Put AI to Work on the Tasks That Eat Your Week

It starts with a paid diagnostic, priced on the size of your team and the number of processes we review. The build that follows is quoted as one fixed scope and one fixed fee, agreed in writing before anyone starts. We do not quote a build before the diagnostic tells us what is worth building, because that is how you end up paying for the wrong tool.

The diagnostic takes about two weeks. The first tool is usually running four to six weeks after that, because we start with the smallest task that eats the most time. Larger work, such as a model trained on your own data, runs longer. The dates go into the scope, and we report against them every week.

If the same task is done by hand every week by more than one person, the work pays back. Below that it usually does not, and we will tell you that in the diagnostic rather than sell you a build. Size matters less than whether your process is repeatable and written down somewhere other than in somebody's head.

Access to the tools your team already uses, a rough log of how last week was actually spent, and one person who can answer questions and approve decisions. If your data lives in spreadsheets, that is fine, we do the joining. The one thing we cannot supply is your team's own knowledge of how the work really gets done.

Two things, usually. Staff quietly go back to the old way because the new tool is slower on the first attempt, which is why we train and then measure. Or the data turns out thinner than expected, which the Data Readiness Check catches before you spend on a build. Both are recoverable, and both are far cheaper to find early.

Prompt engineering means writing the instructions you give an AI tool, so it gives the same good answer every time. Left to individuals, ten people write ten different instructions and get ten different qualities of answer. We write the prompts once, test them against your real cases, and hand your team a library they reuse.

You do not need a mountain of data. We start from models that are already trained and teach them your specific case. What matters more is that the data you do hold is clean, current, and in one place, which is exactly what the Data Readiness Check looks at before anything gets built.

We write down what the tool is allowed to decide and what a person must sign off. We test it on the cases where it could treat one group worse than another, and we keep a record of why it answered the way it did. Your staff get a short written policy on what may never be pasted into a public AI tool.

Start with tasks that are repeated, written down, and low risk if the first draft is wrong. Writing, reporting, first-draft replies, and sorting incoming enquiries all qualify. Anything that ends in a final decision about a person, a price, or a contract stays with a human until the tool has a record you trust.

That is not what we sell. The tools take the repeat work off the people you already pay, so those hours move to work only a person can do. If you are buying this to cut headcount, say so at the diagnostic stage, because the build order for that goal is different and we would rather hear it than find out later.