What is the solution to making this kind of equipment and software more accessible and also just more widely deployed and actually used to accelerate science?

So on that end, let me first go into where automation has actually been used to a larger degree, which is, let's say, a big pharma company or the contract research organizations, short form CRO, that kind of gets contracts from scientists, even, to produce data that is almost ready for the clinic and gives them the freedom to then put in something called an IND application to the large regulators to move on towards human trials.

So these kind of organizations, so Biopharma as well as CROs, for example, sometimes do have large automation setups, commonly known, for example, as high throughput screening platform.

And they have pursued this, but the core difference is they invested time to automate one type of workflow.

So it's program bonds.

And then they just run huge screens of like hundreds or thousands or hundreds of thousands of compounds.

in combination with a certain cell that they want to target, so a cancer cell, or they do protein engineering and see what sticks to a certain antibody, or the reverse, what kind of antibody can you build that sticks to a certain target? So they have done this in the past because they upfront invested into this use case very much, but then it was all about just then repeating it again and again and again.

In these shared lab spaces where you typically have a startup or a few people that are trying to push a field forward, what what are they going to be using this for like how do you connect and actually maybe a better starting question would be what are all the available lab automation software right and and sort of currently how they connect together and how are they not connected together