Almost no business we talk to is blocked by the model. They are blocked because the data it would need is trapped on devices, buried in a legacy system, or stored in a shape nothing can query. We clear that path first, then put AI into the workflows where it measurably pays — with people still accountable for what it does.
The order matters more than the model.
What data you have, where it is lost, and which workflow would pay for itself — before anything is built.
Capture, sync and storage that get the data to somewhere a model can reach it, without losing any on the way.
MCP servers and retrieval over your existing systems, with permissions, tenancy and an audit trail.
Evaluated against real cases from your business, so you know the accuracy before your customers find it.
Every engagement can start with one of these. Each has a name and a scope written down before we start, so the first decision is a small one — and you see how we work before committing to anything larger.
What data you hold, what you are throwing away, and what a model would actually need to answer the questions your business cares about.
A plan you can cost, and an honest view of whether AI is the answer at all.
One of your existing systems exposed to an assistant on purpose — through an MCP server with the permissions, tenancy and audit trail your business already answers for.
An assistant that can safely read and act on a real system — and the pattern for exposing the next one.
One workflow, one assistant, measured against real cases from your business — with a person approving anything that matters.
A go or no-go backed by numbers — accuracy, time saved and running cost — before anything is rolled out.
Every offer is quoted as a fixed price once we have seen the scope — and a fixed price stays fixed: if it takes us longer, that is our problem, not yours.
Two weeks spent on what data you hold, what you are throwing away, and what a model would need to answer the questions your business cares about. It produces a costed plan — and often the useful finding that phase one is plumbing, not AI.
The Model Context Protocol is the standard way to let an assistant read and act on your systems through tools you define. If you want AI working with your own data rather than with whatever someone pastes into it, it is usually the right foundation.
Whichever fits the job, the data residency rules and the budget. The model is a replaceable part; your data, your tools and your evaluations are the asset, and we keep them portable.
Yes. Plenty of problems are still a good query and a better screen, and we would rather say so before you have paid for the alternative.
Tell us what you are trying to improve. We will tell you whether AI is the answer — and what the data needs to look like first.
