Practical AI for business
AI pointed at a real problem, not bolted on so the website can mention it.
The useful applications are unglamorous: reading documents, sorting data, drafting a first pass, checking work against rules. Those save real hours. If a simpler fix would do the same job more reliably, we will tell you and build that instead.
When people call us about this
- Somebody reads documents and types what is in them into a system.
- A person spends hours summarising, sorting or categorising information.
- First drafts of the same kind of document get written from scratch every time.
- Checking work against a set of rules is a job rather than a step.
- You have been told you should be using AI and nobody has said what for.
What we build
Document reading and extraction
Invoices, forms, contracts, statements. The information pulled out and put where it belongs, with the uncertain cases flagged for a person rather than guessed at.
Sorting and classification
Large volumes of records categorised consistently. The kind of job that is accurate for the first two hundred and drifts after that when a person does it.
First drafts
Written in your voice, from your templates and your data, for a person to check and send. Drafting, not sending.
Checking against rules
Comparing what was produced against what the rules require, and raising what does not match. Compliance and quality work where consistency matters more than speed.
The honest no
Plenty of problems are better solved with a script, a form or a conversation. Being told that is worth more than being sold a model.
How it works
Find the actual task
Not "use AI", but which specific job, done how often, by whom, and what going wrong would cost. If that cannot be answered the project is not ready.
Test it against real work
On your actual documents and your actual edge cases, with the accuracy measured rather than assumed. You see the failure rate before you commit.
Build it with a person in the loop
Where the cost of being wrong is real, the output goes to somebody to approve. Automation that quietly makes confident mistakes is worse than no automation.
How long, and what it costs
A proof on your own data usually takes one to two weeks, and it is worth doing before anything else. Building it properly after that is typically three to six weeks.
Price depends on scope, and we would rather look first than quote a range that turns out to be wrong for you. The first conversation is fifteen minutes and costs nothing.
Common questions
Where does our data go?
That depends on the tool and it is the first thing to settle, not the last. For sensitive work there are options that keep data inside your own environment. We will be specific about where information travels before anything is built.
How accurate is it?
Measured, not promised. We test against your real documents and show you the failure rate. Anybody quoting an accuracy figure before seeing your data is quoting somebody else's data.
What if it gets something wrong?
It will, occasionally. That is why anything with a real cost of error is built with a person approving the output, and why we would rather flag an uncertain case than guess at it.
Is this just ChatGPT with our logo on it?
No, and if that is all a problem needs then you should do that yourself and not pay us. What we build is the plumbing around a model: getting the right information in, constraining what it can do, checking the output and putting it somewhere useful.
Related
- Process Automation — The little things that only take a minute, until you add up the year. We take them off your desk and keep them off it.
- Business Solutions — We get underneath the skin of the business, find the expensive thing nobody has fixed, and fix it. Then we stay until it holds.
Fifteen minutes, no obligation. We will tell you straight whether this is the right fix for what you have described.
Time to talk yet?