AI & AUTOMATION

Less admin, better decisions and more time for useful work

We help teams find safe, useful ways to use AI and automation, removing repetitive work without adding noise, risk or another disconnected tool.

WHERE IT HELPS

Useful AI starts with a job someone already needs to do

Find the answer, with the source.

Bring together policies, product information and operational guidance so people can get a useful answer without digging through folders or asking around.

Sort the signal from the noise.

Classify, summarise and route incoming enquiries, documents or cases so the right person starts with the right context.

Get past the blank page.

Give teams a well-structured first draft for routine content, then keep people in charge of the judgement, tone and final sign-off.

Stop asking people to copy and paste.

Connect the systems that already run the business, then automate the hand-offs, checks and updates that quietly consume everyone’s time.

IN PRACTICE

Real uses, designed around real work.

Examples include: triaging and summarising incoming requests; turning policy, product and operational knowledge into a trusted internal assistant; creating first drafts that a person checks for routine communications; and connecting systems so data is captured once, sent to the right place and visible to the people who need it. We start with the workflow, the controls and the result we can measure, not the novelty of the model.

HOW WE WORK

No big transformation programme, just useful progress

Start with a real process, the people doing it and the cost of leaving it as it is.

Set the boundaries early: data, approvals, hand-offs and where a person needs to make the call.

Build a small, testable version, see what changes and give the team the confidence to keep using it.

EXPERIENCE

Experience that reaches from the roadmap to the room.

Callum led AI, integration and automation at Oxbury Bank, introducing new tools in a regulated, operationally complex business. That work included designing solutions, connecting systems and helping colleagues, from delivery teams to executives, understand where AI could create value, where human judgement must stay central and how to adopt it responsibly.

SIX WEEKS, NOT SIX MONTHS

Start narrow, prove it, then widen

Most AI programmes stall because they were programmes. This runs as a short, narrow engagement on one workflow a real team uses daily, with the boundaries agreed before anything is built, because that is what decides whether it ever reaches production.

Map a real week

Days one to five

Not the process diagram from two years ago, the actual work, including the spreadsheet somebody maintains privately because the system cannot cope. We mark anything happening more than five times a week and anything involving copying information between places.

YOU RECEIVE

A written map of the work as it happens

A ranked list of repetitive handling

An honest baseline: minutes per case today

Agree the boundaries

Week two

In a regulated business this is the part that decides whether anything reaches production. Which data can be used, where it is processed, who approves an output, what gets logged, and what happens when the answer is wrong. Agreed before the build, not retrofitted after a pilot.

YOU RECEIVE

A data and processing boundary, written down

Named approvers and human decision points

A logging and audit approach risk will accept

Build one narrow workflow

Week three to four

One team, one workflow, real data, real consequences. We automate the preparation and the admin and leave the judgement with the person accountable for it. Every step stays reviewable, because savings you cannot audit become problems later.

YOU RECEIVE

A working automation in daily use

Review steps a person can inspect

A rollback route if it misbehaves

Measure the boring numbers

Week five

Minutes per case before and after, taken from the same team rather than estimated. How often an output is corrected and what the correction usually is. How much rekeying disappeared, the saving nobody thinks to claim.

YOU RECEIVE

Before and after timings from real cases

A correction rate with the common causes

A written case for whether to extend it

Train and hand over

Week six

We sit with the team on live work and show where judgement stays with a person, what to do when an output looks wrong, and who to tell. Adoption is a training outcome far more often than a technology one.

YOU RECEIVE

An in-person session on live cases

Plain-language guidance for the team

A named owner and escalation route

A short list of what not to automate next

BEFORE THE BUILD

The six things we settle before writing anything

Callum led this work at Oxbury Bank, including the unglamorous parts: making the case internally, agreeing controls with risk colleagues, and helping teams understand where judgement had to stay with a person. The technology was rarely the hard bit.

Data

Which data can be used, which cannot, and where it is processed. Written down before a single prompt is designed, and reviewed with whoever owns the risk.

Approval

Which outputs a person must approve before anything acts on them, and who that person is by name rather than by department.

Logging

What is recorded so a decision can be reconstructed months later. If a saving cannot be audited, it will eventually become a problem instead.

Human judgement

The decisions that deliberately stay with a person, and the reason each one does. This list is short, explicit and treated as non-negotiable.

Failure

What happens when an output is wrong: who notices, how it is corrected, and how the correction is fed back so the same error stops recurring.

Scope creep

What we agreed not to automate yet, and the evidence that would change our mind. Most of the value comes from resisting the second workflow too early.