Swayampakula Ramakanth: Thank you and thanks Jennifer and Brad, so I have just one question. I’m trying to understand how you’re thinking about structuring future deals or future – collaborations especially when you have different offerings from BioStrand from simple in silico work. And then if you’re layering this data management on top of it, would – whenever there is a data management transaction. Is that always in conjunction with in silico sort of work or – will you also have a separate data management kind of a collaboration?
Jennifer Bath: No, that’s actually – that’s a great question, RK. Thank you for that. So, the data management tool can actually stand-alone on its own. And I anticipate that the majority of that type of work would actually do so. What we’re finding right now in those conversations is that much like IPA has been doing over the more recent three to four years here with regard to just collecting and storing data. We’re finding that these large pharma companies have been doing that for 15, 20, 25 years. And so, they’re just sitting on massive amounts of data that are just some cost and to store that data, oftentimes, they’re incurring costs, if they’re doing that in a cloud-based format. And many have it just in silos, very disparate, on hard drives, even floppy disks, some on the cloud, et cetera.
And so that primary driver of what we’re seeing in that space with regard to data management. It’s actually – it’s universally with every group that we’ve spoken to it is how do we take all of that information, get it organized in a way that it all can be utilized and analyzed together. How do we have it in one place to store it so we can remove those silos? And then on the other side of that, is they’re actually a way for us to analyze this, right? If we’re going to have data off of 10 years of data off of equipment, we’re going to be utilizing external data sources that we brought in. Sometimes, it’s even people’s notes and publications they’ve been collecting. And so that’s kind of that added layer that I mentioned in terms of the analytical component where not only can we help them by taking this and organizing it in a way where it can all be organized together and store that with very good security for them, but also now instead of it just being expensive and some cost, we can actually turn that into an asset for them.
We can turn that into something where they can now actually analyze it no matter what kind of data that it is. And that’s the unique differentiator, right? So what we’re dealing with is data from any different sort of domain, any different modality, it really doesn’t matter what sort of data it is. We can now use that to enable them to get insights from it, to learn from it, to support their research programs. And that’s part of what gives us the confidence that we’re providing a real differentiator with regard to just the organization and the actual management. So I would say, by and large, that’s what we foresee is kind of those independent programs where we help people just take all of this lost cost or some cost and data that they can’t use and format it in a way that enables them to utilize our LENSai to turn it into revenue generating insights for them.
On the other hand of that, like I mentioned, we are seeing that people are coming in for that type of work are also asking about the other things we’re capable of doing. And so, they are still asking about using LENSai for analytical insights. They are still asking about de novo in silico work and collaborations, but those all can happen in separate programs that wouldn’t necessarily be related.
Swayampakula Ramakanth: Thank you. Thanks for taking my question.
Jennifer Bath: Absolutely.
Operator: [Operator Instructions] Your next question will come from the line of Michael Freeman with Raymond James. Please go ahead.
