And as we continue to gain insights off of that and really turn our focus more specifically to biologic research associated specifically with therapeutic antibodies that was a significant part of that energy and effort put into that first 24 months, where we’ve been taking that and move that into the integration of these wet lab capabilities. But all-in-all, during this period of time where we’re performing those capabilities and building those modules, those are also are all parts of LENSai. And that LENSai remember being a tool that will be rolled out to our clients and then subsequently also to the public to enable people to not only utilize these tools within their own laboratory, but also to be able to encompass larger aspects of what we’re able to do with LENSai.
So going back to the foundational technology and the ability to use LENSai to gather insights again for multiple LLMs and other resources, as well as to be able to use that for data management. And so we have these components that we’ve been building in and with regard to, as you mentioned, the revenue generation from these repeat clients. We have repeat clients that are certainly adding these capabilities into the workflow as they build novel drugs with our therapeutic wet lab arm, which is fantastic. We get that feedback. We have that learning loop. The HYFTs become more enriched and more informed as we continue to build the robust nature of LENSai. But in addition to that, we do have pharmaceutical clients that are using us in a very different type of capacity, both in the partnership model, as well as direct fee for service work directly at BioStrand and not quite as integrated into the wet lab capability.
So I think when we first started talking about the power of LENSai, and again, going back to this idea of foundation AI, where it really is able to integrate these foundational layers that can improve insights and speed and applicability of other algorithms, other models in the industry, meaning that those insights within LENSai, they not only — they’re not just direct tap-in that people can do in LENSai, but they also complement other technology. So they’re not necessarily competitive with these other technologies. So it’s in supplying these types of tools for people to leverage both technological and biological aspects to enhance what they’re learning, which is also a major part of our partnership goals and collaboration goals with these different groups.
And just as a really specific example, for instance we had mentioned previously really using LENSai to answer questions in the biological space that haven’t been able to be answered in the wet lab. And that’s why with the initial partners we took on, we took on partners who had really complicated programs that couldn’t, they could not actually solve in the wet lab capabilities or wet lab capacity. And so far, not only I’ve alluded to already after one of the questions from Will McHale that not only we really gained these wonderful insights that’s helped us to further build our tools, but we’re also having these wonderful successes in the lab, doing things where still so many people today refer to these as things that people will be able to do in the future with AI technologies things like really enhancing the developability of the asset, right, the potential safety and the lack of toxicity as it moves into the clinic.
These are things that we’re refining and optimizing and using as we go through these partnerships. And in one of the partnerships in general right now, they now have brought forward their third and significantly largest program for us. And I think it’s worth emphasizing that in these types of partnerships are absolutely a focal point for us. It’s not just the integration of capabilities in the lab, these partnerships, foundationally, really help us demonstrate that we can tackle these obviously significant problems in the sciences and the biological industry and drug development using LENSai. And at the same time, we’re simultaneously building these additional tools. And so this group that’s come back for the third and like I said, significantly larger program, probably the most rewarding part of that for everyone at IPA is hearing their verbal feedback on BioStrand’s work, where they have just directly told us, hey, we have worked with, just anecdotally, we have worked with a lot of different AI companies out there to try and solve various tasks or answer various questions.
And then working with BioStrand, this is the first group is what they tell us, where the data that comes back and the data that they validate in their lab is actually spot on with the work being turned down at BioStrand from an encyclical perspective. So that type of external validation, wet lab validation, and to have a group, a large pharmaceutical company really say, wow, this is the first time we’ve seen this, this is really it. This is really meaningful for us because now we know we can trust the data, that little AI skepticism that’s out there for so many people was really resolved. And that’s what led us to actually then getting this even larger, much more comprehensive program, which for us is incredibly rewarding. So it really is this balance of continuing to do the wet lab work, continuing to build that LENSai module, but these partnerships, these collaborations with these pharmaceutical companies, and we do believe that also over the next couple of quarters, we’re really looking to push also more into the technology industry for those collaborations as well.
For us, we really believe these are incredibly meaningful. We not only gain additional insights, but we’re furthering to develop these relationships and get the validation we need to really show that this is real. It really does impact and change how we discover new drugs.
Operator: This concludes today’s question and answer session. I would now like to turn the call over to Jennifer Bath for closing remarks.
Jennifer Bath: Great. Thank you so much, Mandeep. So, first of all, as we wrap up today’s discussion on our third quarter earnings call for fiscal year 2024, I’d like to underscore the strategic execution and resilience that have defined our journey thus far. Our achievements this quarter are not just numbers, they’re a reflection of our unwavering dedication to innovation, strategic foresight, and operational excellence. With a significant 20% year-over-year revenue increase, our results speak volumes about our ability to navigate complex market dynamics and deliver on our promises. Our journey toward reduced net losses now at $0.11 per share is a clear indicator of our financial discipline and operational efficiency. This improvement is pivotal as it signals not only our capability to manage expenses wisely, but also our ongoing journey towards sustainable profitability.
The strategic investments we’ve made so far, particularly in BioStrand and our expanded footprints, are laying down the foundation, not just for growth, but for transformative impact on the drug discovery and development landscape. Looking forward, our focus remains sharply on executing our strategy with precision, managing our resources effectively, and driving innovation that meets the evolving needs of our clients and the industry at large. Our commitment to you is to maintain this momentum to continue making strategic decisions that enhance value and to advance our mission of leading the way in drug discovery and development. We’re not just looking at the next quarter, we’re building a foundation for a future where ImmunoPrecise Antibodies stands as a beacon of innovation and excellence in our industry.
We thank you for your continued trust and support. We’re excited about what lies ahead, and we look forward to sharing our progress with you.
Operator: This concludes today’s call. You may now disconnect.
