What we do, and what it costs.

One hour on one process, a working prototype of it the next day. Mock data and no integrations — it shows the destination, not the finished thing. A euro rather than free, so the exchange is honest both ways.

What we cover

• ·A demo built on your own business — before any theory
• ·Where you are on the seven levels, and where everyone else actually is
• ·What AI actually is, in plain language
• ·How companies like yours are genuinely using it
• ·Where not to use it — the cases that waste money
• ·What to build yourselves, and where you need a partner
• ·Something built live, in the room

What we cover

• ·What AI actually is — GenAI, language models, agents
• ·Where it's strong, where it's weak, and where not to use it
• ·Demos, from simple to genuinely complex
• ·Security, risk, and what data can be used
• ·What to build yourselves, and where you need a partner
• ·Where to start

What we cover

• ·Morning: the four-hour foundation
• ·How AI is used at team, process and company level
• ·What makes a good use case — and what makes a bad one
• ·Ideation in small groups, on your own processes
• ·Evaluation and ranking
• ·Pilot selection

What we cover

• ·Morning: the four-hour foundation
• ·What Copilot is — and where its limits are
• ·Demos: agents, workflow automation, agentic applications
• ·Hands-on building, on your own tools
• ·Identifying what to automate next
• ·Your own agent, built by you

What we cover

• ·A programme built around your own processes, systems and people
• ·Or a facilitated workshop on a single topic
• ·Strategy · use-case prioritisation · tooling and licensing · roadmap

We work through your processes, score the candidates on value against effort, and tag each one honestly: what your own people can build with the tools you already have, and what needs a data foundation first. You get a ranked list from your own operations, with the first pilots chosen. Scoped to one business area.

Where your data lives now, what has to be true before agents can use it, and the shortest route between the two. The security level is agreed here rather than retrofitted. You get a current state, a target state, and the steps between them. Scoped to one data environment.

Where you sit today, where the value actually is in your operations, and what a realistic sequence looks like — written by someone who builds these things, not a vision deck. You get a strategy, a prioritised roadmap and an agreed order of work, including what to stop. Scoped to one company or business unit.

This is the first time it runs on your own data, in your own systems, in front of the people who would actually use it. You get a straight answer on whether the value is there, with the numbers behind it — and sometimes that answer is no, which you hear from us rather than working it out a year later. A fixed price, agreed before anything starts. We scale only if the value is real, and this is where most customers start.

An agent is a role, not a chatbot: a job to do, access to the systems, and responsibility for the outcome. It does the chasing and the checking; the decisions stay with your people. It acts where the work already lives — you decide how far it may go, and at what security level.

One agent does one job. A team covers a whole flow: a specialist for each job, a manager routing the work between them, and someone accountable above them. They consult each other rather than waiting for a person to carry the answer across. You decide the scope of the team and how much it may do on its own.

Most AI is somewhere you go. This is somewhere your people work. They open it and the whole picture is already there — pulled from every system that holds a piece of it, plus the numbers that were never in any system at all. What should be happening is worked out, with the reasoning shown so it can be argued with. Every number has one definition, agreed once, so two teams stop turning up with two answers. Your people still decide — they just stop assembling the picture first.

Better tomorrow than today is what we sell, and what we hold ourselves to: we learn how your operation actually runs, day by day, and it goes straight into what we build next. We teach while we build, so your own people take on more of it themselves — the aim is that you need us for less over time, not more. We say when something is not worth building, even when we would be paid to build it. And we are still there months later, when the value was supposed to appear — to see whether it did. If it didn't, you don't pay for it.