Bring requests together
If requests arrive in messages and their details live in a spreadsheet, we can plan one place to collect the information and track the next step.
Show me a task your team keeps doing by hand. We’ll look at what a focused tool could handle, where AI would help, and what still needs a person’s judgement.

An AI project I’m building for LTA. This capture shows the public sign-in screen; the workspace is private.
View the public pageA few places we could start. The right tool depends on your task, your systems and the people doing the work.
If requests arrive in messages and their details live in a spreadsheet, we can plan one place to collect the information and track the next step.
For work that involves searching documents or comparing details, we can explore AI support that brings the useful information together for your team to review.
When someone copies the same details between systems, we can look at a connection that moves the right information after an agreed action.
The first conversation is about the work you want to improve. The technology comes after that.
We look at how it works today, who does it and where the friction is. You bring the business knowledge; I help work out what is worth building.
We choose one job for the first version, the information it needs and how someone will check its output. Scope, cost and any running services are agreed first.
I build a working version for you to review. We test the important steps together and check whether the tool helps with the job we set out to do.
Before putting it into everyday use, we agree access, hosting, ownership and support. Further features follow what we learn from using the first version.
We agree the people, information and checks each step needs before it becomes part of your day-to-day work.
No. A clear example of the task is enough to start. Show me what happens now and what you would like to make easier.
No. Sometimes a clearer form, a database or a simple connection between systems is the better fit. We can choose AI when it has a useful job to do.
We agree where human review belongs before building. For work where a wrong answer matters, we plan how someone can inspect and check the output.
That depends on the workflow, integrations and information involved. I quote for an agreed scope, with hosting, external service costs and ongoing work explained separately.
A conversation is a good place to start.
Tell me what the task involves, how you handle it today and where it gets slow or repetitive. A real example is enough to start the conversation.