Turner Caldwell

So that includes the kind of orchestration layers around mining operations, as well as autonomous execution, which we'll do with partners.

And then also autonomy around the refining processes.

So the site is a mine and a refinery.

A little bit more than vibe coding.

Basically, the way to think about mining is that you have a whole bunch of different disciplines that have to collaborate.

It's a big coordination problem.

You have geologists.

You have a geologic block model.

You'll have a mine plan.

You'll usually have multiple mine plans, like a long-range, medium-range, and short-range mine plan.

That has to be transferred to the mine operations teams that are running large-scale manual mining equipment.

Then what is actually in the ore usually shows up on a piece of paper to the refinery saying, like, get ready to process this.

Okay.

And then the refiner, the folks who are running the refinery have to figure out like kind of the optimal process conditions to maximize recovery of copper, lithium, nickel, cobalt, manganese, iron, graphite, kind of the whole thing.

All of the different metals through the processing circuit.

And so what we're doing is we're kind of integrating everything into one like integrated data frame.

And so there's a lot of work that goes into kind of bringing all of the data feeds back into one like kind of aggregated architecture.

And then we'll build world models

of the mines, of the refineries that we then use to kind of do a reinforcement learning training that we feed up into a reinforcement learning training pipeline.

And then you want to close the loop between what reinforcement learning agents are recommending on the policies that are developed to the actual thing that's happening in the mine.