When it makes sense to call us
AI consulting has filled up with reports nobody acts on. We come from the build side, so this diagnostic is designed to end in a project, not a PDF. It usually fits when you're in one of these situations:
- You know AI should be doing something for you, but you're not sure exactly what.
- Your team has already tried scattered tools and none of them reached production.
- You have three or four ideas on the table and need to know which pays off first.
- Someone handed you a big proposal and you want a second opinion before signing.
What the diagnostic includes
A map of your operation
We spend a few days inside the company talking to the people who do the work, not just leadership. We come out with the real path your processes take: where hours pile up, where information gets lost jumping between systems, and what tasks are quietly being done twice.
Prioritized use cases
Every opportunity gets scored on two axes: financial impact and difficulty of implementation. What lands top-left is where you start. And we also tell you what not to do yet — usually the most valuable part of the report.
A business case with numbers
Hours recovered, cost of implementation, time to break-even. With the assumptions written down so anyone on your team can challenge them. If the numbers don't work, we say so — we'd rather lose a project than build one that doesn't hold up.
A 90-day plan
What happens in each phase, who needs to be available on your side, what needs deciding before kickoff, and what risks are already flagged. This is the document the execution runs on — whether we build it or someone else does.
The diagnostic is delivered at a fixed price and stands on its own: it's not contingent on hiring us for implementation afterward. If the conclusion is that your priority is fixing your data before touching AI, that's what it will say.
How we do it
We work with short sessions and lots of loose conversations, rather than one long questionnaire nobody fills in properly. The first week is listening: we shadow the people running the processes and note what actually happens, which almost never matches the documented diagram.
The second week is testing. We come back with concrete hypotheses — "this can be fully automated," "this needs human judgment and we'll only assist" — and validate them with your people. What survives that conversation makes the plan.
At the close, we present the findings to leadership, with the financial detail up front. If a project comes out of it, the design phase starts from what's already been done — no time lost re-shooting the opening picture.
What you walk away with
- A process map with the bottlenecks flagged
- A portfolio of use cases scored by impact and effort
- A business case with open, reviewable assumptions
- A 90-day implementation plan with milestones and dependencies
- A presentation session to leadership and a follow-up Q&A
Frequently asked questions about AI consulting
How long does an AI diagnostic take?
Between two and four weeks depending on company size and how many processes need reviewing. For an SME under fifty people, two weeks is usually enough. What sets the pace isn't our work — it's the availability of the people we need to interview.
Do we have to hire the implementation afterward?
No. The diagnostic pays for itself and the plan is yours, detailed enough for your internal team or a third party to execute it. We think it's the only way for the report to stay honest: if it depended on selling you the next phase, it wouldn't be.
Does this work if my company is small?
Yes, and it's often where it shows the most. In a twenty-person company, recovering fifteen hours a week of admin work changes what the whole team can take on. The diagnostic's scope and price adjust to the real size of your operation.