AI automation agency
Ship agents that do the work.
We design, build and deploy agentic AI systems that take a real process off your team, end to end. Not a demo, not a chatbot bolted onto a form: a system with a defined boundary, a log of every decision, and proof it matches human output before it is switched on.
agent.run()
Every system we build runs this loop: read the input, decide what to do, act inside its authority, then check its own work. A failed check retries. Anything outside the boundary goes to a person, with its reasoning attached.
AI automation services
Three ways in, depending on what you already know.
AI workflow automation
Agents that carry a process end to end: read the inputs, decide, act in your systems, then check their own work.
Read more 02AI automation consulting and audits
A fixed-scope review of how work really moves through your team, and a ranked, costed plan for what to automate first.
Read more 03MCP server development
Model Context Protocol servers that make your internal systems safely callable by the AI tools your team already uses.
Read moreHow we work
The boundary gets written before the code does.
Narrow first
One process, one boundary, one measurable output. Broad automation programmes are how pilots die. A narrow system that genuinely runs in production teaches you more than a roadmap does.
Shadow before authority
The agent runs against real inputs alongside the person still doing the job, and the outputs get compared. It takes over only where they agree, and only as far as they agree.
Legible by default
Every run is logged well enough to reconstruct why the system did what it did. An automation you cannot audit is one you cannot safely expand.
FAQ
Common questions
What does "agentic" actually mean here?
That the system decides what to do next based on what it is looking at, rather than following a fixed sequence you drew in advance. The practical difference shows up on the exceptions: a fixed workflow stops when the input is unusual, an agentic one reasons about it and either handles it or escalates with its reasoning attached.
Is our process a good fit for automation?
It depends on three things: how mechanical the judgement is, whether the systems involved can be reached programmatically, and what a mistake costs. Processes that fail the third test are usually the wrong place to start regardless of how tempting the first two look. The audit exists to answer this properly rather than by guesswork.
How do you stop an agent doing something damaging?
By deciding its authority in writing before building it, keeping write access scoped to exactly the agreed actions, logging every run in enough detail to reconstruct the reasoning, and running in shadow mode against real work before it has any authority at all. Safety comes from the boundary, not from the model behaving well.
Do we need to have picked an AI vendor first?
No. The model is an implementation detail chosen per task, and the integration layer is built so swapping it is a configuration change. Committing to a vendor before you know what you are building is the wrong order.
What size of engagement do you take on?
Anything from a fixed-scope audit through to building and deploying a working system. The most useful first steps are small and narrow: one process, one clear boundary, running in shadow mode.
Tell us what the work looks like
Describe the process you want off your team plate. You will get a straight answer on whether agents are the right tool for it, including when they are not.