AI that does a job, not AI that does a demo.
The interesting question is never whether a model can do something. It is whether it does it reliably enough, cheaply enough, to remove real work from your team. We build for that answer.
Where AI genuinely pays for itself
- Reading documents — invoices, forms, contracts, claims — and turning them into structured data.
- Triaging inbound support so the routine 70% resolves without a human.
- Extracting and classifying data that currently costs your team hours of copying and pasting.
- Drafting first versions of repetitive written work for a human to check and send.
Where it usually does not
- Anything where being wrong 5% of the time is unacceptable and nobody checks the output.
- Replacing a process nobody has bothered to document, let alone simplify.
- Chatbots bolted onto a website with no connection to your actual systems.
How we build it
Start with the measurement
Before building, we agree how you will know it works — accuracy against a real sample of your own data, not a vendor benchmark.
Keep a human in the loop where it counts
High-stakes decisions get a review step. Low-stakes ones run automatically. Knowing which is which is most of the design work.
Build it into the workflow, not beside it
AI that lives in a separate tool people have to remember to open does not get used. It belongs inside the system they already work in.
Control the cost
Model calls have a running cost that scales with usage. We design for a predictable monthly bill and tell you what it will be.
No surprises, and no arguments about scope.
You will know the cost and the shape of the project before you commit to anything.
Free scoping call
Thirty minutes. What you need, what it is worth building, and what it is not.
Written proposal
Scope, milestones, a fixed cost band and a timeline — in writing, before you pay anything.
Build in sprints
You see working software every two weeks, not a status report at the end.
Launch and support
We hand over the code and the accounts, then stay on to maintain and improve.
Common questions
Will our data be used to train someone else's model?
Not if it is set up correctly. Business API tiers from the major providers do not train on your data by default, and for genuinely sensitive material we can architect around self-hosted or on-premise models. This is a question worth asking every vendor you speak to.
How accurate is it really?
That depends entirely on the task and your data, which is why we measure on a sample of your real documents before you commit to a full build. Anyone quoting an accuracy figure before seeing your data is quoting a brochure.
Is this worth it for a smaller business?
Sometimes, and sometimes the honest answer is that a well-designed form and a clear process would solve the same problem for a tenth of the cost. We will say so if that is the case.
Tell us the problem. We'll tell you what it takes.
A free 30-minute scoping call, a written estimate, and an honest answer about whether it's worth building.