Four phases with deliverables and exit criteria. The sequence matters: skipping discovery is the most common way to end up with a tool nobody uses.
We understand the operation as it is, not as it is documented. We interview the people doing the work, observe the process live, and measure volumes, times and costs. We come out with a numeric baseline: without one there is no way to demonstrate results later.
We define the solution before building it: architecture, technology selection, acceptance criteria, risks and a mitigation plan. We agree in writing on what working means, so that argument does not arrive at the end of the project.
We build in short iterations, with real users testing from the first week. Rollout is gradual: a subset of the volume first, widening as quality metrics hold.
We measure against the discovery baseline and adjust. Once a use case stabilises, we take the next process off the roadmap. In parallel we transfer capability to the internal team until they can run it without us.
The question is not which model to use. It is which part of the work is better done by a machine and which part is better left to a person.
We measure before touching anything. If we cannot compare after against before, we cannot claim anything improved.
We do not resell licences and we hold no channel agreements. We pick the stack per case, and if the right answer does not involve AI, we say so.
We document and train so that we can leave. A vendor who becomes indispensable is a problem, not an achievement.
Permissions, data quality and exception handling are eighty per cent of the real work. We solve those before the nice demo.
Each phase is quoted with defined deliverables. If scope changes, we discuss it beforehand, not on the invoice.
A thirty-minute call is usually enough to tell whether there is a case here. If there is not, we will say so.