Sounds like primarily, right? Where in there did machine vision come in? How did you end up making that move?

yeah so in fact there was a project that i got involved in where the oem had sold some robotics that was integrated with machine vision it was quite a complex robotic system that was processing some food products and there was some significant challenges in that project actually and before that i hadn't been doing too much in machine vision i'd be more focused on the automation and other parts of the integration And that project had fallen on my lap and I had to sort of deep dive into the world of machine vision without having done too much before that, but having a lot of automation experience and manufacturing experience and trying to unpick, okay, what are the challenges in this system and how do we actually make this work to the customer's requirements? And it turns out that what happened in that project was that there was a mismatch between customer expectation versus what the OEM could actually deliver for that product, which is quite, quite common across the board.

And so my goal was helping the customer define actually what is the requirement.

So actually, what are you trying to measure? What is your quality specification? How are you measuring it? What is a defect? What is a pass fail look like? And then translating that into some information that the machine vision system could interpret and make decisions from.

So I came in at that interface layer between OEM and end user and bridging that gap.

And actually, if I really summarize ATEC in one line, it's really that's where we sit.

We sit between the technology and the end user and we help bridge that gap.

There's some user component there that's required.