Automation that gives you your week back.
Most companies do not need artificial intelligence. They need to stop copying data between three systems by hand. We start there, because that is where the hours actually are — and we add AI where it earns its place, not where it sounds impressive.
Where the hours hide
Every business has the same handful of leaks, and they are rarely the ones people complain about.
Re-entering the same data.
An order arrives by email, someone types it into a spreadsheet, someone else types it into the accounting system. Three copies, three chances to diverge, and an hour a day that produces nothing.
Documents that need reading.
Invoices, delivery notes, forms, contracts. Someone opens each one, finds four numbers and types them somewhere. This is the single most automatable task in most companies.
Reports assembled by hand.
Weekly numbers pulled from several places, pasted into a slide, sent by email. It takes half a day and it is identical every week.
Chasing.
Following up on quotes, payments, missing documents. Necessary, tedious, and entirely rule-based.
None of that needs AI. It needs systems that talk to each other, which is cheaper and far more reliable.
Where AI genuinely helps
And then there is the work that rules cannot describe, which is where a model earns its cost.
Understanding unstructured documents.
A scanned invoice in a layout you have never seen, a contract in three languages, a handwritten form. Rules break here; models do not.
Sorting and routing.
Incoming messages classified by intent and sent to the right person, with the urgent ones surfaced. Simple to describe, impossible to write as rules.
Drafting.
First versions of replies, summaries, product descriptions. A human still validates — but reviewing a draft takes a fraction of writing one.
Answering from your own documents.
An assistant that responds using your procedures, your catalogue, your history, rather than the open internet. This is the use case that most often justifies itself, because the alternative is someone interrupting a colleague.
How we decide what to automate
We do not start from the technology. We start by watching where time goes.
We map the process as it really happens
, including the workaround everybody uses and nobody documented. That workaround usually is the process.
We measure
, roughly: how many times a week, how many minutes each time, how often it goes wrong. A task done twice a month is not worth automating, however irritating it is.
We automate the boring middle first.
Highest volume, lowest judgement. It pays for itself fastest and it teaches us your data before we touch anything sensitive.
We keep a human where judgement is required.
Automation that decides alone on things it should not is worse than no automation — it fails silently and confidently.
What we will tell you not to do
An honest constraint, because it saves everybody money.
An AI assistant on top of disorganised data will disappoint you. If your information lives in five inboxes and a shared drive with no structure, the first project is structure, not intelligence. We would rather say that during the first call than three months in.
Equally: automation applied to a broken process makes the process fail faster. If a workflow is wrong, we fix it before we accelerate it.
What we build
Integrations.
Integrations between your existing tools so data stops being retyped.
Document pipelines.
Document pipelines that read, extract, validate and file.
Assistants.
Assistants that answer from your own knowledge base.
Reports.
Reports that build and send themselves.
Alerts.
Alerts that fire on a condition rather than on a person remembering.
Most of it is invisible once it works, which is the point.
What it costs
1,500 – 5,000 $
one automation: a document pipeline, a connection between two tools, a scheduled report.
5,000 – 10,000 $
a set of automations around one process, with an interface for the people who supervise it.
10,000 – 20,000 $
an internal platform: several processes, roles, an assistant on your own data.
20,000 $ and above
an operating system for the business: connected systems, maintained and extended over time.
The right frame is not what it costs but what it returns. An automation that saves five hours a week pays for itself, and then keeps paying.
Frequently asked questions
- Will my data be sent to an AI provider?
- Only if you agree, and only what is strictly needed. Some work can run entirely on your own infrastructure; some benefits from a hosted model. We tell you which is which and what it implies before anything moves, and sensitive data is a decision you make, not one we make for you.
- Can it work with the software we already have?
- Usually. Most business tools expose an API. When one does not, there is almost always another route — a scheduled export, a database connection, a file drop. We check yours during the first call.
- Will it replace people?
- In our experience it removes tasks, not people. The work that gets automated is the work nobody wanted: retyping, chasing, assembling. What remains is what people are actually good at.
- What if the AI gets something wrong?
- It will, occasionally, and the design has to assume it. Anything with consequences passes through a human check, and everything is logged so a wrong output can be traced and corrected rather than discovered by a customer.
- Where do we start?
- With the most repetitive task in your week. It is the cheapest to automate, the easiest to measure, and it tells us more about your data than any meeting would.
- Do we need to change tools?
- Usually not. Replacing working software is expensive and disruptive. We connect what you have first, and only recommend a change when the tool is genuinely the obstacle.
Something eating your week? Book a free 30-minute call. Describe the task, and we will tell you whether it is worth automating and what that would take. No commitment.