Work requests are read the moment they arrive, attachments included, and become jobs on the board without anyone retyping them. Purchase orders reconcile themselves on a schedule.
M Squared is a commercial landscape and groundworks contractor working across many sites, and most of its incoming work arrives as email. A client asks for a repair, a framework customer orders extra works, something needs recharging back. Each one is a job that has to reach the people who will do it, with the right detail, on the right site, against the right budget.
Traditionally somebody in the office read every message and copied it into the system by hand. It was a full-time job in all but name, and a fragile one: when that person was off, the work still arrived and nothing moved. Purchase orders added a second version of the problem, with the client’s order in one system, the record of the work in another, and a monthly reconciliation that everybody postponed.
Agency AI built M Squared a set of automations that take the work out of the mailbox and put it where the business operates, without anyone retyping it.
Incoming work emails are read as they arrive. Our AI works out what each one is asking for, pulls the detail out, including anything sitting in an attachment rather than the message, and creates the job with that detail attached. Ad-hoc works and recharge works are separated, because they are billed differently and confusing them costs money.
Alongside it, purchase orders are reconciled on a schedule rather than at month end. The system compares what has been ordered against what has been recorded, and reports the difference to the team. A mismatch surfaces while it is still a question, rather than becoming a write-off.
Route planning is connected too, so what is scheduled in the field and what the office sees stay the same thing.
It understands what is being asked. Work requests do not arrive in a standard format. They arrive as somebody typing what they need, sometimes in the message, sometimes in a document attached to it. Our AI reads both, so a job described in an attachment is not missed.
It knows what is not work. A mailbox is full of replies, acknowledgements, out of office notices and threads the business is only copied into. Everything is filtered before anything is created, because an intake system that creates a job from a thank-you email gets switched off in a fortnight.
It handles volume without falling over. Emails are worked through in batches rather than all at once. That is what keeps it steady when a client sends forty orders on a Monday morning.
It tells the team what it did. Purchase order checks report into the channel the team already uses, so the reconciliation is something people see rather than something they have to remember to run.
It fails loudly. When something breaks, an alert is raised rather than the inbox quietly filling up. That is the difference between automation you can rely on and automation you have to check.
Copying an email into a system takes two minutes, and that was never where the money went. It went on the request that sat unread for three days while a client waited, the recharge nobody spotted was rechargeable, and the work carried out against an order that did not cover it. Each of those is invisible on the day and obvious at the end of the month.
With intake automated, the work starts the day it arrives, and the record of it is the same record the client is working from.
See the operating system this feeds into →, or the recruitment pipeline built the same way.
AI reading an email is a small share of what makes this work in a real business. The rest is judgement about the mailbox: which senders matter and which are noise, how to tell an order from a query about an order, whether an attachment is the job or a signature image. Getting those wrong does not produce an error message, it produces a board full of rubbish, and a board full of rubbish gets abandoned within a month.
Volume gets the same care. A client who sends four emails a day for three weeks, then forty on a Monday, breaks anything built for the average, so the system works in batches and keeps its place if something fails halfway. None of that is visible when it is running well, which is the point.
Tell us where your team loses the most time. We will tell you honestly whether AI pays there, what it takes to build, and what we have already delivered for businesses like yours.
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