Capabilities / AI and automation
Put AIto work.
AI can now read, analyse and act on information at a scale that was not practical until recently. We build it into the way your business already works: taking repetitive jobs off people's desks, finding the answers buried in your own information, and helping your team do more with what they already know.
Your business knowsmore than you think.
Years of quotes and thousands of invoices. Customer emails, specifications, orders and reports, and the decisions made on the back of all of them. AI gives us a new way to put that information to work, so we start with yours rather than with a demo.
We start with something real
Give us the documents, data or process that is actually taking up people's time. We work on the real thing from the beginning, so the results mean something.
We show you what it can do
We test it on your own work and measure what happens. You see where it performs, where it needs a person and what difference it would make, before anybody commits to a build.
We build it into the business
When it works, the experiment becomes part of how you run: connected to the systems you already use, in front of the people who need it, and with a person checking the decisions that matter.
What we build here
More capability, less busywork.
Most businesses have all three somewhere, and one of them is usually costing far more time than anybody has added up.
Document intelligence
Invoices, delivery notes, specifications and certificates, turned into information your systems can use without somebody reading and re-keying every one. Usually the clearest payback in the building.
Agents that get things done
AI given the tools to finish a job, not just answer a question: raise the order, chase the missing certificate, reconcile two systems, or line up the next step for somebody to approve.
Estimating and pricing
Years of your own trading history, put to work. Tools that help your people estimate, price and plan from what the business has already learned on the jobs it has done.
It has to be right.Or it has to say so.
All of that is only worth having if somebody can tell when it is wrong. A system that is confidently mistaken costs more than the job it replaced.
Four things are in every build:
Measured, not claimed
Accuracy on your data, written down as a number, before anything goes live. If it is not good enough we will say so and you will not have spent the build.
It shows its working
Every answer comes back with where it came from, so somebody can check it in seconds rather than taking it on trust.
A person where it counts
Anything that moves money, sends something to a customer or changes a record gets a human in the loop. Speed is not worth an apology.
Your data is not the product
Nothing you give us trains a model anybody else benefits from, and we will tell you which provider is processing what before it does.
How it works
One job at a time.
A typical first piece of work looks like:
One call
Tell us where the time goes, or where you wish the business knew more. Twenty minutes is usually enough to say whether AI is the right tool for it, and we will say so when it is not.
A test
On your own documents or your own history, priced as a small piece of work. You get the accuracy number and the failure cases, whatever they say.
The first job live
The noisiest one, running in weeks, with a person still checking it. We measure the difference it actually made before anybody talks about the next.
When you are ready
Tell us where you want an advantage.
A process that takes too long. A pile of information nobody has time to use. A decision that rests too heavily on one person's experience, or something you simply wish your systems could do. Tell us about it, and we will tell you what AI could realistically do with it.
Technologies
No model loyalty.Whatever does the job best.
Models change every few months, and being tied to one is how you end up paying for last year's. We pick per job, move when something better arrives, and use plain software wherever plain software does it better.
Where it runs
Where your data goes, and where it stops.
With this kind of work the first question is never how fast it is. It is what leaves the building, who processes it and whether any of it is kept.
AWS
Where models can run inside your own account and region, so the documents never leave infrastructure you control. The default for anything sensitive.
Krystal
UK based and renewable, for the parts that are ordinary software rather than inference. Most of any AI system is still just a system.
Vercel
For the interface people actually use. Fast to load and close to whoever is asking, which matters when somebody is checking an answer between jobs.
Before you ask
The questions we always get asked.
Will our data be used to train somebody else's model?
No, and you will be told which provider processes what before anything is sent. The enterprise tiers of the models worth using contractually exclude training on customer data, and where a job genuinely cannot leave your building we run it somewhere it does not have to.
What happens when it gets something wrong?
It will, which is why the design starts there rather than ending there. Anything touching money or a customer is checked by a person, low-confidence cases are flagged rather than guessed at, and every answer carries its source so a human can settle it in seconds.
Does our data need tidying up first?
Usually less than people fear. Reading messy documents is the thing this technology is genuinely good at, and waiting until the data is clean is how these projects stay permanently three months away.
Is this just ChatGPT with our logo on it?
If that is what a job needs, we will say so and it will be cheap. Most of what is worth building is not: it is a model pointed at your own documents and history, with the plumbing to get answers back into the system people already use.
What does it cost to run?
There is a per-use cost and you should see it modelled before you commit, because it is the part that surprises people. For most document work it is pennies per item, and we will tell you when the volume makes a cheaper approach the sensible one.
What if we want to turn it off?
Then you turn it off, and the process it replaced still exists because we do not delete it on your behalf. Anything that cannot be switched back off has not been built carefully enough.
Keeping you in the loop on the technology changing how work gets done.
Read our hot takes on the technology changing how people work, and find out a bit about the nerdy things our team have been doing.
Read the latest issueNo pitch, no deck
Tell us what isactually happening.
Describe the problem in your own words. You do not need it worked out first - an honest conversation usually gets further than a tidy brief.
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