AI agents that do the repetitive work, not just answer.

• Deterministic parts stay deterministic. Calculations, checks and business rules are code, not prompts.
• Every step is logged, like an audit trail.
• Confidence thresholds route uncertain cases to a person.
• Measure before extending: start with one process, measure time saved and errors, then widen.

I work as AI Automation Specialist at Conio, an Italian fintech company, where automation runs under strict control requirements.

1. 01Discovery. Before any code: interviews, a look at the data you already have, technical and business constraints on the table.
2. 02Architecture. Data schema, component boundaries and failure points decided on paper, when changing them costs an hour and not a month.
3. 03Build. Short iterations with working demos instead of one delivery at the end. AI speeds things up where it makes sense; it never replaces review and testing.
4. 04Handoff & scaling. Documentation, runbooks and a system your team can run without depending on me. No lock-in.

Every project is priced after a scoping call: the same word, "chatbot" or "agent", can mean a two-week prototype or a system in production for thousands of users. What moves the price:

• number and complexity of the processes;
• systems to connect (ERP, CRM, email, shared drives, external APIs);
• quality and variety of incoming documents;
• required level of human review and traceability.

The first call is free and ends with a clear answer: whether the project makes sense, roughly how big it is, and what to do first.