Method
A process, not a promise.
Every project goes through the same four steps. From the start we know what we measure, how long it takes and who is responsible for each stage.
Diagnosis
Typical duration[DE COMPLETAT]
We analyse processes, data and existing systems. We identify the use cases where AI delivers measurable value — and rule out the ones where it does not.
What happens in this stage
- Workshop with your team and process mapping
- Audit of available data and its quality
- Specification with prioritised use cases
- Budget and timeline estimate
Prototype
Typical duration[DE COMPLETAT]
We build a working prototype on your real data, not generic demos. You test it with real users before any major commitment.
What happens in this stage
- Working prototype on one chosen use case
- Accuracy evaluation on real data
- Testing with users from your organisation
- Documented go/no-go decision
Implementation
Typical duration[DE COMPLETAT]
We build the production system: secured, integrated with existing infrastructure, tested end-to-end and documented.
What happens in this stage
- Production system with authentication and roles
- Integration with ERP, CRM or legacy systems
- Functional, security and performance testing
- Technical and operational documentation
Operation and evolution
Typical duration[DE COMPLETAT]
Launch is the beginning, not the end. We monitor the system, measure accuracy and continuously improve it, with periodic reports.
What happens in this stage
- Monitoring and alerts
- Periodic accuracy and usage reports
- Continuous model improvements
- Support and training for the internal team
Typical durations are defined for each individual project, depending on complexity and data availability.
The first step is a conversation.
The initial diagnosis is free and implies no commitment.