AI-native development, smaller teams, lower costs
Senior engineers direct AI through code, tests and documentation, so you pay for fewer hours of routine work without giving up review.
- Small senior teams
- Every change reviewed
- Yours to run without us
Where the savings come from
Not from cutting corners, but from spending fewer paid hours on routine work.
Smaller teams
A few senior engineers direct AI, instead of a large team doing everything by hand.
Less routine work
AI drafts boilerplate, tests, migrations and documentation for engineers to review.
Fewer handoffs
Fewer people between your idea and the code means less time lost in translation.
Earlier feedback
You see working versions sooner, so wrong turns cost less to correct.
The market is moving to smaller teams
Traditional delivery
- Larger teams and more handoffs
- Routine code, tests and docs written by hand
- Long phases before you see working software
- More paid hours spent on routine work
AI-native delivery
- A small senior team directing AI
- AI drafts routine code, tests and docs, and people review them
- Working versions earlier and more often
- Fewer paid hours on routine work, so a lower build cost
Gartner lists AI-native development platforms among its top strategic trends for 2026, and predicts that by 2030, 80% of organisations will evolve large engineering teams into smaller teams augmented by AI.
What we build this way
Always connected to your systems of record, so nothing gets typed twice.
Web applications
Customer-facing products and business applications, built around your workflow.
APIs and integrations
Payments, marketplaces, shipping, EDI and legacy systems, with retries and error reporting.
Portals
Self-service for customers, suppliers and field staff, on your own records.
Data pipelines
A clean, documented warehouse for reporting and model training.
Internal tools
The screens a team needs when a spreadsheet has become critical.
Legacy modernisation
Old applications documented, then replaced in stages rather than all at once.
Lower cost, same standards
Four rules that hold however much of the first draft AI wrote.
A person reviews everything
AI writes drafts. An engineer is accountable for each change that ships.
Tests with the code
Each change ships with the tests that protect it.
Security checks
Automated scans and human review, because AI can repeat insecure patterns.
A real handover
Documentation and access, so your team or next vendor can run it without us.
Questions we get asked first
What is AI-native software development?
It means building AI into the whole delivery process, from requirements and code to tests, documentation and migrations, rather than adding a coding assistant on top of an unchanged process. A small senior team directs the AI and stays accountable for what ships.
How does it reduce development cost?
Routine work takes fewer paid hours because AI drafts it and engineers review it. Smaller teams also have fewer handoffs and less coordination overhead. You spend more of your budget on decisions and less on repetitive typing.
How much can we save?
It depends on the project, so we do not quote a flat percentage. Savings are usually largest on routine, well-understood work such as standard applications, tests and integrations. They are smaller on novel problems, unclear requirements or undocumented legacy systems. We will tell you which parts of your project fall where.
Does AI-written code mean lower quality?
It can if nobody checks it, which is why it isn’t left unchecked here. An engineer reviews each change, tests ship with the code and security checks run before release.
Is our code or data sent to AI providers?
We agree the tools and data rules with you before work starts. Where your rules are strict, we can use approved enterprise services or models running on infrastructure you control.
Who owns the code?
That is agreed in the contract before work starts. Our default is that you receive the source code, documentation and access needed to run it without us.
Can you integrate with our existing software?
Usually, yes. It depends on what the other system exposes, such as an API, files or a database. We check that early, along with how failures should be retried and reported.
Can you modernise an old internal application?
Yes. We start by documenting what it really does, including the rules nobody wrote down, then replace it in stages where possible rather than all at once.
Got a quote that felt too high? Let’s look at it.
Tell us what you want built, and we will show you where AI can lower the cost and where it can’t.
Start the conversation