Find the right first question.
You do not need to understand AI before getting in touch. These questions help you decide whether the work is worth examining, what a sensible first step looks like, and what remains under your control.
Before you decide
Bring a recurring task that feels slow, unclear, or dependent on too much manual coordination.
Understand when better process, existing software, or AI is the useful answer.
Know the scope, risks, ownership, and decision point before a larger build begins.
Do we need to buy a new AI platform?
Not necessarily. We begin with the software, data, and workflows you already use. Sometimes the right move is a better handoff, cleaner data, or a small missing component. If a suitable product already exists, we will tell you to buy it.
What happens in the first conversation?
You bring the business context and a rough description of work that feels slow, repetitive, error-prone, or difficult to see across the team. The purpose is to decide whether there is a problem worth examining, not to sell a vague transformation programme.
What does a first engagement look like?
Most first engagements are fixed-scope workflow diagnostics. We map one recurring process, identify the useful intervention, describe risks and dependencies, and make a written recommendation. A larger build is scoped separately only when the evidence supports it.
Can AI make mistakes?
Yes. The defence is system design: use automation where mistakes are easy to catch, place checks around important actions, retain human approval where consequences are high, and make it possible to see and correct what happened.
Is our data safe to use with AI?
It depends on the data, tools, permissions, and workflow. Before anything ships, we identify what information each step uses, where it goes, who can access it, and what the tool retains. Access and review points are agreed before launch.
What do we own after the work is complete?
Your accounts, data, credentials, and commissioned deliverables remain under your control according to the project agreement. The scope defines documentation, handover, support, and any ongoing maintenance.
What if AI is not the answer?
Then it should not be forced into the workflow. Sometimes the useful answer is cleaner data, a simpler process, clearer ownership, better reporting, or better use of software you already pay for.
Who does the work?
You work directly with the founder. If specialist support is needed, the scope states who is involved and what they are responsible for.
Still uncertain?
Bring the work, not an AI strategy.
A rough description of the process, report, handoff, or tool everyone dreads is enough to start.