Custom software, owned for years.
Web platforms, mobile apps, cloud infrastructure and applied AI: designed, built and kept running by one senior, AI-native team.
You own the code, the accounts and the documents from the first invoice.
Four things, done properly.
The product your business runs on: SaaS, portals, CRMs, dashboards, subscriptions and payments.
The next surface of a product we already run, one codebase across iOS and Android.
The infrastructure it all runs on: architecture, security, deployment and monitoring.
LLM features, retrieval over your own data and agentic workflows, in production, not a demo.
Three ways this starts.
All three begin small. None of them require a long contract up front.
A new product from scratch
You have a business and a plan, not a codebase. We shape the spec, make the architecture calls and ship the first working version.
Take over something built
The product exists but the team behind it left, stalled or never quite fit. We inherit it, stabilise it and take the roadmap forward.
Add a surface or AI
A mobile app on the backend you already have, or AI features inside the product your customers already use.
Whatever we build, we keep running.
The people who architect it are the ones who build and maintain it.
Testable acceptance criteria first, so "done" means verified.
Working software in small slices, behind flags, with monitoring.
You own the code, the pipeline and the IP from the first commit.
AI-native, not AI-assisted.
Most teams paste AI on top of the old workflow. We rebuilt the workflow around it: coding agents do the mechanical work between the gates, and everything a machine can check, from types and tests to security scans and deploys, runs automatically. Senior judgment goes where it counts, so you get the speed of agents with the accountability of engineers who own the outcome.
Frequently asked questions.
Do you use AI to write our code?
Yes. Coding agents handle the mechanical work (boilerplate, tests, migrations) inside a pipeline where every change still needs a green build and a senior engineer's review before it merges. The architecture, tests and product decisions stay human.
Will our data be used to train AI models?
No. We build and test against synthetic data, your production data and PII never enter a prompt, and which models may see which data is written into the contract as a hard delivery constraint. We put AI into software; we don't train models.
Can you add AI features to an existing product, not just build new ones?
That is the most common way this starts: one feature in production inside the product your customers already use. We review the codebase and data first, then ship the smallest useful version before expanding it.
Do you build with WordPress, or only custom code?
Both, and the platform is an architecture decision rather than a rate card. When a content or membership site is genuinely the right tool for the job we build it in WordPress, properly, to be maintained. When it isn't, we'll tell you.
Who owns the code and the AI pipeline you build for us?
You do: the code, the pipeline, the infrastructure accounts and the IP, outright and from the first commit. Nothing is licensed back to you and nothing is held hostage at the end of an engagement.
Does "AI-native" mean you'll build our product faster but less carefully?
The opposite: agents make writing code cheap, so the craft moves to verification. Everything a machine can check (formatting, types, tests, security scans, deploys) runs as an automatic gate, and senior judgment goes to architecture and what to build.
Tell us what you're planning.
In 30 minutes we'll name the architecture decision that matters most and what we'd build first.
Free · 30 minutes · with a founder · no deck, no pressure