Welcome. I want to thank everyone who's dialed in for the eGain webinar. I'm Brian Schwartz. I'm the Software Analyst at Oppenheimer. This is our 29th Annual Technology Conference. I'm thrilled that we've got the leadership team of eGain with us. We've got Ashu Roy, who's the CEO, and we've got Eric Smit, who's the CFO. They're going to go through a presentation of the company for our listeners. Then I'll come back at the end, ask a couple of questions. Please submit any of your questions from the audience through the chat, and I'll ask them of the management team at the end. With that, I'll pass it over to Ashu. Thank you, Brian, and good morning and good afternoon, everyone. We are excited to have the opportunity to share our story with you. Let me jump right into it. We are the leaders in a trusted knowledge platform for enterprise AI. Now, we have got to this opportunity in an interesting way, because AI has taken off, as we all know, so quickly around us. The market itself for enterprise AI is really looking for a governed and reliable foundation of knowledge content that feeds into AI systems to help automate and enable the workforce. The first area that most businesses are recognizing is ripe for automation is customer operation and customer service. That to us is the largest near-term opportunity because within customer service, we have been operating with a leading market solution that we have for knowledge management, and knowledge has become the instruction for AI systems now. So if you have trusted, governed, verified know-how and content that is feeding AI tools, then the outcome of those AI capabilities is reliable and scalable. So that is a very large opportunity for us to go after. We are seen as leaders, and this is an important inflection point in the market. This was earlier in the summer. Gartner published their first ever Magic Quadrant for customer service knowledge management, and they saw customer service as the functional area in enterprises where knowledge management was becoming the most critical component of driving automation and experience improvement. We have had a lot of success in that specific area, working with large enterprises, mostly in compliance-heavy sectors like financial services, insurance, healthcare, telco, and so on and so forth. JPMorgan Chase, Liberty Mutual, these are some client companies of ours in those sectors. We are also seeing that our business in the knowledge area as it pertains to AI, what we call AI knowledge management, is growing quite nicely, it's u p until the third quarter of our Fiscal 2026, it's grown at about 26% ARR year- over- year, and now that comprises about two-thirds of our total SaaS ARR as a business. As a company, we have the wherewithal to really invest in this opportunity. We are a sizable business with about $92 million of LTM total revenue, with good gross margins and good cash flow, and a very healthy balance sheet. It gives us all the things that we are now investing and driving in a go-to-market motion to take advantage of this under-penetrated market. The problem that we are trying to solve is very simple and quite hard to solve. Businesses, as we all know, have jumped into the AI automation area both feet first. Lots of investment is starting to come short, if you will, in terms of scalable ROI that it delivers for businesses in terms of cost reduction or in terms of better customer experience. Those areas and most of these companies as they have invested, I would say ahead of the foundational capabilities, are recognizing that without a foundation of trusted know-how and knowledge that is feeding these AI tools, you get what people will call as hallucination, unreliable, and so on and so forth. The core issue here is that the knowledge and the know-how and the procedures and policies and compliance requirements that you are feeding into the AI systems are not being governed and managed adequately. That is the reason for this huge challenge with enterprise AI investments not delivering at scale ROI. What is needed is an AI Knowledge Ops approach, the same approach that in the world of software coding is applied to the rigor of managing the software development life cycle. Even with coding agents, in order to develop reliable software and deploy it, you need to have DevOps. Just the same way, in order to have reliable, verified, governed knowledge that is feeding your AI agents, you need to have AI Knowledge Ops. That is a cycle that we have been marketing and bringing out to the clients, and now Gartner is adopting that terminology themselves in their Magic Quadrant. They talk about AI Knowledge Ops as something which is a continuous process of sourcing and capturing the relevant know-how and knowledge in a business, making sure that you synthesize and curate it appropriately, personalizing and publishing it through the right channels, and then learning from the usage and bringing it back into the loop of Continuous Knowledge Ops. That is the platform that eGain offers. Enterprises are choosing eGain for four reasons. First of all, we have been doing this for 20+ years, now over 25 years, actually. That shows because we have all the corner cases and all the configurable requirements that are part of our platform, which then allows businesses like a JPMorgan Chase to go live with our platform in six months. We are talking about JPMorgan Chase. I will talk about our rollout at JPMC. We are now deployed to 125,000 users at JPMC in the U.S. This is at scale. We are also the only ones that have the ability to deliver closed-loop improvement of that knowledge with the AI Knowledge Ops approach that I talked about. This is unique to us. We provide a governed system of record with traceability and compliance, which is critical for these large enterprises. Finally, the security and infosec and integration requirements that large companies are absolutely demanding if they are going to build out an infrastructure of trusted knowledge on which you build out AI capabilities for customer operation automation. The kind of value we deliver, and this continues to be more and more impressive as we deploy more at scale. In a large telecom client of ours, we are delivering over $7 million of attributed benefits with our AI tools, supporting now 12,000+ customer service advisors. With a branded manufacturer, this is Specialized Bicycle Components, as you all probably know about. They have reduced their abandonment rate by nearly 50% because they are automating a lot of their customer service with our self-service and agent assist. With a consumer brand like PMI, which used to be Philip Morris, they call themselves PMI now. We are rolled out to 64 local markets around the world with one knowledge platform that is powering all their AI initiatives. In a place like insurance, just to jump ahead, this is Country Financial in the U.S. They have now seen a 20%-30% productivity uplift from using our AI-powered knowledge capability to assist their agents as well as their field service personnel. So it is a true knowledge platform that starts out in the customer operation area and then extends out to the whole enterprise. The market is still very early for AI knowledge management. When we say AI knowledge management, what we mean is the need for AI to have an underlying foundational knowledge capability feeding the AI systems. That is what we mean when we say AI knowledge management. Our assessment based on internal data and market feedback is that only about 4% of the market today is in a state of what we call high maturity, which means they have a system of knowledge management, which is governed and AI-ready, so that they can use that reliably to feed their AI systems to drive automation. Most of the business out there in our target market, which is about 1,000+ employees is our ICP. In that target market, we believe that over 80% of those businesses are either in low maturity mode or in the medium mode, which means they cannot really build at-scale AI systems, which are based on that kind of not governed, not verified knowledge content. That is a huge problem. Customer operations, as I mentioned earlier, is our primary landing point in businesses for two reasons. One, it is a present opportunity because businesses see a big customer service area as an area of both improving experience for their customers as well as automating that experience. So it is a win-win. That, for us, means it starts out in the contact centers in these large compliance-heavy organizations with AI assist tools that are powered by our knowledge, and we provide the entire stack, but we also provide the APIs to drive third-party AI tools. Followed by customer self-service, because that sort of naturally builds confidence when you have your agents who can feel good about the assistance they are getting, that same knowhow can be put in front of your end customers to drive self-service. And then finally, we see this opportunity to enhance with all the talent that is being now unlocked because of automation and customer care groups, turning those tenured staff into sales agents, into sales staff. That is a big growth curve that we see businesses starting to invest in. More of our clients who have kind of gotten the benefits of service automation and efficiency gains are starting to move some of that human resource talent into sales motions. I mentioned JPMorgan Chase, so let me just talk to you a little bit about that. We have been working with them for over a year now, and we are deployed at this point to 125,000 users in the U.S. This is predominantly in their CCB, which is their Consumer Banking Group, the Community Bank Group. This is across six lines of business. W e were able to, we started out this journey with them, when we started it out, the expectation was that it would take about a year to deploy, given their complexity and the scale. We managed to do that in six months. That is largely because of the fact that the knowledge platform that we offer now has the advantage of automation that we have built into it with AI. That makes the process of sourcing, curating, publishing so much faster than what used to be. So it is a win-win now with AI and knowledge. Knowledge is needed to drive reliable AI outcomes, and AI technologies are useful in automating and making it easier to manage enterprise knowledge. So this is the Gartner Magic Quadrant I mentioned earlier. This came out in July. It is the inaugural Magic Quadrant for customer service knowledge management systems. Gartner did this largely because they are seeing this as a category that is emerging in the market, a software, a service category, an infrastructure that is needed to build the next level of customer service experience improvement and operational efficiencies. We are proud to have the position we have in this. Obviously, it is based on the focused work we have done, our proof points with large companies and customers, and our go-to-market moving forward. A couple of comments I want to share with you, which will give you a view into how businesses are seeing a provider like eGain, an AI knowledge platform provider. What you see is a dual track assessment and recognition from these large companies. These are comments made by our execs in our client companies. The first one happens to be Healthfirst, which is a large health insurer out in the New York area. What you see is the CIOs are starting to recognize, I would not say this is universal among the CIO community yet, but it is a growing awareness, which is something that we are feeding and marketing and driving more. That is that in order for reliable AI deployments and rollouts, reliable at scale, you need two foundational capabilities. You need trusted knowledge, and you need reliable data. If you have those two, data is the context, knowledge is the instruction. Combining the instruction and the context into your AI systems, into your agentic systems, delivers reliable experiences, which are both automated or assisted, depending on your use case. Also, we see businesses on the operations side seeing the importance of an AI knowledge platform because they have been trying. A lot of companies now are in the situation where they have promised to their CEO and their board that they are going to deliver real financial benefits out of all the AI investments in terms of cost reduction or revenue goals. We are, at this point, focused more on the efficiency side of the equation. That part, businesses are recognizing, and the head of operations and the head of CX are recognizing that that reliable knowledge infrastructure enables successful AI outcomes. That to us, that duality is important, right? We are selling to the business and to the tech groups because we see that the two of them have to work together to deliver scalable AI outcomes. What is our growth plan as we are executing it now? We are investing in our marketing and looking at not just the areas of traditional knowledge management, which used to be purely in customer service, but also radiating out into CIOs and the tech groups of these organizations because they are looking at ways to solve their lack of scalable AI proof points and ROI. We are combining those two, and we are driving our marketing in both those groups. Both in the business side of it, which is Head of Customer Service, Head of CX, Head of Customer Operations, and in parallel, the CIO and the CTO and the Head of AI. We are also expanding very actively in our existing customer base because that we see as a big opportunity, going beyond just the customer service groups and going across the enterprise. As a result, we are seeing good new logo activity, which is encouraging for us. We are also seeing great validation. We had a fantastic customer event in London in May this year. Now we are going to have the next one, the big one in Chicago in October. We are accelerating our new product roll-outs in the area of enabling more and more AI systems to take advantage of our knowledge platform, as well as customer stories around the success they are getting from our investments in technology and product. With that, let me move it across and have Eric talk a little bit about the financials, and then we will come back and wrap it up and take questions. Great. Thanks, Ashu, and thanks, everyone, for joining us. Before reviewing the financials, a reminder that we have not released our financial results for our fiscal year ended June 30th, 2026. The numbers I am presenting are as of our Q3 quarter ended March 31st. Starting with our ARR, as Ashu mentioned, we have seen accelerated growth in our AI knowledge ARR since making the strategic focus shift to AI Knowledge as our core business. AI Knowledge now represents roughly two-thirds of our SaaS ARR, up from where it was less than 50% three years ago. Looking at our gross margins on an LTM basis, we have seen a healthy increase in both our total SaaS gross margins, as well as our total gross margins. As you can see, our SaaS gross margins on an LTM basis are now up to 80% over the last year. Now, looking at our bottom line and operating cash flow, I am pleased to report that while increasing the investment in our AI Knowledge product, resulting in the success that Ashu has highlighted, we have driven meaningful operating efficiencies across the business, resulting in improved adjusted EBITDA up to 17% on an LTM basis at the end of Q3, and also strong operating cash flow, as you can see, up significantly year-over-year. This is, as Ashu had mentioned, we are increasing our focus, continuing to invest in the AI Knowledge business, but through operating efficiencies, have seen the meaningful improvement in both our bottom line as well as the cash flow. When looking at our ending financial results as of the end of Q3, we ended the quarter with roughly $80 million in cash and no debt. I think very exciting place for us to be, as we look forward to our fiscal 2027, well-positioned to really capitalize on this exciting opportunity. In closing, I thought I can hand it back over to Ashu for some closing comments. Thank you, Eric. If you look at where we are now, we are sitting at a point where knowledge is becoming fundamental and foundational to enterprise AI success. Businesses are looking to build out that trusted governed knowledge infrastructure, and that is something that we are leading the market with a proposition, with product and proof point and the ability to scale beyond where we are. Our AI Knowledge ARR is growing, as I mentioned, and Eric repeated as well. Our operating efficiencies are good, so we are able to drive more and more of our investments back, profitability-wise, into building out more capability and going to market to drive growth. That foundational capability, which we are delivering to the market, is something that Gartner is recognizing is a category in and of itself, and the inaugural Magic Quadrant has placed us at least as the leading player in that market. So we are very excited, and we look forward to sharing more of this progress and success with you as we move forward. Thank you. We will take questions, if any. Thank you, Ashu and Eric. We do have some questions, came into the field. I had a few too. Why don't we start with the right to win? You showed the slide where your AI Knowledge Hub ARR is accelerating to 26% year-over-year growth. It looks like it is 64% of your total SaaS ARR. When a large enterprise or a big bank runs a competitive knowledge management evaluation, what specifically wins the deal for eGain versus, say, a CCaaS platform's bundled knowledge module? Yeah, that's a good question. You're right. There is always the incumbency, and that is something we compete with. What we see, we sell against incumbents in the CCaaS space or the CRM space a lot. The big advantage that we bring, I would say two things, right? One is our solution is much more complete and comprehensive when it comes to AI enablement for that business. Most businesses are looking to now drive automation with agentic capabilities on top of the knowledge system. Our knowledge platform provides a level of granular control and compliance capabilities, which are quite critical for these big enterprises. That is one advantage, because as people expose, as businesses expose their internal knowledge and content to AI systems, they're always very worried that they might end up providing the wrong content or the wrong input. That is something we do an excellent job of creating a trusted knowledge capability with continuous evaluation. The second thing, which is critical for large companies, is that most of them are looking now at knowledge management not as a functional capability. They're looking at it as an enterprise infrastructure. Players in CCaaS or even in CRM tend to look at knowledge as an add-on into the functional capability of customer contact or customer engagement only. When CIOs and tech groups get involved in these enterprises, they would like to have a platform that is more enterprise-wide infrastructure, and that's another advantage that we bring to that discussion. Just to follow up on that. Thinking about the AI era here and your capabilities, how much of the right to win is the content, the taxonomy, the governance, versus the AI agent layer itself? Ashu, which one of those two do you think is harder for a competitor to replicate? That's another good question. I will say that building agentic tools, and this is not to take away from the amazing technological capability that language models offer. It's just mind-boggling. We are all in agreement on that. Building agentic AI tools is quite easy. In other words, it's not very hard for enterprises to cook up agentic AI layers, which will handle specific workflows and processes that they're trying to automate. The hard part is making sure that the input that is being fed into these agentic layers is governed, trusted, reliable. It's the infrastructural messiness, if you will. How do you corral and make that manageable is where the delays are in most of these organizations. We are working with a very large insurance company, and I won't name them. They have been stuck with one group that has deployed agentic capabilities on our platform, but the next larger group, which is much larger, is stuck because their underlying knowledge still needs to be moved into the eGain system. We are helping them with that, but that's where the slowdown happens. That's good. We've got two questions from the field. I think they may be for Eric because they're financials. One is just on the structure of the contract, so I'll just read it. Does higher AI-driven deflection reduce your customers' agent seat counts? Then talk about how your contract structure is either exposed to that or insulated from that. No, good point, and I think certainly Ashu can contribute that as well. If you look at our AI knowledge offering, from a licensing standpoint, we offer a seat-based pricing, so that's the traditional user-based. We've also always historically offered outcome or session-based type pricing. What we see is sort of a shift, and we offer that flexibility where customers can move from one to the other. Typically what we do, the standard is three-year contracts that we commit to, given the target customers that we sell to. Generally, they're looking for that long-term commitment for a multi-year initial agreements. What we're seeing is that there's still that combination, typically in new opportunities, where there's a need to provide, as we mentioned with JPM, sort of actual users of the underlying software as a solution. But then in addition to that, there's going to be the agentic layer that will be driven off sessions. So we see that over time. As efficiencies drive down the number of agents, then for us, that just means that we'll see more usage-based or outcome-based revenue coming as a result of that. Okay. Second question that came into the field was a mechanical question. Eric, I'm thinking about, you showed us your bookings here, or at least the AI Knowledge Hub ARR is accelerating. What is the bridge that would cause the total revenue growth to accelerate? Without giving any timing, because obviously you're in a quiet period right now, but what are the mechanics that would need to happen to see revenue follow what you're seeing with your ARR? Nope, a good point, and it's obviously something that we're monitoring and tracking, and we'll certainly be providing more information on visibility to this when we do our Q4 results and guidance for fiscal 2027. But as we pointed in that ARR, obviously, is the leading indicator that then translates into revenue. So what we've seen is that from an ARR perspective, up until the Q3, that's roughly two-thirds of our ARR. So continuing that growth rate while we manage the, what we'd consider our non-core business. So at the moment, we see that part of the business as operating very efficiently. It's very profitable, and that's contributing to the growth in the AI knowledge side. But that's something that obviously, as we expect over time, that the ARR or effectively the business becomes beyond two-thirds up into 80%, 90%. Then, with that, we'll start to see that translate into the revenue growth. So again, not giving any specific timeline, but over a couple of years, you would expect that as the business becomes predominantly AI knowledge, then the ARR growth rate and the revenue growth rate certainly should be closer to where they are from where they are today. Yeah. There's a question just about cash and operating cash and uses of cash. The first question was, through the first three quarters, the operating cash flow was quite close to $19 million, 27% margin. But Q3 alone was actually an outflow of $1.8 million. The first question was, what drove that intra-year swing? The second question, and maybe that's for Ashu, is the uses of cash. You've got $80.5 million of cash and essentially no debt. What is the capital allocation priority between buybacks, M&A to fill AI product gaps, or investing in sales capacity? Sure. First off on the variability of the cash flow generation, I think if you look back historically, our Q4 and Q2 are typically our strongest quarters historically of booking. As I'd mentioned, typically what we're doing is multi-year contracts with annual payments upfront. Generally what we see is that a big uptick in our AR balance at the end of Q4, resulting in strong cash inflows in our first two quarters, and then that stabilizes into Q3 and Q4, and then it repeats itself. So it's more just the seasonality of the timing of our bookings, which was driving the movements in the cash flow. Then maybe Ashu. Yeah, I can add to that without getting on screen. I think the order right now, and this is obviously something we keep looking at more and more. For the last year and a half, as you have seen, we've added much more fuel to the fire on the product development side, increased our investment there. Now I think we'll probably hold it stable with marginal increase, but now we are going to be investing very heavily, and obviously smartly into go-to market and marketing and sales brand building, and then scaling out our field capabilities. So that's our primary focus in this coming fiscal year and the next year beyond that as well. Of course, the inorganic stuff is always on our radar. We keep evaluating. Right now, we feel very good about our platform and product. That's something which we keep assessing as we move forward. And I think the final piece is that as we've done in the past, we have bought back shares at prices that we feel is appropriate. So that certainly has been the use of cash as well over the last couple of years. Okay, last question. You announced four new products at the eGain Solve London. I know you have another conference coming up now. Maybe you can just remind us again those new products that you announced at the London conference. And then how should investors think about the pricing? Are they separate SKUs? Are they embedded in the platform for retention enhancers? How should we think about those new product introductions? Yes. And thanks for bringing it up. So broadly, the products fall in two categories. One is tools and capabilities in the platform that help businesses automate knowledge management and observe the health and governance on an ongoing basis. Right? So that is a platform-level capability that we are now pricing as incremental to the platform. So that's almost an add-on. That is something a lot of customers are showing interest in that. And the second bucket is delivering more out-of-the-box agentic solutions. So one of the ones we announced was the IVA, which is an incarnation, if you will, of our AI Agent for the voice channel. So that's an area which is more about using our knowledge capability and then delivering automation with trusted actions on the voice channel. So those are the two areas. The second one is more priced on outcomes. The first one is more priced on a platform basis. Perfect. Well, we're out of time. These guys have meetings they got to get to. I want to thank Ashu, I want to thank Eric for presenting eGain today. Thank you. Thank you. Thank you, Brian. Thank you. Yeah. Take care.