We build AI automation for business process and revenue work: the documents, quotes, enquiries and handovers that move between your systems and your people. No PLCs, no robotics.
AI automation on this page means business process and revenue automation. It does not mean industrial control, PLCs, machine vision on a production line, or warehouse robotics. Those are a different trade. The work we automate happens between your systems and your people, when an enquiry arrives, a document has to be read, a price has to be calculated, or a record has to be retyped into a second piece of software because the first one does not talk to it.
Process automation and workflow automation have been sold for two decades as connectors between apps. Those still earn their keep, and a good build uses them. What changed recently is that a system can read an unstructured thing, a scanned inspection report, a long email thread, a voice note in another language, and turn it into structured data your existing tools can act on. That is where most of the remaining hours in a mid-sized company sit.
So the useful question is not which tasks are technically automatable. Almost everything is, badly. It is which tasks cost you enough, often enough, to be worth engineering properly, and which are cheaper left with a person.
Most of what an operations team does all day is move information from where it arrived to where it is needed. A supplier PDF becomes a line in the job system. A site report becomes a quote. A signed form becomes a customer record, a schedule entry and an invoice. Each hop can lose a detail, and each is paid for in salaried hours that produce nothing new.
Our automations and document intelligence work reads that paperwork once, checks it against what you already hold, and actions it in the systems you use. Anything the system is unsure about goes to a person with the source document attached and the field in question highlighted, rather than sitting in a queue for someone to find later.
The Quoter is the version of this that is live today, in plumbing. It reads an inspection report or a Spanish voice note, prices every line off the client's own book, and flags what needs a human. The arithmetic is kept away from the model, so the numbers come from the price book rather than from a guess.
The reason most owners are wary of AI in an operational process is not that it will be slow. It is that it will be confidently wrong in front of a customer, and nobody will notice until the customer does. That is a design problem, and it is solvable, but only if you design for it from the start instead of bolting a review step on at the end.
Every system we build is scoped around what it may decide alone and what it must hand over. Deterministic work, arithmetic, lookups and eligibility rules, is done in code where it is testable. Judgement work is done by the model, with a confidence threshold and an escalation route attached, so an ambiguous input becomes a flagged item rather than an invented answer.
The failure mode changes as a result. A badly built system fails silently and you hear about it from a complaint. A properly built one fails cheaply, into somebody's queue, with the reason written next to it.
Ask what the system has to be worth before they build it, and ask them to prove it on your records rather than a demo set. That is how we run it. We agree the number up front, build the smallest working version, run it on your own messy historical data, and score it against that figure. If it clears, you buy the build with the risk taken out. If it does not, you do not buy anything.
Ask also how they will know whether it worked afterwards. Most automation projects are measured by the fact that they shipped. The Tracking Layer exists to stop that: conversion tracking rebuilt end to end across a financial services funnel, so every unit of spend could be traced to the customer it actually produced. If you cannot see the hours or the errors coming out of the baseline, you have bought a story.
Proven By Data is a trading name of Only Big Jobs LLC. We sell AI services to owner-led companies from roughly $5M in revenue across the UK, Ireland, the United States and Australia, wired into the tools you already run and handed over so your team can operate them without us.
We map where information is retyped, re-checked or lost between systems, and price what each hop costs you a year.
We build the smallest working version, run it on your own historical documents rather than a clean sample, and score it against the number agreed beforehand.
We wire it into your existing tools, set what it decides alone and what it flags, and put a person in front of anything customer facing.
We track it against the baseline, review what it flagged and why, and widen its remit only where the evidence supports it.
Nothing here is sold as a silo. Most engagements start with one of these and pull in the next once the first is paying for itself.
Half an hour with a founder, not a sales rep. We tell you straight whether a machine fixes this, what it would cost, and what it would return.
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