Services
Five pillars, and the discipline to stop there.
We would rather be genuinely good at a handful of things than list thirty and mean none of them. Everything below is work we do ourselves.
These five overlap more than the headings suggest, and most engagements touch two or three of them. An AI project almost always turns out to need data engineering first; a product launch needs somewhere to deploy; a modernisation programme needs the integrations rebuilt before anything else can move.
You do not need to know which one you are buying. Describe what is not working and we will tell you which of these it actually is — including when the honest answer is that it is none of them.
The five pillars
What each one actually covers.
Each page sets out the same four things: what the work involves, what we will not do, how an engagement runs, and the questions people ask before committing.
Custom AI Solutions
AI agents, LLM integration and intelligent automation built for your data and your workflows — not a wrapper around someone else’s demo.
Custom AI Solutions in detail →What you walk away with
- A working system in production, not a proof of concept
- Evaluations that tell you when quality regresses
- Cost per operation you can forecast
- Build
Product Engineering
Web and mobile products taken from zero to launch — and from launch to the version that actually holds up under load.
Product Engineering in detail →- A product in your users’ hands, in increments
- A codebase another team could pick up
- Documented decisions, not tribal knowledge
- Run
Cloud & DevOps
Kubernetes, CI/CD and infrastructure-as-code — so deploying on a Friday stops being a topic of debate.
Cloud & DevOps in detail →- Deploys that are routine rather than events
- Infrastructure described in code and reviewable
- A recovery plan that has actually been tested
- Connect
Data & Integration
Pipelines, APIs and analytics that get your systems talking — usually the unglamorous prerequisite for everything else on this page.
Data & Integration in detail →- One version of the numbers
- Integrations that fail loudly instead of silently
- Reporting people trust enough to act on
- Extend
Dedicated Teams
Engineers embedded in your team, on your board, in your standups — for when you need capacity rather than a project.
Dedicated Teams in detail →- Capacity that integrates with your process
- Knowledge that stays when the engagement ends
- Monthly terms, not annual lock-in
What we will not do
A list of services is only useful next to the work it refuses.
Custom AI Solutions
Training foundation models from scratch — almost nobody needs this, and if you do, you need a research lab rather than us
Product Engineering
Pixel-matching a design file without questioning the parts that will not work
Cloud & DevOps
Kubernetes where a managed platform would serve you better
Data & Integration
Dashboards nobody has agreed to act on
Dedicated Teams
Bodies on a bench presented as a team
Each pillar page carries the rest of its list.
Before you choose one
Most people arrive with a problem rather than a category.
That is the right way round. Tell us what is not working and we will say which of these it is, how we would approach it, and whether it is worth doing at all.
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