How it works · Step 1 of 4
Find the work worth automating
Most AI projects fail because they solve the wrong problem well. Before we write any code, we learn how your business actually runs — and where software would genuinely help.
What happens
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Sessions with the people doing the work
Not just leadership. We sit with the team handling the tickets, the invoices, the spreadsheets — because the friction that costs you money is rarely visible from the top of the org chart.
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A look at your data and systems
What you collect, where it lives, how clean it is, and what your tools can talk to. This is usually the single biggest predictor of what is realistic in the next three months.
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Opportunities scored honestly
Every candidate gets rated on business impact, build effort, and risk. Seeing them side by side usually makes the first move obvious — and rules a few favourites out.
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A written recommendation
You get a short, plain-English memo: what we found, what we would build first, what it costs, and what we would leave alone for now. It is yours to keep either way.
What you walk away with
Every engagement is different, but these hold regardless of what we are building for you.
- A ranked shortlist. Concrete opportunities in priority order, with rough effort and payback for each.
- Honest no-go calls. The ideas that sound good but will not pay off yet — and why.
- A fixed scope for step two. Clear price and timeline for a prototype, so there is no open-ended commitment.
- No obligation. If the answer is that you do not need us yet, that is a perfectly good outcome.