Software built AI-first, owned by senior engineers.
We build web apps, mobile apps, internal tools and AI features with engineers who work AI-first every day. Coding agents take on much of the routine work in parallel, while experienced engineers own the architecture, the review and the result. You typically get working software sooner, from a leaner team, with the accountability you would expect from any serious partner.
AI-native is a way of working, not a tool subscription
Almost every developer now has access to an AI assistant. Using autocomplete in an editor does not make a team AI-native, any more than owning a spreadsheet makes a company data-driven. The difference is whether the delivery process itself has been redesigned around what AI does well.
In an AI-assisted team, engineers work much as they always have and ask an assistant for help along the way. In an AI-native team, engineers spend a large part of the day directing several coding agents at once, each on a well-defined task in its own branch, then reviewing, correcting and integrating what comes back. Tests, documentation, migrations and research are produced alongside the code instead of being left until the end.
That shift changes where human effort goes. Less time typing boilerplate and wiring up integrations; more time on the decisions that determine whether software succeeds: how the system is structured, what happens at the edges, how data is protected, and whether the feature solves the problem it was built for.
Inside our AI-native development workflow
This is the rhythm a typical feature follows. The tools change quickly; the rule that a senior engineer is accountable for every merge does not.
Senior engineers shape the work
PlanArchitecture, data model and interfaces are designed by experienced engineers first, then broken into small, precisely specified tasks that agents can execute well.
Coding agents work in parallel
BuildSeveral AI coding agents run at once on separate tasks and branches. We built and open-sourced perchd, a command-line tool for developers running agents in parallel, because this is how our own engineers work every day.
Tests and review on every change
VerifyAgents write unit and integration tests alongside the code. Automated checks and AI-assisted review catch the obvious issues, and a senior engineer reads the diff before anything merges.
Documented, deployed, demonstrated
ShipDocumentation and changelogs are drafted as the work happens, deployments run through CI, and you see working software in regular demos rather than status reports.
Traditional, AI-assisted or AI-native development?
The labels get used loosely. This is how we see the practical differences if you are the one buying the software.
| Traditional team | AI-assisted team | AI-native team | |
|---|---|---|---|
| How AI is used | Little or not at all | Individual assistants and autocomplete | Parallel agents built into the delivery process |
| Team shape | Larger, more junior capacity | Similar size, somewhat quicker | Smaller and senior-heavy |
| Routine code, tests and docs | Written by hand, often squeezed | Partly generated, varies by developer | Generated by default, reviewed by seniors |
| Delivery pace | The baseline | Moderately faster | Typically faster, with shorter feedback loops |
| Where your budget goes | Engineer-hours on routine work | A mix of routine and design work | Senior judgement, review and product decisions |
| Main risk | Slow and expensive | Inconsistent practices across the team | Unreviewed AI output, if discipline slips |
Real gains depend on the problem. Well-understood products benefit most; novel research and heavily regulated systems benefit less. Our AI development speed calculator gives an indicative comparison for your project.
What an AI-native software development partner changes for you
Efficiency only matters if it reaches the client. These are the outcomes we design every engagement around.
Faster delivery
Working software in front of users sooner, because routine work no longer queues behind a handful of developers.
A leaner team
A small group of senior engineers directing agents, instead of a large team with handoffs and coordination overhead.
Lower cost for the same scope
Fewer engineer-hours spent on routine work, which is reflected in how we scope and price the engagement.
The same accountability
Named senior engineers answer for architecture, security and quality. "The AI wrote it" is never an explanation we offer.
Better-documented code
Tests, documentation and decision records produced as part of the work, so any future team can pick it up.
Your code, your IP
Code in your repositories, agreed rules on which AI tools may touch your code and data, and all IP assigned to you.
What AI does not do for you
We would rather set expectations now than explain them later. These limits apply to every AI-native team, ours included.
It does not decide what to build
Agents are excellent at executing a clear task and poor at knowing whether the task matters. Product judgement stays with you and our senior engineers.
It does not remove the need for review
AI-generated code can look correct and still hide security holes, subtle logic errors or needless dependencies. Every change is read by an experienced engineer.
It does not make hard problems easy
Novel algorithms, messy legacy integrations and ambiguous requirements still take careful thought. The gains are largest on well-understood work.
It does not change your compliance obligations
Privacy, data residency and confidentiality rules still apply. We agree which AI tools and settings are acceptable for your code and data before work starts.
It does not settle intellectual property questions on its own
We use tools with commercial terms suited to client work, keep sensitive data out of places it should not go, and include licence checks in review.
Questions we often hear
What is AI-native software development?
AI-native software development is a way of building software where AI coding agents are part of every stage of delivery, from implementation to tests and documentation, rather than an occasional assistant. Engineers direct and review the agents' work and spend their own time on architecture, product decisions and quality.
Is AI-generated code safe to use in production?
It can be, with the same controls you would apply to any code: senior review, automated tests, security scanning and careful dependency management. The risk comes from shipping AI output that nobody has read or understood. Our process requires an experienced engineer to review every change before it merges.
Is an AI-first development agency cheaper than a traditional one?
For the same scope it is typically cheaper, because fewer engineer-hours go on routine work and the team can be smaller. How much cheaper depends on the project. When comparing quotes, ask each vendor how they use AI in delivery and whether that efficiency shows up in their price.
Who owns code written with AI tools?
On our projects, you do. Code lives in your repositories and intellectual property is assigned to you under the contract. We use AI tools with commercial terms suitable for client work, and we can agree restrictions on specific tools if your organisation has its own policies.
Can you help our in-house team work AI-first?
Yes. We can work alongside your engineers on a real project, set up agent workflows, review practices and guardrails, and help you assess how candidates use AI when you hire. Most teams learn faster by shipping something together than by sitting through training sessions.
What is perchd?
perchd is an open-source command-line tool we built for developers running multiple AI coding agents in parallel. We created it because it fits how our own engineers work, and released it so other teams working the same way can use it too.
Related reading and tools
Tell us what you want built.
Describe the product or feature, your timeline and anything already in place. We will reply within 24 hours with how an AI-native team would approach it and what it might realistically take.
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