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Automating the work that needed judgement

Intelligent process automation for tasks rules could never quite cover — routing, triage, extraction and reconciliation.

When the rules stop being enough

Rule-based automation works until the exceptions outnumber the rules. Then you have a system nobody dares change and a person handling everything it kicks out.

The tasks worth automating with AI are the ones that were never quite rule-shaped: reading a supplier invoice that arrives in a different format each time, deciding which team a ticket belongs to, matching payments to orders when the reference is wrong. We start with a measured baseline of how long it takes today.

What the work includes

What the work includes

Process and volume analysis

How often, how long, and what an error costs — so the payback is arithmetic, not a claim.

Automation design

What is automated, what is assisted, and what stays with a person. That line is the design.

Build and integration

Connected to the systems the work already flows through.

Exception handling

A clear queue for what the system will not decide, with context attached.

Accuracy monitoring

Sampled and reviewed, so quality is known rather than assumed.

Payback reporting

Hours saved and error rate, measured after launch against the baseline.

Who this is for

If one of these sounds like your situation, it is worth a conversation.

A team doing high-volume reading

Invoices, applications, claims — the same shape of task, all day.

Rules that grew unmanageable

Hundreds of conditions, exceptions on exceptions, and nobody willing to touch it.

Hiring to keep up with volume

Headcount rising in step with transactions. That relationship can usually be broken.

How we keep it honest

The difference between a model that helps and one that quietly misleads.

A baseline before a model

We measure how well the current process does first. Surprisingly often it is the thing to beat, and sometimes it wins.

Evaluated on data it has never seen

Held-out, time-split where time matters. An accuracy figure from the training set is not a result.

A human in the loop where it counts

Confident cases go through; uncertain ones go to a person. That boundary is set by you, not by us.

Monitored after launch

Data drifts, behaviour changes, accuracy decays. You get the alerting, not just the model.

Part of AI & Data

Part of AI & Data

AI that survives contact with your data

Machine learning, generative AI, automation and analytics — applied to problems where the numbers justify it, and measured after launch.

See all AI & Data services
What we commit to

What we commit to

  • A written recommendation not to proceed, where that is the honest answer
  • Accuracy reported on held-out data, with the failure cases shown
  • Your data stays yours — never used to train anything we sell elsewhere
  • A documented fallback for when the model is unavailable or unsure
  • Running costs estimated before you commit, not discovered on the first bill
How an AI project runs

How an AI project runs

Deliberately front-loaded with the question of whether to proceed at all.

Feasibility

We look at your actual data, establish the baseline, and give you a straight answer on whether this is worth building. Some of these end in "do not".

Prototype

A working model measured against the baseline on held-out data, with the error cases in front of you rather than averaged away.

Production

Integration into the system where the decision is actually made, with the human-review boundary, logging and rollback in place.

Monitoring

Drift detection, periodic re-evaluation, and retraining when the numbers say so rather than on a calendar.

Common questions

Rules work until the exceptions outnumber them. AI handles the tasks that were never quite rule-shaped — a supplier invoice in a different format each time, or a ticket that needs reading to route.

We measure the current process first: how often, how long, and what an error costs. The payback becomes arithmetic rather than a claim, and it is reported again after launch.

They go to a clear exception queue with the context attached. That boundary is the design — automating the confident cases and routing the rest is what makes it trustworthy.

Is this the right fit?

Tell us what you are trying to do. If AI automation is not the answer, we will say so.

Get in touch
What AI and automation covers

What AI and automation covers

Process automation

The repetitive work between your systems, done without a person in the middle.

Document intelligence

Pulling structured data out of invoices, forms and contracts.

Forecasting

Demand, churn and capacity models built on your own history.

Conversational interfaces

Assistants grounded in your content, with citations.

MLOps

Models versioned, monitored and retrained on a schedule.

Where it pays off

We start with the process, not the model.

Talk to our team
Operations teams

Operations teams

Hours of copying between systems replaced by a reliable pipeline.

Finance

Finance

Invoice capture and reconciliation with a human only on the exceptions.

Customer service

Customer service

First-line answers drawn from your own documentation, with a clean handoff.

What good automation looks like

What good automation looks like

  • A measured baseline before anything changes
  • A pilot on one process, not twelve
  • Accuracy reported honestly, including the misses
  • A clear path when the model is unsure
The best result we can give you is often "this does not need AI" — and we will say so.
How we work

By the numbers

60%

average manual effort removed

8 wks

typical pilot to production

30+

processes automated

Our approach

Automate the process you have, then improve it. Doing it the other way round takes twice as long.

Measure first

If we cannot count the time it takes today, we cannot prove we saved any.

Human in the loop

Confidence thresholds, review queues and a clear override.

Explainable by default

Every automated decision leaves a trail you can read.

Responsible AI

Responsible AI

How we handle your data

Your data trains your models, in your tenancy. Nothing is shared across clients, and we will tell you plainly when a task does not need a model at all.

About us
From idea to production

From idea to production

A short, honest path with a decision point at the end of each stage.

Week 1 · Process mapping

What happens today, and what it costs.

Week 2–4 · Pilot

One process, measured against the baseline.

Week 5–8 · Production

Monitoring, exception handling, rollout.

Ongoing · Retraining

Drift detection and a scheduled refresh.

Common questions

In our experience it moves people off the parts of their job they already dislike. We build the exception queue precisely so judgement stays with a person.

Into your cloud tenancy. We do not send client data to third-party training pipelines, and any external model call is documented before it is built.

Not sure where automation would help?

A two-week process review will tell you — and it is fixed price.

Book a review

Send us a note

Tell us what you are trying to do. We reply to everything within one working day.

Available 09:00 to 17:00, from 24 hours ahead.
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