01
Find the bottleneck
Understand what repeats and what a correct result looks like.
Connect the right tools and automate the steps that repeatedly slow people down. Use AI where it helps, with review and clear boundaries where the result matters.
The problem worth solving
The same information is typed twice. A report waits for someone to copy a file. Important details disappear between systems. Automation should reduce that friction without hiding mistakes.
A good fit for
Teams with a repeatable process, accessible source information and someone who can judge whether the output is right.
Typical starting points
How the work moves
01
Understand what repeats and what a correct result looks like.
02
Use realistic examples, including awkward and incomplete inputs.
03
Build the workflow with clear access rules and review points.
04
Watch the actual result and prioritise the next useful change.
A clear scope matters.
AI output is a draft where accuracy matters. Model costs, information access, human review and failure handling are agreed explicitly. Unsupported facts are not filled by guessing.
Before you start
No. A simple rule, integration or better form may solve the problem more reliably.
We assess the data, access permissions and processing options before choosing a workflow. No deployment is described as private or secure without checking its actual controls.
Next chapter / Let’s talk
Send your idea in your own words. I’ll reply by email and help shape a practical starting point.
Start by email