Many companies come to us with a clear idea: they want to add AI. Our first question is almost never about technology. It's about the process: what's done today, what it costs and where it breaks.
We have a principle we repeat in every project: not everything needs AI. We only use it when it improves the result.
First, the process
Automating makes sense when one of these conditions is met:
- The process is repetitive and well defined.
- The volume makes doing it by hand unfeasible.
- Operational errors create cost or friction.
- Traceability and control are needed.
Rules, logic or AI
A single process contains very different steps. If a step is stable and always the same, well-made rules handle it better than any model. AI comes in when something has to be understood that doesn't arrive neatly: an email, a PDF or a CV.
And when a decision is needed, we use agents with clear limits that escalate to a person when necessary.
An example: invoices that arrive by email
Reading an invoice that arrives as a PDF needs AI. Checking that the base plus VAT adds up to the total is a rule. Posting the entry needs approval from someone on the team. Three steps, three different tools, one single flow.
The question isn't where we put AI. It's which step of the process improves with it.
How we know it works
We measure from day one: hours automated, response time, cost per operation and errors avoided. All tied to business goals.