Training and capability building
Taught on your machines and your data, so what your engineers learn on Thursday is usable on Monday.
Buy a platform and hope, or build capability that compounds
There is a version of industrial AI capability that consists of buying a platform and hoping. There is another in which four of your engineers can specify a measurement, define a label, design an evaluation and tell when a result is too good to be true. The second one compounds.
Our workshops are built around your data rather than a public benchmark, which changes the experience completely: the awkward parts of your process show up in the exercises, and the questions people ask are about machines they know. Level and depth are set after a short conversation with the team, not from a syllabus.
The most effective format we have found is a workshop followed by paired work on a live project, where our engineer sits with yours on a real problem for a few weeks. It costs more than a course and it is the only format that reliably changes what a team does afterwards.
What is included
Workshops tailored to your engineers' level and your machines' data; paired work on a live project; evaluation and labelling protocols your team keeps; mentoring for data scientists moving into industrial sensing.
Workshops on your data
Industrial signal processing, time-frequency representations, dataset engineering, honest evaluation, edge deployment and sovereign architecture — chosen to fit the team.
Paired work on a live project
Our engineer alongside yours on a real problem, which is where the transfer actually happens.
Protocols your team keeps
Evaluation and labelling protocols written during the training and owned by you afterwards.
Mentoring
For data scientists moving into industrial sensing, where the priors from web or vision data mislead.
Four stages, each with an output you can check
Level-setting
A short call with the team and a look at what they are working on now.
Workshop
One to three days, on your data, with exercises drawn from your process.
Paired project work
Two to six weeks alongside a live problem.
Review
What the team can now do without us, and what is still worth outsourcing.
Sectors that buy this most often
What clients ask before they start
What level do participants need?
Comfortable with Python and basic machine learning. Signal processing background is helpful but not assumed — a large part of the value is precisely that bridge.
Can you train maintenance and quality engineers rather than data scientists?
Yes, and it is a different course: less modelling, more about what these systems can and cannot tell you, how to read an alarm, and how to judge a vendor's claims.
Do you deliver remotely?
Workshops work remotely; the paired project work is better on site, at least for the first week.
Ask about Training & capability building
Tell us the asset or process and what goes wrong. We answer every enquiry within 48 hours on working days, and the first call is free.
- NDAs signed before the first call if you prefer
- Anonymised or synthetic samples are fine to start
- Your data is never used to train models for anyone else
Services clients combine with this one
Ready to find out if it works on your machines?
Start with a free 30 to 45 minute discovery call. Bring the problem, the recordings you already have and your questions. We will tell you honestly whether sensing-based AI is the right tool and what the next step would cost in days, not months.