Expertise
Industrial AI Consulting
Industrial AI succeeds when it is tied to a measurable operational problem, real data and an integration path into existing systems. It fails when it starts with a technology demo. Independent advice helps separate feasible use cases from expensive experiments.
When this is relevant
- Evaluating AI proposals from several vendors
- Predictive maintenance, quality inspection or process optimization initiatives
- Connected products that collect data without clear use
- Pilots that worked once but never scaled
How we approach it
- 01
Start from the operation
Define the decision or process AI should improve and how value will be measured.
- 02
Check the data
Review sensor, process and quality data for availability, quality and labeling effort.
- 03
Design a scoped pilot
Agree on success criteria, integration requirements and a realistic timeline in advance.
- 04
Plan for production
Address deployment, monitoring, ownership and maintenance before scaling.
Typical deliverables
- —Use-case prioritization
- —Data readiness assessment
- —Pilot specification
- —Vendor and architecture evaluation
- —Scale-up roadmap
Working on something complex across borders?
Tell us about the program, the organizations involved and what is not working yet.