AI workflow automation for manufacturers and distributors
In most mid-sized manufacturing and distribution businesses the software is fine and the joins between it are not. The enquiry arrives as an email, the price lives in a spreadsheet, the specification is a PDF attachment, the stock position is in the ERP, and a person holds all four in their head long enough to produce a quotation. That gap is what we automate.
The eight places the hours go
Across this kind of business the same symptoms turn up, whatever the product is: enquiries that sit unanswered because the specification is unclear; quotations that take two days because pricing lives in three places; documents retyped from PDF into a system; the same twenty customer questions answered from memory every week; paperwork chased by phone; and one person who is the only one who knows how a particular thing is done.
We start with one of those. Not all eight.
What the first version usually looks like
An assisted workflow, running beside your team rather than instead of it. It reads the incoming work, does the fetching and the drafting, and puts a finished draft in front of the person who would otherwise have built it from scratch. They correct it. Those corrections are how the system gets good, and they are also how you find out whether it is working before you have committed to anything.
What this does not do
It does not make a decision that needs judgement about a customer. It does not know a price you have never written down. And it does not survive a process that changes every time it runs — if the answer to "how do we do this" is "it depends who is asking", that has to be settled first, by you, not by software.
Questions people ask
We are not a technology company. Is this for us?
It is mostly for companies that are not. The work we automate is ordinary office work — reading an enquiry, finding a price, preparing a document — and the less of it your team has to retype, the more obvious the value.
How much of our time will this take?
The discovery is the part that needs you: a few sessions with the people who actually do the work, and access to real examples. After that the demand on your team is reviewing output, not building anything.
Will this replace people?
It replaces retyping. In every process we have looked at, the constraint is not headcount, it is that experienced people spend their day on work that does not need their experience.
What if our data is a mess?
It usually is, and you do not need to tidy it first. Messy real history is more useful to us than a cleaned-up sample, because the mess is what the system has to survive.
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Last updated August 2026