If you point AI at five different systems and ask what happened last month, you’ll get a slow, expensive answer with numbers nobody can check. We pull the data in with normal code first, check it, and only then let AI read the clean version. It’s the same tools, but a completely different result.
Say you want a monthly report pulling from your store, your accounting and your ad accounts. Here’s the difference in practice.
The way that disappoints people. You give AI access to all three and ask it for a report. It’s reading screens and exports made for people, so it misreads them. It costs money every time it runs, even when nothing’s changed, and a small layout change can break it without anyone noticing. There’s also no way to check its working, so a wrong number looks exactly like a right one.
The way that works. We pull the data from each system on a schedule and put it into one consistent format. The import is checked so the totals match, gaps are flagged and nothing is guessed. Only then does AI see it, and its job is small: spot what changed and write the commentary.
The numbers come straight from your data, and only the words come from AI. You can click any figure in the report to see where it came from.
Connecting systems is a solved problem, so we use proper integrations and save AI for the parts that actually need judgement.
“Draft a reply to this email using these five past answers” is reliable. “Handle my inbox” isn’t, and that difference matters more than anything else.
Tax, pricing, stock counts, invoice totals and anything with legal consequences are always handled by normal, tested code.
Anything a customer will read stays a draft until a person approves it. That’s how we set it up by default, and you decide if that ever changes.
Answers come from your own documents, history and data, with a link to where each one came from, rather than from the internet or the AI’s memory.
Every AI step is logged with what went in and what came out. When it gets something wrong you can see why, and we can fix the cause instead of guessing.
If it isn’t saving you real hours or money, we’ll tell you and turn it off. A feature that only looks good in a demo isn’t worth paying to run.
It’s good at jobs where the input varies, someone checks the output, and being right most of the time is still a big improvement on how things are done now.
Replies written in your voice from your own past answers, ready for you to read and send. It can save close to an hour a day.
Supplier invoices, packing slips and PDF price lists turned into line items automatically, instead of someone typing them in twice a week.
Enquiries sorted, urgent ones flagged, and spam set aside where you can overrule it, so it learns from your corrections.
A forty-message thread, or a month of support, turned into what actually happened and what’s still outstanding.
“Which products lost money once freight is counted?” answered from data that’s already been imported and checked.
The report itself is produced by code, and AI writes the paragraph explaining what changed and why, using the same numbers.
If AI isn’t the right fit for what you’ve described, we’ll tell you and quote the normal version instead. It’s usually cheaper anyway.
Your data isn’t used to train anyone’s AI. We use business plans where that’s written into the contract, and we’ll tell you which provider we’re using before anything is built.
Where information is sensitive, we keep AI away from it and handle that part in code. Any AI feature can also be switched off without breaking the rest of the tool, because the tool doesn’t depend on it.
Tell us what’s going on in your own words. We’ll usually come back to you within one business day with a clear next step, and if it turns out you don’t need us, we’ll tell you.