The fit call and workflow intake response are free. The AI workflow audit is paid work that maps the real process and defines what is worth building.
Automation, internal tools, and customer-facing products can all follow this ladder. The right level depends on how much of the real operation is already understood and proven.
Map how the work actually runs across people, tools, data, decisions, and exceptions before committing to implementation.
€2,500 fixed
What you leave with
Current operating map
The real steps, handoffs, systems, and exceptions.
Decision boundaries
What belongs in software, AI, or human review.
Deployment recommendation
Priority, evaluation approach, and first build scope.
The model is rarely the main cost. Scope follows the business system around it: the work, connected tools, controls, and evidence needed for a dependable release.
Inputs, decisions, exceptions, data quality, and access determine how much mapping and preparation the work needs.
More branches or unreliable source data add validation, fallback, and review work.
Integrations, authentication, permissions, approvals, audit history, and human override define implementation depth.
More connected systems and higher-risk actions require stronger controls and QA.
Accepted outputs, edge cases, monitoring, documentation, adoption, and support shape production readiness.
A daily operational system needs more evidence and ownership than a contained first release.
If the workflow is still unclear, the data is inaccessible, or the risk is too high for the available budget, I will recommend fixing the operation first.
Send the current workflow asynchronously, or book a short call if the scope is easier to explain live.
