AI Consulting
Know what is worth building before you spend on it.
Most AI projects fail before a model is ever called, in the setup: a tool bought before a problem was named, a pilot with no baseline, nobody on the business side who owns the thing. We map where the hours actually go, rank what is worth automating, and hand you a plan with numbers attached. You can bring that plan back to us, give it to your own team, or act on none of it.
- A map of where working hours actually go, function by function
- A ranked shortlist of processes worth automating, with hours attached
- Baseline numbers recorded before anything gets built
- Build-or-buy calls on the tools already on your shortlist
- A staged roadmap your own team could execute
- Board and management sessions on what AI does and does not do
What the mapping actually involves
We sit with each function and list its recurring processes: hours per week across everyone who touches it, how error-prone it is, how much of it follows describable rules, and who owns it. That last question gets skipped most often and matters most later. A process nobody owns cannot be automated, because nobody can say what correct looks like.
Why we insist on a baseline
Ten minutes of measuring before buys you an honest after. With no recorded starting number the project ends in opinions, and whoever liked it declares it a success. "Quoting takes 4 days from enquiry to sent offer and we want it under 1" is a project. "We want to be more efficient with AI" is a budget line waiting to be cut.
The plan is yours either way
What you get is a document, and it commits you to nothing. It names the processes, the sequence, what each one needs and what it should cost. Some clients hand it straight back to us. Some run the first project internally and call about the second. Both are fine, and we would rather you built the right thing without us than the wrong thing with us.
Questions we get asked
How long does the mapping take?
1 to 2 weeks for most companies, depending on how many functions are in scope. It is interviews and watching real work rather than a questionnaire, so it moves at the speed of your team calendars.
Do we need to hire data scientists?
For agent and automation work on existing models, no. These systems are built on Claude and GPT through APIs, orchestrated with n8n and Python. What you need in-house is an owner per process. Deep AI hires only start to make sense once AI becomes part of your product.
Our data is a mess. Should we fix that first?
Start anyway, on a workflow where the data is good enough, and let the project fund the cleanup. "Fix all our data first" is a multi-year programme with no deliverable. Most automations need clean data in one narrow slice, and cleaning a slice is measured in days.
What if the answer is that we should not build anything?
Then we say so, and you have saved considerably more than the mapping cost. It happens, usually when an off-the-shelf product already does the job and the real work turns out to be configuration.
Tell us what's slowing your business down.
30 minutes. No pitch, no deck. Just listening to your needs and seeing how we can help.
Schedule a discovery call