AI, automation and software that lasts
Nine practices and 44 specialisms, one senior team. AI and automation lead because that is where most of the value is now — but the engineering underneath is what makes it hold.
How we decide what to build
Four principles that apply across every practice above, and that shape what we will talk you out of as much as what we will build.
The cheapest answer first
Off-the-shelf software is the right call more often than agencies admit, and we will say so before quoting a build. Custom work earns its cost where your process is a genuine advantage, where nothing on the market fits, or where integration has already cost more than building would.
AI only where judgement is needed
Most of a working system should be deterministic — it is cheaper, faster and far easier to debug. A language model belongs at the two or three points that genuinely need interpretation, not everywhere, and an AI feature you cannot measure is one you cannot improve.
Boring infrastructure
Novel architecture is a recurring operational cost that somebody carries after we leave. We size the tooling to the team that has to run it, which is why we talk most teams out of Kubernetes rather than into it.
You own the result
Your repository, your cloud account, your data, from the first commit. Documented handover, no licensing arrangement and no hosting lock-in — you should never need us to make the next change, only choose to.
Common questions
Do you only do AI work now?
No. AI and automation lead because that is where most of the value is right now, but SaaS platforms, mobile apps, backend APIs and cloud engineering are the same practice and the same team. A good share of what we run in production is Laravel.
Can you work with our existing codebase?
Usually. We start with an audit — architecture, dependencies, security, test coverage — and give you a written assessment of what is sound, what is risky and what stabilising it would cost, before committing to ongoing work.
How do you price?
By scope, after a call. Where the requirements are still unclear we propose a short paid discovery phase instead of a number that would only be a guess. You get the likely range early, before you have spent much time on us.
What if our data cannot go to a third-party AI provider?
We deploy self-hosted models with Ollama and a local vector database. It costs more in engineering and hardware and the quality ceiling is lower than a frontier model, so we will tell you honestly whether your use case can afford that trade.