Is our data used to train someone else’s model?

Not without your explicit agreement. We select providers and configurations where your data is excluded from training by default, and we tell you exactly where your data travels before anything is built.

How accurate will it be?

That depends entirely on the task, and anyone who quotes a figure before seeing your data is guessing. We establish a measurable baseline on a real sample early, so you can decide whether to proceed with a number in front of you.

What does it cost to run?

AI features carry ongoing per-use costs, unlike traditional software. We estimate that running cost during scoping so it is part of the decision rather than a surprise on the first invoice.

Service

AI & Machine Learning Consulting

Practical AI work for businesses that want a specific problem solved — document processing, customer support, forecasting, classification — rather than a strategy deck about artificial intelligence.

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Start with the problem, not the technology

Most AI projects that fail do so because they began with the technology and went looking for a use. We work the other way around: we look at where your team loses hours to repetitive judgement work, and whether a model can do that part reliably enough to be trusted.

Good candidates tend to look similar. Someone is reading documents and copying fields into a system. Someone is triaging enquiries into categories. Someone is producing a forecast from the same spreadsheet every week. These are narrow, measurable tasks where accuracy can be checked against a known answer — which is exactly what makes them safe to automate.

Being honest about what it cannot do

We will tell you when AI is the wrong tool. A large language model is a poor choice for anything requiring exact arithmetic, guaranteed consistency, or a decision you must be able to defend line by line. Plenty of problems presented as AI problems are better solved with a database query and a well-designed form.

When we do build something, we build in evaluation from the start: a test set, a measured accuracy figure, and a defined path for the cases the system gets wrong. A model that is right most of the time with no way to catch the remainder is a liability rather than an asset.

What you get

An assessment of whether AI genuinely fits the problem — including when it does not
Working integrations into the systems your team already uses
Measured accuracy against a real test set, not a demo
Human review paths for low-confidence cases
Clear running cost estimates before you commit

Common questions

Is our data used to train someone else’s model?: Not without your explicit agreement. We select providers and configurations where your data is excluded from training by default, and we tell you exactly where your data travels before anything is built.
How accurate will it be?: That depends entirely on the task, and anyone who quotes a figure before seeing your data is guessing. We establish a measurable baseline on a real sample early, so you can decide whether to proceed with a number in front of you.
What does it cost to run?: AI features carry ongoing per-use costs, unlike traditional software. We estimate that running cost during scoping so it is part of the decision rather than a surprise on the first invoice.

Related services

Tell us what you’re trying to build

We’ll give you an honest read on scope, cost and timeline — including whether it’s worth doing at all.