Custom AI automation
Custom AI automation built around your process
You know the process and you know where it goes wrong. What you may not have is the team to build the AI that runs it. That is the part we do, whatever the process happens to be. QuotifAI already runs custom AI automation in production every day, and the same people build the rest.
Your process · Your systems · We build it and run it
You know the steps, the exceptions and the judgement calls. What you do not have is the team to build the thing that does it.
Idea is clear · no one to build it| Job | Looks at | Answer goes to |
|---|---|---|
| Match invoices to orders | PDFs in a mailbox | Your ERP |
| Check items against a spec | Photographs | Pass or flag |
| Sort incoming requests | Email and attachments | Your folders |
| Spot unusual readings | Exported measurements | A daily report |
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Why AI projects stall
The idea is rarely the hard part
Most teams know exactly which work should be automated. What stops them is everything around it: what the thing has to look at, the rules that live in someone’s head, the systems it has to fit alongside, and the exceptions a generic tool has never seen.
We start from your process
There is no product to fit into and no single industry we work in. We look at how the work is done today and build around that.
It works on whatever the job involves
Documents and messages, photographs and video, screens, readings from equipment or records in a system. If a person can do the job by looking at something and deciding, it can usually be built.
Messy input is normal here
Scans, photographs, spreadsheets, free text and whatever the real world produces. Clean, structured data is not a requirement for getting started.
Your rules stay your rules
Thresholds, tolerances, approvals and the judgement calls your people make are configured to match how you work, not what a generic tool assumes.
It ends up in production
We are not a prototype shop. The measure of the work is whether it handles real traffic on a normal Tuesday.
We run it once it is live
Hosting, monitoring, cost tracking and fixes are part of the arrangement, not a separate conversation.
From an idea to something that runs
How we build custom AI automation
01
You bring the process
We sit with the people who do the work now and watch what actually happens, including the parts nobody wrote down. By the end of it you know what is worth building, what it would take and what it should save.
02
We agree what to build first
Some processes are worth building end to end from the start. Others are better proved on one part before the rest follows. We shape the scope around the problem and what it is worth, not around what is quickest for us.
03
We build it into how you already work
Whatever the job needs to look at, we work with. Wherever the answer needs to end up, we put it. The connections, the file formats and the rules that go with them are part of the build, not a separate project.
04
We run it, and keep it running
The work does not end at go-live. We monitor it, watch what it costs, and fix what the real world throws at it once actual traffic starts arriving.
What you get out of it
What changes when the work runs itself
You do not need an AI team
You supply the process knowledge. We supply the engineering, the integrations and the people who keep it running afterwards.
It fits the systems you already have
We build into the tools your team already uses, so nobody has to move somewhere new to get the benefit.
You keep the domain knowledge
Nobody understands your work, your customers and your exceptions better than the people already doing it. We build around that instead of flattening it.
No standardising before you start
Rule-based automation usually needs everything to arrive in the same shape first. AI works from what actually turns up, so nothing has to be standardised or cleaned up before you start.
The awkward cases are the point
The messy cases are usually the reason the work cannot be automated with rules. They are the first thing we look at, not the thing we defer.
You can see what it did
Every decision is recorded with the reasoning behind it, so you can check the calls it makes and correct the ones you disagree with.
It is looked after once it is live
Once it is live it stays our responsibility. Your team does not inherit a system they now have to keep running.
You know where you stand early
That first look gives you a straight answer on scope, effort and value, so nothing is committed on a guess and there is no long discovery phase to pay for.
You stay in control of it
Built to be checked, not trusted blindly
A record of every decision
What it did, what it read and why it decided that way is all logged and available to look at.
A person can always step in
Anything uncertain is flagged for review rather than guessed at, and your team has the final say.
You decide what it can reach
You choose which systems it connects to and what it is allowed to do in them. Access is scoped to what the job actually needs, and nothing wider.
Questions
Before you get in touch
What kind of processes can you automate?
Anything repetitive that needs a judgement. Usually that means looking at something, working out what it means and then acting on it. What it looks at is up to the job: a document, a message, an image, a screen or a set of readings. It does not have to resemble the products on this site.
Do you only work with wholesalers and distributors?
No. That is where our own products started, so it is what most of this site talks about, but the underlying work is the same in any industry with documents, rules and systems that do not talk to each other. Tell us the process and we will tell you whether it fits.
We have an idea but no technical team. Is that a problem?
No. That is the usual starting point. You bring the process knowledge and we bring the engineering, the integrations and the people who keep it running once it is live.
Do we have to replace the systems we already use?
Usually not. We build into the tools you already run, so your team carries on working where they do today.
How long before something is actually running?
It depends on the size of what you want. A contained piece of work can be running in weeks. Something larger takes longer, and we will tell you which one it is before you commit to anything.
What happens after it goes live?
We monitor it, track what it costs to run, and fix what changes around it. File formats and systems move, and keeping up with that is part of the work.
What if our idea turns out not to be a good fit?
Then we will say so in the first conversation. We would rather turn down work that will not succeed than bill you to discover it.