AI workflow automation
The repetitive work that quietly consumes your week — triage, routing, data entry, first-draft reporting. Built as observable pipelines with a clear failure mode, not a black box that occasionally surprises you.
Applied AI, built to last
AI adoption that holds.
We build AI systems on your real data, prove they work before they ship, and hand them over documented enough that your team can run them without us. Most pilots fail on one of those three. Ours are designed around all of them.
02 / How we work
Four weeks to something real.
- I
Week one — audit
We look at your actual data and the workflow you want changed. You get a written scope: what's feasible, what isn't, and what it costs — before anything is built.
- II
Weeks two to four — pilot
One workflow, built on real data, with its evaluation set written on day one. You use it, we measure it. If it doesn't clear the bar we agreed, we tell you.
- III
Handover
Documentation, tests, and a working session with whoever will run it. We stay available — but the system does not depend on us being available.
04 / Working together
A small team is the point.
You talk to the person building it. There is no account layer between you and the work.
Most AI consultancies sell you a partner and deliver a junior. We're small enough that the person who scopes your project is the person who writes it, and small enough that we turn down work we don't think we'd do well.
That means we say no more often than a larger firm would. If your problem is better solved by an off-the-shelf tool, or if the data isn't there yet, we'd rather tell you in week one than bill you for discovering it in month three.
It also means our scope is honest. We quote what we can deliver, and when something turns out harder than estimated, you hear about it while there's still time to decide.
You don't need a technical background to work with us. Part of the job is explaining what we built, and why, in language your team can actually act on.
01 / What we build
Three things, done properly.
Narrow on purpose. These are the parts of AI adoption that most often break, and the ones worth doing well.
Knowledge systems
Making your own documents answerable. The hard part isn't retrieval, it's knowing when the answer is wrong — so these ship with an evaluation set and a measured accuracy figure you can hold us to.
Evaluation and quality
The part almost everyone skips. We build the test set, the scoring, and the regression suite, so you can change a prompt or swap a model and know within minutes whether you improved anything.
05 / Why pilots stall
Four ways AI projects quietly die.
If you recognise one of these, that's the conversation to have.
It worked in the demo and not on our data.
The demo used clean examples. Your data has inconsistent naming, missing fields, and cases nobody documented. That gap is the project.
- Build on real exports from day one
- Edge cases in the test set
- Failure modes made visible
We can't tell whether it's actually any good.
Without an evaluation set, every change is a guess and every opinion carries equal weight. Measurement is what makes the argument end.
- An agreed bar before building
- A test set that decides it
- Regression checks on every change
The person who built it left.
If the system only runs because someone remembers how, it has a countdown on it. Documentation and tests are the deliverable, not the paperwork around it.
- Handover session with the operator
- Tests that explain intent
- Runbook for the failure cases
Nobody uses it.
Usually because it solved a problem nobody actually had, or added a step to a workflow instead of removing one. That's a scoping failure, and it's preventable in week one.
- Scope from the workflow, not the technology
- One workflow at a time
- Say no when it isn't worth building
Who this isn't for.
If you want a proof of concept to show a board, or the fastest possible demo, we're the wrong choice — that work is real, it just isn't ours.
- Not the cheapest option
- Not the fastest demo
- Not a staffing agency
07 / FAQ
The questions worth asking.
Including the sceptical ones.
You shouldn't hire us on reputation, because we don't have one yet. Hire us on the scope document you get at the end of week one — it's fixed price, it's yours either way, and it tells you whether the project is worth doing at all, including when the answer is no.
A week-one audit is fixed price. A pilot is scoped from that audit, so you see the number before committing. We don't quote a range before understanding the data, because that number would be fiction.
We agree a measurable bar and build the test set that decides it before writing the system. At the end you get the measurement, not an impression. If it misses, we say so.
You get the code, the tests, the documentation, and a working session with whoever will run it. We stay reachable, but the system is designed not to need us.
That's usually the wrong frame, and often the reason projects get quietly resisted. The work we automate well is the work people are glad to stop doing: triage, re-keying, first drafts.
We work in your environment where possible, and scope data access to what the task needs. If a project requires data we shouldn't hold, we'll say so before it starts.
Often, yes — and that's a legitimate outcome of the audit. If an off-the-shelf product solves your problem, we'd rather tell you than build something worse.
Start
Tell us what's stalled.
Thirty minutes, no cost, and a straight answer about whether we can help.
Send a message
Tell us what you're trying to change and what's in the way.
- Reply timeOne business day
- First call30 minutes, no cost
- LanguagesEnglish
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