Our process - How we work

We work closely with our clients to understand their needs and goals, embedding ourselves in their every day operations to understand what makes their business tick.

For AI work, this phase is mostly about the data. We audit what you actually hold — how it is structured, how clean it is, and whether it can support the outcome you have in mind. Most projects are decided here, before a line of model code is written.

We come back with a comprehensive plan: what is feasible, what is not, how we would measure success, and what it will cost. If the honest answer is that AI is the wrong tool for the problem, we say so at this stage.

Based on the discovery phase, we develop a roadmap and start working towards delivery. Work is sequenced so the riskiest assumption is tested first — for AI projects that usually means proving the model can reach the required accuracy before we build anything around it.

You work directly with the people building the system, not through an account manager. Our team is small enough that this is practical, and it removes the layer where requirements normally get lost.

Progress is visible throughout. We ship to a staging environment continuously, so you are reviewing working software rather than status reports.

Delivery means the system running in your environment, against your real data, with the people who will use it trained on it. A model that works on our machines is not a delivered project.

We deploy where the requirements dictate — AWS, Azure, GCP or on-premise. For clinical and other regulated work, on-premise is often the only acceptable answer, and we are set up for it.

Everything is handed over properly: source, infrastructure definitions, credentials and documentation. You are free to continue with us or without us.