-
01
Assessment and feasibility
We analyze your current process, available data and the real cost of the problem. We leave this phase with an honest yes/no on whether AI delivers a return — and if the answer is no, we tell you.
Deliverable: feasibility report with impact estimate -
02
Scope and stack
We define exactly what gets built, with what technology (see technology stack) and against what measurable success criteria — before writing any production code.
Deliverable: scope document + success criteria -
03
Development
We build the model or software in short iterations, with partial deliveries you can review — not a black box that appears months later.
Deliverable: verifiable functional increments -
04
Production deployment
We integrate the solution into your real operation. This is the phase where Spray Analyzer, our reference case, went from model to a system a real farming client uses daily.
Deliverable: system in production -
05
Monitoring and continuous improvement
We measure how the model behaves with real usage data — not just training data — and adjust it as the process changes.
Deliverable: periodic performance report
How we work
Five phases, one clear deliverable each
No AI or computer vision project goes straight to production. This is how we structure every one, from the first assessment to continuous improvement.