Latest / WTR Small-Cap Spotlight / eGain Corporation (EGAN): Turns Messy Enterprise Data into Trusted AI Answers

Transcript

eGain Corporation (EGAN): Turns Messy Enterprise Data into Trusted AI Answers · WTR Small-Cap Spotlight

• 0:01
  SPEAKER_01: Welcome to the WTR Small Cap Spotlight Podcast.
• 0:04
  SPEAKER_01: I'm your host, Tim Gerdeman, Vice Chair and Co-Founder and Chief Marketing Officer of Water Tower Research.
• 0:11
  SPEAKER_01: Today I'm joined by Gantum Gard, Vice President of Finance at EGain Corporation NASDAQ Ticker Sumble, E D A N.
• 0:19
  SPEAKER_01: EGain is a leader in AI-powered knowledge management and customer service automation solutions.
• 0:25
  SPEAKER_01: With over 25 years of experience in knowledge management, E-Gain helps enterprises unify siloed content, automate trusted knowledge workflows, and deliver measurable AI ROI through its AI Knowledge Hub.
• 0:40
  SPEAKER_01: Global 2000 companies across industries rely on e-Gain to transform customer service, improve employee productivity, reduce cost, and accelerate AI adoption.
• 0:51
  SPEAKER_01: The company recently launched several products, including AI Knowledge Suite for Retail Banking, AI agents for the Contact Center and Salesforce, an IVA product for voice self-service, and a composer platform that opens e-gain's architecture to developers.
• 1:08
  SPEAKER_01: Also joining is my WTR technology equity research colleague, James Kisner.
• 1:13
  SPEAKER_02: Good morning, gentlemen.
• 1:14
  SPEAKER_02: Thank you, Tim.
• 1:15
  SPEAKER_02: It's great to be here, and I appreciate the opportunity to speak to you and your listeners.
• 1:20
  SPEAKER_01: Got him, you joined e-Gain after a path that included investment banking and engineering, as well as various business operation roles at Oracle.
• 1:28
  SPEAKER_01: Now you're in a finance leadership seat at a company building AI knowledge infrastructure.
• 1:33
  SPEAKER_01: What attracted you to e-Gain at this particular moment?
• 1:36
  SPEAKER_01: And for listeners new to the story, how would you describe what e-gain does in plain language?
• 1:42
  SPEAKER_02: Yes, happy to do so.
• 1:44
  SPEAKER_02: So before e-Gain, I spent nearly 15 years in investment banking advising enterprise SaaS companies.
• 1:50
  SPEAKER_02: And prior to that, as you said, had various uh development and business roles at Oracle.
• 1:54
  SPEAKER_02: So I've had a pretty much front row seat to how enterprises successfully build and scale software.
• 2:00
  SPEAKER_02: What really drew me to e-Gain was its 25-year deep domain and technical mode and knowledge management.
• 2:06
  SPEAKER_02: And this was suddenly becoming incredibly valuable to customers in this new AI first world.
• 2:12
  SPEAKER_02: Further, the company's core business was growing at over 20% year over year.
• 2:17
  SPEAKER_02: And the company had a healthy balance sheet and cash flow profile.
• 2:21
  SPEAKER_02: That combination was attractive to me.
• 2:23
  SPEAKER_02: And I was excited to be joining the company at what seemed to be a really pivotal stage in its growth strategy.
• 2:29
  SPEAKER_02: A simple way to describe e-Gain is that it's a software company that provides offerings to help B2C enterprises reduce their contact center costs and importantly still maintain their customer satisfaction.
• 2:45
  SPEAKER_02: It achieves this by enabling AI agents to resolve questions autonomously and assisting human agents with questions that get escalated to them.
• 2:55
  SPEAKER_02: But the real story is the knowledge platform underneath the hood.
• 2:58
  SPEAKER_02: Our AI Knowledge Hub connects and unifies content from silos like SharePoint, Salesforce, and ServiceNow into an AI-ready knowledge layer.
• 3:11
  SPEAKER_02: That foundation is what allows both AI models and human agents to provide accurate, compliant answers at scale.
• 3:20
  SPEAKER_01: Excellent.
• 3:20
  SPEAKER_01: Well, thank you for that.
• 3:21
  SPEAKER_01: And I have to say, as in a personal side, I always have an appreciation for former investment bankers that go into corporate America because obviously you've seen hundreds, if not thousands, of business models.
• 3:30
  SPEAKER_01: So you must be bullish on this one.
• 3:32
  SPEAKER_01: So with that, I'll turn it over to James.
• 3:35
  SPEAKER_00: Thanks, Tim.
• 3:37
  SPEAKER_00: So your CEO, a Shu, has framed knowledge management as core AI infrastructure, a must-have, not a nice to have.
• 3:46
  SPEAKER_00: So that framing has been central to how E-Gain talks about the AI knowledge hub.
• 3:50
  SPEAKER_00: Can you kind of unpack that for us, what that actually means for an enterprise that is deploying generative AI today?
• 3:56
  SPEAKER_00: And why do you think that E-Gain believes trusted knowledge is the foundation that everything else builds on?
• 4:02
  SPEAKER_02: Yeah, thank you for the question, James.
• 4:04
  SPEAKER_02: So to start off, MIT research shows that 95% of enterprise AI pilot programs fail to deliver measurable PL impact.
• 4:13
  SPEAKER_02: And the root cause is almost always the same.
• 4:17
  SPEAKER_02: You have garbage coming in, you're going to have garbage going out.
• 4:20
  SPEAKER_02: Companies point their LLMs at their data, hoping for business outcomes.
• 4:24
  SPEAKER_02: But if the underlying knowledge is stale, missing, or inconsistent, the AI output will reflect that, producing hallucinations, weak answers, and in regulated industries, real compliance risk.
• 4:37
  SPEAKER_02: So the problem really isn't about the model, it is the knowledge that the model draws from.
• 4:44
  SPEAKER_02: And ASHU framed the infrastructure positioning really nicely at our Solve London event in May.
• 4:52
  SPEAKER_02: ASHU reinforced that the quality of knowledge determines the quality of AI outcomes.
• 4:57
  SPEAKER_02: However, enterprise knowledge isn't simply static data that just needs to be cleaned once and then forgotten about.
• 5:05
  SPEAKER_02: It's the critical instruction layer for AI.
• 5:08
  SPEAKER_02: And it needs to be governed with the same rigor as software code by being continuously monitored and optimized.
• 5:16
  SPEAKER_02: Importantly, once an enterprise has a trusted knowledge layer, only then can they effectively scale their AI initiatives.
• 5:24
  SPEAKER_02: That's exactly what our AI Knowledge Hub facilitates.
• 5:27
  SPEAKER_02: The ROI, when customers get it right, is very compelling.
• 5:32
  SPEAKER_02: One customer, as an example, who is using our AI Knowledge Hub to improve its customer service offering saw a roughly 35% improvement in first contact resolution, a 50% reduction in agent training time, and a 30-point gain in NPS.
• 5:49
  SPEAKER_02: As a result of these types of customer success stories, our AI Knowledge Hub ARR grew by 26% year over year to 48 million in Q3 2026, which is our recent reported quarter.
• 6:04
  SPEAKER_02: This represents nearly two-thirds of our SaaS ARR today, and up from below half just two years ago.
• 6:14
  SPEAKER_02: This reflects the value our customers are seeing when they get the knowledge foundation right.
• 6:22
  SPEAKER_00: We've all experienced hallucinations and we all know about uh kind of garbagey garbage out.
• 6:26
  SPEAKER_00: We've all heard about um kind of the hiccups people are having um as they deploy AI.
• 6:31
  SPEAKER_00: So e-Gain has launched several uh new AI products recently, including the AI Knowledge Suite, Free Trail Banking, um, the AI agent IVA evaluator uh in Aegetic Studio, which I believe you just launched at your Sol26 event in London.
• 6:47
  SPEAKER_00: So with AJTX Studio in particular, it sounds like the goal is to help AI move beyond simply answering questions and actually do some things, resolve some customer issues.
• 6:55
  SPEAKER_00: So, how do these launches fit together and you know what are the bigger problems that customers are asking EGAN to solve?
• 7:03
  SPEAKER_02: Yeah, so the common thread is that customers have moved past the pilot phase and are trying to scale AI into production.
• 7:09
  SPEAKER_02: And that's where the hard and unexpected problems emerge.
• 7:13
  SPEAKER_02: The AI Knowledge Suite for Retail Banking addresses this directly for our banking customers who have very specific regulatory requirements around how AI answers are generated, delivered, and audited.
• 7:25
  SPEAKER_02: The IVA, our interactive voice agent, fills the voice channel gap in particular.
• 7:31
  SPEAKER_02: Voice is still a very high volume, if not the highest volume, contact channel for most enterprises, but historically been relatively weak for AI.
• 7:41
  SPEAKER_02: IVA brings the same governed knowledge foundation that powers our digital channels into voice.
• 7:46
  SPEAKER_02: The AI understands spoken queries, retrieves the right answers, and either resolves the call or hands off to a human agent, meaning escalates to the human agent with full context, no rigid threes and no customer repetition.
• 8:03
  SPEAKER_02: Evaluator addresses quality assurance, ensuring AI-generated answers remain accurate and compliant as knowledge evolves.
• 8:11
  SPEAKER_02: It continuously monitors responses against the government knowledge base and surfaces issues before they reach customers.
• 8:18
  SPEAKER_02: This is a solution that is generating a lot of excitement for our customers and prospects.
• 8:30
  SPEAKER_02: This is a fundamentally different problem.
• 8:32
  SPEAKER_02: An AI that can answer what is my return policy is really useful.
• 8:38
  SPEAKER_02: But an AI that actually processes the return, verifying eligibility, triggering the workflow, confirming with the customer is transformative.
• 8:47
  SPEAKER_02: Agentic Studio gives teams the tools to build those end-to-end resolution workloads on our knowledge foundation, with GodRails governing what the agent can do autonomously.
• 8:58
  SPEAKER_02: The underlying question across all these launches is the same.
• 9:02
  SPEAKER_02: How do you get AI working reliably in production, not just in pilots?
• 9:07
  SPEAKER_02: And that's what we've built our infrastructure to answer.
• 9:11
  SPEAKER_00: So many problems I think we're all familiar with there that you're solving.
• 9:14
  SPEAKER_00: So we're grateful for that.
• 9:15
  SPEAKER_00: Um, so one launch that stood out is Composer, uh, a more go-to-market-oriented angle that opens uh your architecture, developers, and partners through APIs and model context protocol or MCP.
• 9:28
  SPEAKER_00: You've also announced enterprise AI platform connectors for Copilot for Microsoft, Anthropic Claud, uh Google Gemini, and Cursor.
• 9:37
  SPEAKER_00: So, how should investors think about Composer's role in the strategy and what kind of partners and use cases are you seeing the most engagement for so far?
• 9:45
  SPEAKER_02: Yeah, so Composer is really about meeting the market where it's going and expanding our goal-to-market channel.
• 9:53
  SPEAKER_02: Our customers are asking how can eGain's knowledge connect into our AI stacks that we're already building?
• 10:01
  SPEAKER_02: Composer answers that with an SDK and API environment that lets developers and partners build directly on top of our knowledge hub in a very easy and seamless manner.
• 10:11
  SPEAKER_02: Critically, we are fully open on the model side.
• 10:14
  SPEAKER_02: You can bring your own model, bring your own agent, whether it be from OpenAI, Copilot, Gemini, Cloud, and others, and we will seamlessly integrate.
• 10:23
  SPEAKER_02: From an investor perspective, Composer expands our total addressable market by creating a new distribution channel through developer ecosystems and system integrator partnerships.
• 10:35
  SPEAKER_02: Early engagement has been very encouraging, and we look forward to sharing more about go-to-market progress on Composer relatively soon.
• 10:45
  SPEAKER_00: Sounds great.
• 10:45
  SPEAKER_00: I mean the history of the tech industry has pretty much shown that open usually wins, maybe with the exception of the uh the iPhone.
• 10:52
  SPEAKER_00: But um that's like a lot of opportunity you're unlocking.
• 10:54
  SPEAKER_00: Um so um management has talked about a meaningful increase in RFP activity over the last 60 days, a lot of that from the Fortune 1000 banking, insurance, and healthcare uh industries.
• 11:05
  SPEAKER_00: What do you think is driving that surge?
• 11:07
  SPEAKER_00: Is it better awareness?
• 11:08
  SPEAKER_00: Is it the maturation of Gen AI platforms or is it something else?
• 11:12
  SPEAKER_00: And what are buyers consently asking for in those RFPs?
• 11:16
  SPEAKER_02: Yeah, so we did call this out in our Q3 earnings, and we secured multiple customer expansions and saw a meaningful increase in RFP activity, particularly in the last 60 days of that uh Q3 quarter.
• 11:35
  SPEAKER_02: And this was driven by growing awareness of the garbage in garbage out problem, as I mentioned earlier.
• 11:41
  SPEAKER_02: Two things are converging converging.
• 11:43
  SPEAKER_02: First, the pilot phase is mostly over.
• 11:47
  SPEAKER_02: Enterprises that have spent the last couple of years experimenting with AI are now under pressure to show ROI in production.
• 11:55
  SPEAKER_02: And contact center operations is one of the highest ROI and a highly measurable environment that can be transformed by AI, given its highly labor-intensive nature.
• 12:08
  SPEAKER_02: Second, the failure modes from first gen AI deployments are now well understood.
• 12:13
  SPEAKER_02: Organizations that went live with our proper knowledge layer experience, hallucinations, and compliance incidents.
• 12:18
  SPEAKER_02: And now they're back in the market for a solution that is structured and enterprise grade, that delivers trusted knowledge, which is what our AI knowledge hub does.
• 12:29
  SPEAKER_02: In terms of what buyers are asking for, governance and auditability are table stakes, especially in regulated industries such as banking, insurance, and healthcare.
• 12:39
  SPEAKER_02: They want to know where every answer came from, who approved it, and how it can be updated when policy changes.
• 12:45
  SPEAKER_02: AI readiness and an open architecture that connects into existing systems, CRM, CCAS, and calibration systems, for example, are also highly valued, both of which are core to how e-gain is built.
• 13:00
  SPEAKER_00: Sounds like you're really helping some tough industries uh move forward with AI.
• 13:04
  SPEAKER_00: Um, so partner source opportunities are up, I think 67% year to date is the last quarter.
• 13:10
  SPEAKER_00: So that's a meaningful shift in how pipeline is being built for you, I think.
• 13:14
  SPEAKER_00: So can you walk us through how that partner ecosystem is evolving, including new arrangements where companies are building on top of the e-game platform and what that means for e-game's reach in the market over time?
• 13:26
  SPEAKER_02: Yeah, so this increase reflects a deliberate go-to-market shift.
• 13:30
  SPEAKER_02: We've been investing meaningfully into expanding our partner ecosystem and are seeing the results of that work.
• 13:36
  SPEAKER_02: Our solvent event in May underscored that momentum.
• 13:40
  SPEAKER_02: It had the largest partner contingent in our history with dedicated boots where partners showcased customer solutions built on e-game.
• 13:49
  SPEAKER_02: Our new partnerships with Ascent AI and here.io are particularly relevant.
• 13:54
  SPEAKER_02: Ascent AI brings regulatory compliance intelligence and here brings an enterprise browser capability to our retail banking vertical solution.
• 14:03
  SPEAKER_02: And both are embedded into our open architecture to strengthen the solution's value proposition.
• 14:09
  SPEAKER_02: We continue to invest in our partner ecosystem and develop our platform into one that can easily integrate with partners' offerings to drive pipeline and new logo opportunities.
• 14:24
  SPEAKER_00: So Salesforce has been pushing its agent force contact center.
• 14:27
  SPEAKER_00: And there's a broader question about how CRM and contact center platforms are converging.
• 14:32
  SPEAKER_00: And how does e-Gain think about its position in a world where some of the major CRM and contact center vendors are building more native AI capabilities?
• 14:39
  SPEAKER_00: And where does e-gain's open architecture and knowledge specialization give the company a durable advantage?
• 14:47
  SPEAKER_02: So we see Salesforce, Agent Force, and similar moves as really validation of the market opportunity, not a threat.
• 14:54
  SPEAKER_02: CRM platforms build AI well within their own data domains, cases, contacts, transactions.
• 15:00
  SPEAKER_02: Where they seemingly struggle is unstructured knowledge, content governance, compliance, and making such knowledge AI ready across the enterprise.
• 15:11
  SPEAKER_02: Knowledge isn't static, it continually evolves alongside regulations, policies, and products and needs to be managed as an ongoing instruction layer for AI, not a one-time cleanup event.
• 15:24
  SPEAKER_02: We are hearing that a lot of Salesforce-based AI projects stall at the production stage because of content lifecycle and quality issues.
• 15:33
  SPEAKER_02: That's what we solve for.
• 15:34
  SPEAKER_02: We also benefit from being generally open.
• 15:37
  SPEAKER_02: EGain integrates with all the major CRM, CCAS, and AI platforms and many other systems.
• 15:43
  SPEAKER_02: So we can serve as the knowledge platform, regardless of which CRM or agent system a customer has chosen.
• 15:51
  SPEAKER_02: Enterprises don't want to be locked into a single vendor's knowledge silo, and that's been a consistent advantage in competitive conversations.
• 15:59
  SPEAKER_02: Therefore, our competitive advantage arises in this context due to our platform delivering a measurable AI ROI as well as architectural flexibility.
• 16:09
  SPEAKER_02: As a reference, the CIO of a$20 billion plus health insurance company customer of ours expressed that his company has two foundational platforms, just two, in its AI architecture.
• 16:22
  SPEAKER_02: One of them being Amazon Data Lake House, and the other one being eGame AI Knowledge Hub, with everything else being built on top of these two.
• 16:32
  SPEAKER_02: That's a really strong validation of the value that our platform brings to AI first businesses.
• 16:38
  SPEAKER_00: That's a great clarification of the dynamic there.
• 16:41
  SPEAKER_00: Thank you.
• 16:41
  SPEAKER_00: So as enterprises mature their AI architectures, what use cases beyond customer service are you seeing knowledge get built into HR, field service, regulatory compliance, um, internal developer use cases, and how is e-gain showing up there?
• 16:58
  SPEAKER_02: Yes, this is a really good question.
• 17:00
  SPEAKER_02: This has primarily been organic pull into this into these new use cases.
• 17:06
  SPEAKER_02: The knowledge problem is structurally similar, whether the audience is a customer, an HR employee, another employee, a field technician, or a compliance officer.
• 17:16
  SPEAKER_02: And enterprises that deploy e-gain for customer-facing knowledge have been asking us to extend it more to the enterprise.
• 17:24
  SPEAKER_02: AHR is a common expansion use case.
• 17:28
  SPEAKER_02: And typical areas would be onboarding, benefits, and policy lookups.
• 17:33
  SPEAKER_02: Field service is another, as technicians need the same curated procedural knowledge that contact centers, the agents and contact centers also do in real time.
• 17:45
  SPEAKER_02: Increasingly, eGain is becoming the system of record for enterprise know-how across every function, not just customer service.
• 17:54
  SPEAKER_00: That sounds great.
• 17:55
  SPEAKER_00: So I'm answering the financial uh question for a second here.
• 17:59
  SPEAKER_00: Um, e-gain is continuing to show strong profitability while investing in AI products to go to market.
• 18:04
  SPEAKER_00: Uh, year today, your operating cash flow was around 19 million at 20% margin.
• 18:08
  SPEAKER_00: And management has talked about disciplined cost management alongside the shift toward a more product-led sales model.
• 18:15
  SPEAKER_00: So, how are you and Eric thinking about balancing reinvestment and growth while maintaining that profitability profile?
• 18:21
  SPEAKER_02: So, this is how we've been thinking about it.
• 18:23
  SPEAKER_02: Both profitability and growth are important metrics for e-game.
• 18:28
  SPEAKER_02: And the Q3 numbers reflect that.
• 18:31
  SPEAKER_02: 22.5 million in revenue, which is up 7% year over year.
• 18:35
  SPEAKER_02: AI knowledge ARR year over year growth of 26%, and year-to-day our nine-month operating cash flow of 18.7 million, reflecting a 27% margin.
• 18:47
  SPEAKER_02: We've also embraced AI tools internally across RD and operations, which has increased our velocity of innovation.
• 18:55
  SPEAKER_02: We've launched more products this fiscal year than in any period in recent history.
• 19:00
  SPEAKER_02: We also ended the quarter with over 80 million in cash, or 80 and a half million in cash and no debt.
• 19:06
  SPEAKER_02: So we are self-funding our product and go-to-market investments without really needing to access the capital markets.
• 19:14
  SPEAKER_02: And on capital allocation, beyond organic reinvestment, we continuously evaluate share repurchases and MA when we believe the opportunity is compelling.
• 19:30
  SPEAKER_00: So good to see the results that you're driving with that.
• 19:33
  SPEAKER_00: Um, just one more to wrap up here.
• 19:35
  SPEAKER_00: So Calderman's, you know, to wrap up, Egan has talked about being well positioned as uh AI knowledge moves from an early adopter phase to an early majority phase of the adoption curve.
• 19:45
  SPEAKER_00: So as you look out over the next year, like what are the priorities that you and the team are most focused on?
• 19:51
  SPEAKER_02: Yeah, so the numbers speak to the transition underway.
• 19:54
  SPEAKER_02: AN Knowledge Hub ARR grew 26% year over year to$48 million in Q3, and now contributes 64% of total SaaS ARR, which is up from 54% the year prior.
• 20:07
  SPEAKER_02: We're confident in continued strong growth for that segment, which will increasingly drive overall company performance.
• 20:14
  SPEAKER_02: We plan to release full-year guidance for fiscal year 2027 in our next earnings call, which will provide more color on management's growth and profitability expectations.
• 20:25
  SPEAKER_02: We also continuously evaluate additional metrics to possibly disclose in our future results and guidance, which would provide more insight and transparency into our core knowledge business and other segments.
• 20:36
  SPEAKER_02: Regarding near-term priorities, it's mostly business as usual for now, with the team focused on lead gen, converting our pipeline into bookings, driving renewals and expansion within the existing base, and disciplined capital management to fuel growth and shareholder value.
• 20:53
  SPEAKER_02: Finally, for the benefit of your listeners who are interested in learning more about e-Gain, I want to mention that we have a refreshed investor presentation on our IR site.
• 21:03
  SPEAKER_02: In addition, our website now posts all the recorded sessions from our Solve London event.
• 21:08
  SPEAKER_02: This includes Ashu's keynote, all of the customer presentations, and sessions of our new product launches.
• 21:16
  SPEAKER_00: Great.
• 21:16
  SPEAKER_00: Those are some solid results that you posted and looking forward to to seeing uh where you guys deliver in the future.
• 21:22
  SPEAKER_00: So I'll pass it back to Tim to wrap things up.
• 21:26
  SPEAKER_01: Thanks, James, and uh thanks, Scottam, for uh joining us today to talk about e-gain.
• 21:29
  SPEAKER_01: Uh, I would mirror what uh James said, some very impressive gains, and uh you're in a super hot space, so enjoy the ride, as they say.
• 21:37
  SPEAKER_01: Thank you so much.
• 21:39
  SPEAKER_01: Thank you for listening, and don't forget to subscribe, as well as visiting www.watertoweresearch.com to stay up to speed on the company's small cap written research reports, podcast, fireside chats, industry specific symposiums, and conference schedules.
• 21:55
  SPEAKER_01: We will see you next time for another edition of the WTR Small Cap Spotlight Podcast.
• 22:00
  SPEAKER_01: Finally, a special thanks to the producer and editor of the podcast, Christoph Fitzpatrick.