We move phase by phase, with verified results.
We do not start by building everything. We start with a bounded process, agree with you on what success means and how it is measured, prove it with your real data, and only then scale.
Principles.
- 01
Progressive validation
Each phase has defined deliverables and acceptance criteria.
- 02
Fail fast and cheap
The biggest risks are tackled first. Finding out that something is not viable in the proof of concept costs less than finding out in production.
- 03
Human in the loop
The AI assists, classifies and extracts. The person validates, approves and decides.
- 04
Success metrics
The qualitative is translated into indicators before we build. What is not measured cannot be approved.
Five phases, one critical question in each.
Each phase has defined deliverables and acceptance criteria. Finding out that something is not viable in the proof of concept costs less than finding out in production.
- Phase 0
Bounded kickoff
Weeks, depending on the case
Is there a real, measurable, bounded business problem to solve with AI?
Together we define the process, the success metric and the alternative we compare against. When the case calls for it, the kickoff is a feasibility sprint with its own deliverable, useful even if you do not continue with us.
Deliverables
- Bounded process and scope
- Success metric and baseline
- Go or no-go recommendation
- Phase 1
Analysis and design
2 to 3 weeks
What are the real processes, their exceptions, and how do we measure success?
We map the process as it actually happens, not as it is documented: sources, rules, exceptions and the systems we need to integrate with.
Deliverables
- Process map and its exceptions
- Architecture and integrations
- Measurement plan
- Phase 2
Proof of concept
Depends on complexity
Is it technically feasible to reach the metrics with the real data and processes?
We test with your real records, not laboratory data. The proof-of-concept code is the foundation of the pilot; it is not thrown away.
Deliverables
- Test with real data
- Result against the agreed metric
- Open risks and requirements
- Phase 3
Pilot
1 to 2 weeks of deployment, plus testing
Does it work at real operating scale, with the business's volumes and users?
Operation with real users and real volume. Rules and exceptions are tuned with the team that will operate the solution.
Deliverables
- Operation with real users
- Measured volumes and times
- Tuned rules and exceptions
- Phase 4
Production
Variable
Is it ready for production, and which support model continues?
It is integrated into your environment once validated. Before closing, your team carries out a significant change with our support.
Deliverables
- Deployment in your validated environment
- Support with SLA and escalation
- Documented handover
After launch
Your AI is never left alone. Dedicated teams of AI experts and cloud operations; support with SLA by priority and four escalation levels up to the CTO; continuous evolution: rule tuning, new sources, new document types and model monitoring.
- Critical incidents: response team with 24/7 coverage
- Defined times by priority for mitigation and restoration
- Four escalation levels up to the CTO
- AI quality monitoring: accuracy, guardrails and drift detection
Exit
The exit is written from the start
Export of data and configurations, documentation, rights over components, dependencies and license continuity, and handover to your team. You know exactly how you would leave.