AI Automation
The work your team repeats every single day, automated: document handling, data entry, classification, routing and reporting. With a person still in the loop wherever being wrong is expensive.
Start where the work is boring and the volume is high.
The best automation candidates are not the exciting ones. They are the tasks somebody does forty times a day, where the rules are mostly consistent, and where being wrong occasionally is recoverable. Those pay back quickly and build the confidence to attempt harder things.
What we typically automate
Documents into data
Invoices, purchase orders, forms and delivery notes read and turned into structured records, with anything uncertain routed to a person rather than guessed.
Classification and routing
Incoming email, tickets and enquiries read, categorised and sent to the right team, with priority and sentiment attached.
Drafting and replying
First-draft replies written from your own documents so your team edits rather than composes. Faster, and consistent in a way people never are.
Extraction and checking
Pulling the specific facts out of contracts, reports or statements, and flagging where two documents disagree with each other.
Reporting that writes itself
The weekly summary somebody assembles by hand, generated from the source systems with the commentary drafted.
Human in the loop by design
Confidence thresholds decide what goes through and what a person checks. You choose where that line sits, and you can move it as trust grows.
How we approach automation
Discover, Design, Build, Deploy, Optimize.
Forward Deployed Engineering: senior engineers inside your team, on the same five stages every time.
Discover
We map how your business actually sells, what your data looks like, and what it would take to get this working. You get a costed plan, not a proposal full of maybes.
Design
Architecture, integrations and the checks we will be judged by. Agreed before anybody writes code, so nobody argues about done later.
Build
Senior engineers writing code inside your environment, in two-week sprints. You watch it get built rather than waiting for a handover.
Deploy
Live and monitored, on your own servers or private cloud where your data governance needs it. This is the stage most projects never reach.
Optimize
Measured against the checks from stage two, tuned for cost, and watched. We stay until it runs without us.
Questions buyers ask us first
In our experience it moves them onto the work that needed judgement, because the volume that was queuing up gets handled. We are not going to promise you headcount reduction, because the honest outcome is usually more throughput from the same team.
It is designed to escalate rather than guess. You set the confidence line, everything is logged, and actions are reversible. Before go-live we run it alongside your team and compare, so you see the error rate before it matters.
We measure the task first: time, volume and current error rate. If those numbers do not justify the build, we will tell you before you spend anything.
Only if you are comfortable with that, and we tell you exactly what would be sent. Where policy does not allow it we run smaller models on your own infrastructure instead.
Name the task that eats your team's week.
The repetitive one everybody complains about. A senior consultant will tell you whether it automates well, what it would cost, and whether the numbers justify doing it.