Use case · 01
Quality control on the line
Money metric
Cost of defects per month
Industry · 01
Manufacturing
We work where processes are already measured in numbers — the precondition for an honest KPI.
Use case · 02
Predictive equipment maintenance
Money metric
Losses from unplanned downtime
Use case · 03
Energy optimization
Money metric
Energy cost per unit of output
How we work
We agree on the result before we start.
We don't sell technology. We take a measurable process, improve its numbers and are accountable for the figure agreed up front.
01
Goals and KPI
Before kickoff
Together we choose the process and agree on a financial KPI: what we improve, by how much and how we measure it.
02
Process analysis
30 days
Data and process diagnostics, a working prototype, the first numbers on impact.
03
Implementation
From 3 months
We take it to production inside your infrastructure — and are accountable for hitting the agreed targets.
04
Evolution
Ongoing
Quality monitoring, model fine-tuning, expansion to adjacent processes.
Deployment
Your data stays yours.
Three deployment options, from fully isolated to hybrid. The process owner decides; your security team signs off on the requirements.
Isolated
On-premise
Models and data stay inside your perimeter. No external calls — the highest level of control.
- Local GPUs or your own cluster
- Runs without internet access
- On-site certification and audit
Private cloud
Your cloud provider
We deploy in your own account with your cloud provider. You own the infrastructure and the access.
- Your tenant, your keys
- Scales with the load
- Redundancy and SLA
Hybrid
Mixed setup
Sensitive data is processed locally; heavy training runs on dedicated capacity.
- Separation by data class
- Training separate from inference
- Single access control