Put AI to work in the business.
Most organizations don't have an AI problem — they have a "where would it actually pay" problem. This is a packaged program that answers that question with evidence, proves it on one real workflow, and leaves your team able to run the next one without us.
The technology is rarely what's stopping you.
- You've seen a demo that impressed everyone and shipped to nobody.
- Skilled people still spend hours a week moving data between systems.
- Documents arrive as PDFs and leave as spreadsheets, by hand.
- Everyone has an opinion about where AI should go, and no evidence.
- You need AI inside a regulated process and can't see how to evidence it.
- Tools were bought, nobody adopted them, and the licences renew anyway.
Four stages, known before you start.
This is the part that makes it a product rather than an engagement: the shape is fixed, so you know what you're buying.
- 01
Find where it pays
We walk your actual workflows and rank them by two things: how much cost the AI would remove, and whether the evidence exists to do it well. Most candidates fail the second test — better to learn that in week one.
- 02
Prove it on one
One workflow, built properly, running against real work with an evaluation you can read. Not a demo on cherry-picked examples — a thing that either holds up or visibly doesn't.
- 03
Put it in the work
Integrated where the job already happens, with a human in the loop at the point of judgment, and the fallback defined for when the model is wrong.
- 04
Hand it over
Your team learns to run it, extend it and judge the next candidate themselves. The program ends with you not needing us for the next one.
The work worth pointing it at.
Four patterns come up again and again. If your problem doesn't look like one of these, that's worth knowing early — and the first stage is designed to find out.
Reading what arrives
Documents, forms, emails and applications that a person currently opens, understands and re-types somewhere else.
Triage and routing
Deciding what a thing is and who should handle it — where the wait is the queue, not the work.
Drafting the first version
Reports, responses and summaries where a person is faster editing than starting from a blank page.
Finding it in the pile
Answering questions from your own documents and records, with the source attached so the answer can be checked.
The part most AI work skips.
A pilot that impresses is easy. Something that survives contact with real work, and that you can defend when it's wrong, is the whole job.
A human at the judgment
The model proposes and a person decides, wherever the decision carries consequences. Automating the typing is not the same as automating the call.
Measured, not demoed
Every deployment ships with an evaluation set and a number you can track. If it degrades, you find out from the dashboard rather than from a customer.
Traceable answers
Where the work is regulated or contested, the output carries its evidence — what it read, and what it based the answer on.
We'll say when it isn't AI
Plenty of the problems we're shown are better solved by fixing a form, an integration or a process. We'd rather tell you that than sell you a model.
This, or the bespoke version?
Take the program when you want a known shape and a fast answer about where AI pays. Take the service when you already know the workflow and it needs something built around it specifically.
AI & Automation
Our consulting service — bespoke, scoped around your workflow rather than around a program.
See the serviceBefore you commit.
How is this different from your AI & Automation service?
Scope. This program has fixed stages and known outputs, so you know what you're buying before you start. The service is bespoke — it starts from your workflow and designs whatever that workflow needs. If the program doesn't fit what you've got, we'll point you at the service instead.
Do we need our data in order first?
Usually less than people fear, and the first stage tells you exactly what's missing before you've committed to building anything. Where the evidence genuinely isn't there, that's a finding, not a failure.
Does our data get used to train anyone else's model?
⚠️ TODO(client): state the data-handling position plainly here — which providers are used, what is retained, and what is contractually excluded from training. This is the first question every serious buyer asks and a vague answer loses them.
How long does the program take?
⚠️ TODO(client): fill in the real duration per stage once the program shape is fixed.
What does it cost?
⚠️ TODO(client): decide whether to publish a price, a range, or route to contact. This answer currently routes to contact.
What if it turns out AI isn't worth it for us?
Then the first stage says so and you've spent a fraction of a build to find out. We'd rather end there than deliver something that quietly gets switched off.
Start with one workflow.
Tell us where the time goes in your organization. We'll tell you which parts AI would genuinely help with — and which parts it wouldn't.