AI Advisory and Development

We start not with a technology decision but with a sober review of your processes. We look for the places where a lot of repeated judgement work accumulates — reviewing documents, classifying enquiries, extracting data from unstructured sources. That is where the benefit is measurable.

The opposite direction matters just as much: we will tell you where a conventional rule, an interface or simply a tidied-up database solves the problem better. A model that gets a task right 92 per cent of the time is worthless where 100 per cent is required.

AI projects rarely fail on the model and usually fail on the data. We examine what actually exists: completeness, quality, currency, the legal basis, and whether the data can be reached at all from where the application needs it.

From that comes an honest estimate of effort — including the preparatory work that sits ahead of the project proper. That is less comfortable than a quick prototype, but it prevents the kind of project that stalls after the pilot.

An AI feature is only useful if it appears where the work happens — in the ticket system, the ERP, the inbox. We build the interfaces and workflows needed and attach them to your existing estate rather than standing up yet another island.

That includes an explicit approach to uncertainty: suggestions are labelled as such, edge cases are handed to people, decisions are logged. It stays clear who decided what, and on what basis.

Whether a model runs in your own data centre or at a provider is a trade-off between protection requirements, cost and effort. We cost out both routes, including the running cost per transaction — which, with API-based usage, quickly becomes the dominant line in the budget.

On data protection we establish up front which data may leave the building and which may not, and set the processing up accordingly. The record of processing activities and any impact assessment are planned in rather than bolted on afterwards.