Take the repetitive work out of your business and put your data to work.

A customer emails a purchase order as a PDF. Someone opens it, reads it and types the details into the order system, line by line. Later the same order is typed into the accounts package, and at the end of the month someone else pulls figures from both systems into a spreadsheet so the directors can see how the month went. None of these steps needs judgement. They just need doing, and together they take up a surprising share of the week.

Meanwhile, the board is asking what the business is doing about AI.

Those two things are more closely connected than they look. AI is good at reading a document and pulling out the details, but it can only be trusted with that job when the process around it is clear and the data underneath it is clean. That’s why we work in a particular order.

We start with the steps that follow fixed rules. We map the process with the people who do it and automate the parts that don’t need a decision, using tools such as n8n, Make, Zapier and Power Automate, or custom code when the job is too specific for them. That usually gives the team time back within a few weeks.

Next comes the data. We bring the figures from separate systems into one place and build dashboards in Power BI, Metabase, or custom solutions that update themselves, so the month-end spreadsheet stops being anybody’s job.

Then AI, in the places where it’s reliable enough to earn its keep. That means reading documents like that purchase order, assistants that answer questions from your own data, drafting and summarising, and agentic workflows where a person approves any step that matters. We build the MCP servers and integrations that let AI tools work with your systems safely, and we set up evaluation and monitoring so you can see when a model gets something wrong. It will, occasionally, and you should know when it does.

We work with Claude, OpenAI and Azure OpenAI models, and our own delivery runs the same way. It’s a large part of how we build at the speed and cost we do, and it’s also why we’re up-front about where it falls short.