AI development.

Which repetitive task should software read, sort or draft for you?

AI is useful when it takes on a narrow, repetitive task: reading documents, sorting requests or drafting replies for a person to check. We start with one task, test it on real examples from your business and measure accuracy and cost before building anything bigger. Where mistakes matter, a person reviews the output.

• Staff spend hours reading, sorting or re-typing documents and emails.
• People can't find answers buried in your own documents and systems.
• An AI prototype works in a demo but isn't reliable, secure or affordable in production.

1. 01

   Assess

   Pick one task, check the data behind it, and agree how success will be measured.

2. 02

   Prototype

   Test on real examples from your business and measure accuracy before building anything bigger.

3. 03

   Build

   Integrate with your systems, add human review where mistakes matter, and keep data private.

4. 04

   Monitor

   Track accuracy, cost and failures in production, and improve the system as it is used.

• Which AI models do you use?

  Large language model APIs such as Claude and OpenAI, and open-weight models where data has to stay on your own infrastructure. The choice depends on accuracy, cost and privacy for your task.

• How do you keep our data private?

  We agree what data the system may see before building, and limit it to that. Where it matters, open-weight models can run without sending data to a third party.

• How do you know the AI is accurate enough?

  We build a test set from real examples, measure accuracy before launch and keep tracking it in production. If it falls short, we say so before you invest further.

• Do we need a lot of data to start?

  Not always. Many tasks work from the documents and emails you already have. We check the data behind the task in the first stage.

Tell us about the project. We'll reply with clear next steps.