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An assessment of where AI would actually pay in your operation, what it would cost to run, what your data can and cannot support, and a build order you can fund one step at a time.
Two things make an AI project expensive, and neither is the modelling. The first is starting with a technology and hunting for a use for it. The second is discovering in month four that the data needed to train on was never captured, or was captured inconsistently by eleven branches for nine years.
A consulting engagement exists to surface both before the build budget is committed. It is short, it is fixed-price, and its output is a document you own — including the parts that say a given idea is not worth doing, which is the finding clients most often tell us paid for the engagement on its own.
Every candidate process scored on volume, repetitiveness, data availability and cost of a wrong answer.
What you actually hold, how consistent it is, and the cleaning work that would precede any model.
Inference, storage and human-review cost projected at your volumes, so the business case survives launch.
Which decisions a model may take alone, which need a human signature, and how that is recorded.
Sequenced so stage one is useful on its own — no roadmap that only pays off at the end.
The report is yours to take anywhere. Engaging us to build is a separate decision, made afterwards.
Two to four weeks, depending on how many processes are in scope and how many systems hold the data. Run remotely, with sessions booked around your team rather than ours.
A written assessment, not a slide deck. It is meant to be read by a finance director and by whoever would build the thing, so it carries both the business case and the technical constraints.
We decline AI work where the data does not support it, where the decision is regulated in a way that forbids automated determination, or where the volume is too low for the running cost to make sense. Those are not sales objections to be handled — they are reasons the project would fail after we had been paid, which is worse for us than not winning it.
A business that wants to know where AI would actually pay before it commits a build budget, and which of the ideas it has been sold are not worth doing. It suits an operation whose data is spread across a POS, an ERP or accounting package and a set of spreadsheets, because finding out what that data can support is half of the engagement.
The report is written for two readers at once: a finance director who needs the business case, and whoever would build the thing, who needs the constraints.
Straight answers, including the ones that rule us out.
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