eGain Corporation Oppenheimer 29th Annual Technology, Internet & Communications Conference

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. 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. Up until the third quarter of our FY 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-plus 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 6 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-plus 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 plus 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.

And we 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.