, type checks and security scanning before a senior engineer approves it.
At the end of the cycle you see working software in a demo, as in any well-run project. The difference is how much more gets through review in the same two weeks.
What drives the cost of AI-native development?
AI compresses implementation time. It does not remove the rest of the work, so these factors still decide the cost.
| Factor | Effect | Why it matters |
|---|---|---|
| Product scope | High | Features, roles and integrations still need designing, building and testing, whoever types the code. |
| Discovery and architecture | High | Deciding what to build and how it fits together is human work that AI does not shorten much. |
| Review and testing depth | Medium | Every change is reviewed and tested; regulated or high-risk domains need more of both. |
| Existing code quality | Medium | Extending a messy prototype or legacy codebase needs clean-up before AI assistance pays off. |
| Security requirements | Medium | Automated scanning is standard; sensitive data adds threat modelling and deeper review. |
What a typical engagement looks like
- 011–3 weeks
Discover
Process map
- 022–4 weeks
Design
Interactive prototype
- 03two-week cycles
Build
Working software each cycle
- 04ongoing
Ship & support
Automated pipeline
What we build it with
- Assistants
- Claude CodeCursorGitHub Copilot
- Quality
- VitestPlaywrightESLintTypeScript
- Pipelines
- GitHub ActionsAutomated reviewDependency scanning
Where we have done this
Questions we get asked
01Is AI-generated code safe to run in production?
Not on its own — and anyone telling you otherwise is selling something. It is safe when it passes the same gates you would apply to any engineer's work: human review, automated tests, security scanning, and a rollback path. That is exactly how we run it.
02Who owns the code you write?
You do, in full, including anything produced with AI assistance. We assign all rights on delivery and can execute an NDA before discovery begins.
03Will my codebase become unmaintainable?
That is the real risk with unsupervised AI development, and it is why review is non-negotiable for us. We hold AI-written code to the same architectural standards as hand-written code — explicit boundaries, readable structure, documented decisions.
04Does this actually make it cheaper?
It compresses implementation time, which is a real share of any build. It does not compress discovery, architecture, review, or testing — so expect a meaningful reduction, not a miraculous one. We would rather set that expectation now than miss it later.
05Which AI coding tools do you use?
Claude Code, Cursor and GitHub Copilot, chosen per task. The tools change quickly; the review gates around them do not.
Adjacent capabilities
Further reading
Tell us what you are building.
Share the problem and we will come back with an approach, a rough shape of the work, and what it would take to deliver it.