What we can build. All of it, in plain English.

Things that read for you

The inbox, the supplier PDFs, the forms, the scans of handwritten job sheets. An AI reads them, pulls out what matters, and either files it where it belongs or puts a one-line summary in front of you. Email gets sorted into "needs you", "needs someone else" and "needs nothing". Supplier delivery dates get lifted out of attachments and into your job system. Invoices get matched to the orders they belong to.

This is usually the first thing that pays off in a business, because it's the first thing that's eating the morning.

Things that write for you

Quotes written in the van from a photo and three words. Proposals that sound like you, because they're built from the last twenty you sent. Product descriptions, customer replies, the monthly report nobody enjoys writing. The AI drafts from your own past work, in your voice, and you read it before it goes.

The trick isn't the writing, it's the reading first: it only sounds like you if it has seen how you do it.

One place to ask

Your bookings are in one system, your invoices in another, the customer's last email in a third, the stock in a spreadsheet. We join them up so an AI can see all of them at once, read-only where it should be, and you ask it the way you'd ask a good office manager: "Who's in this week that still owes us?" "What did we quote the Smiths last time?" "Which lines haven't sold since spring?"

Technically this is a mix of connecting to your systems directly and teaching the AI to look things up in your documents rather than guess. You don't need to know that. You need to know every answer shows where it came from.

Agents that do the job

Once the AI can read your systems, it can act in them. An order comes in: check payment, check stock, book the courier, print the label, email the tracking, post it to the accounts. An invoice arrives: match it to the purchase order, flag the difference, queue the payment for approval. A lead goes quiet: draft the follow-up, wait a week, nudge again, tell you when they bite.

Every one of these has a step where a person approves. You decide which step. For a tracking email, probably none. For a refund, always.

Dashboards and apps your team actually opens

Not forty charts. One page with the numbers that matter this week, where each one came from, and what to do about it. Net sales against last month. Margin by product after the real costs. The cash snapshot at half eight. A shared action plan the team can tick off. Built as a web page or a small app, with logins if different people should see different things.

The AI part is the layer that reads the page every Monday and writes down what it would do next. The dashboard shows you the numbers; the notes tell you what they mean.

Taking input from anywhere

Customers don't all use the website form. They WhatsApp, they phone and leave a voicemail, they email a photo, they fill in a paper form at the counter. The AI can take all of those in, understand what's being asked, and put it into the one place it belongs, whether that's a booking, a job, a quote request or a complaint for a human to pick up.

Machines, sensors and the data they throw off

This is the one nobody else local does. Thirty years of engine control units, dynamometers and race-car telemetry means we're comfortable with a log file from a CNC machine, a compressor's run hours, a van's tracker feed or a lab instrument's export. The AI reads the stream, spots what's drifting, and tells you before the thing fails rather than after.

If you make, fix or run physical things in Yorkshire and you've got data coming off them that nobody looks at, this is probably where the money is.

Keeping it safe

Every build gets the same five rules whether it's a clinic or a factory. The AI gets the least access it needs, read-only wherever it can be. The steps that matter have a person approving them. Every action is logged with what it read and why it did what it did. Your data stays in your systems, in the UK where the provider allows it, and nothing is used to train anyone's model. If a build can't meet all five, it doesn't get built.

Opening your own software to AI

If you already know what you need, this is the grown-up version. For firms with their own software, a big API, or systems that nothing off the shelf will talk to, we build the bespoke connection that lets an AI model use them safely: what's called an MCP server. It's the same work the founder has shipped for an automotive calibration platform used by major car makers, and the same thing running under Loftsoft Racing.

It comes with the governance to match: who the AI can act as, what it can see, what it must ask about, and a record of everything it did. Happy to talk at that level from the first conversation.