eGain (EGAN) Q1 2027 Earnings Call Transcript

DATE

Thursday, Nov. 5, 2026

CALL PARTICIPANTS

• Investor Relations - Jim Byers
• Chief Executive Officer - Ashutosh Roy
• Chief Financial Officer - Eric N. Smit

TAKEAWAYS

• Total Revenue -- $21.9 million, representing a 7% decrease driven by the planned runoff of legacy business.
• AI Customer Revenue -- $14.4 million, increasing 7% and accounting for 66% of total revenue.
• AI Customer ARR -- $54.5 million, growing 11% and reaching 75% of total SaaS ARR compared to 72% at the end of fiscal 2026.
• Total SaaS ARR -- $73.4 million, down 4% due to continued declines among legacy customers.
• Non-GAAP Total Gross Margin -- 74%, down from 76% reflecting lower legacy SaaS revenue against a largely fixed hosting cost base.
• Non-GAAP SaaS Gross Margin -- 78%, a decrease from 81% in the prior year.
• Non-GAAP Operating Expenses -- $13.6 million, up 4% and including a 17% increase in sales and marketing expenses.
• Non-GAAP Net Income -- $2.4 million, or $0.08 per share, exceeding management's guidance.
• Adjusted EBITDA -- $2.7 million, yielding a 12% margin compared to 21% in the previous year.
• Cash and Cash Equivalents -- $71.3 million, with no debt on the balance sheet at quarter end.
• Share Repurchases -- $3.6 million, covering approximately 644,000 shares at an average price of $5.64 per share.
• Net Retention -- 104% for AI customers, compared to 117% a year ago which included a significant expansion with a U.S. mega bank.
• Total Remaining Performance Obligations -- $71 million, declining 18% due to the timing of multiyear renewals and legacy business runoff.
• Q2 2027 Revenue Guidance -- $21 million to $21.5 million, with AI customer revenue expected between $14.5 million to $14.8 million.
• Full-Year Revenue Guidance -- $84.5 million to $86 million, with AI customer revenue growth projected at 8% to 10%.
• Full-Year Adjusted EBITDA Guidance -- $650,000 to $1.4 million, reflecting a margin of 1% to 2%.
• Fiscal 2030 AI Customer ARR Target -- $100 million to $120 million, representing a 17% to 20% compound annual growth rate.
• Operational Cash Flow -- $1.8 million, down from $10.4 million due to the timing of an annual payment from a federal customer.

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RISKS

• Smit warned that "the runoff of legacy revenue to become more pronounced beginning the second quarter," specifically noting the planned wind-down of the legacy messaging business.
• Smit stated, "The decline reflects the lower legacy SaaS revenue against a largely fixed hosting cost base," which contributed to the year-over-year compression in SaaS gross margins.

SUMMARY

eGain Corporation (NASDAQ:EGAN) reported first-quarter financial results that exceeded its prior guidance for revenue and net income despite a 7% decline in total top-line performance. Management stated that the decrease is part of a planned transition away from legacy business, which declined 28% during the period. The company reported that its AI-enabled platform now accounts for 75% of total SaaS ARR, and management reaffirmed its long-term objective to have AI customers represent substantially all recurring revenue by fiscal 2030. Management indicated that it is trading near-term profitability for growth by increasing sales and marketing investments to capitalize on its recent recognition as a leader in customer service knowledge management systems.

• CEO Roy stated that "AI success does not hinge on the model," noting instead that results are dependent on the underlying knowledge foundation provided by the platform.
• Management introduced AI KnowledgeOps, a new framework including tools like WiCA and Smart Evaluator to automate knowledge maintenance and test AI pipelines for answer quality.
• The company reported that event-sourced leads nearly doubled year over year, with growing inbound RFP volume from large enterprises following its inclusion in the Gartner Magic Quadrant.
• Operational case studies provided on the call included a New York health insurer that reduced misrouted contacts by 76% within 30 days of deploying the platform.
• Management noted a new logo win with a consumer packaged goods company where the AI agent will assist 1,000 plant floor operators as a virtual expert.
• The company is targeting specific vertical markets, including dedicated go-to-market teams for healthcare, government, and the banking and credit union sectors.

INDUSTRY GLOSSARY

• ARR: Annual Recurring Revenue, a metric that normalizes the value of recurring subscription contracts over a one-year period.
• AI KnowledgeOps: A framework and set of capabilities designed to run enterprise knowledge as a continuous operation to feed and govern AI systems.
• K Forge: A tool that converts content from disparate silos like SharePoint or ServiceNow into AI-ready knowledge.
• RPO: Remaining Performance Obligations, representing the total value of contracted revenue that has not yet been recognized.
• SaaS: Software as a Service, a software licensing and delivery model in which software is licensed on a subscription basis.
• Smart Evaluator: A product that enables engineers to continuously test AI pipelines for answer quality and identify specific failure points.
• WiCA: What Your Customers Ask, an analytical tool that identifies critical customer questions within a company's firewall for compliance.

Full Conference Call Transcript

Operator: Thank you for standing by, and welcome to eGames' 27 First Quarter Financial Results Conference Call. All lines have been placed on mute to prevent any background noise. After the speakers' remarks, there will be a question-and-answer session. If you would like to ask a question during this time, simply press *1 on your telephone keypad. If you would like to withdraw your question, please press *1 again. Thank you. I would now like to turn the conference over to Jim Byers. PondelWilkinson, Investor Relations. Please go ahead.

Jim Byers: Thank you, operator, and good afternoon, everyone. Welcome to eGain's 27 First Quarter Financial Results Conference Call. On the call today are eGain's Chief Executive Officer, Ashutosh Roy and Chief Financial Officer, Eric N. Smit. Before we begin, I would like to remind everyone that during this conference call, management will make certain forward-looking statements which convey management's expectations, beliefs, plans and objectives regarding future financial and operational performance. Forward-looking statements are generally preceded by words such as believe, plan, intend, expect, anticipate or similar expressions. Forward-looking statements are protected by Safe Harbor provisions contained in the Private Securities Litigation Reform Act of 2000.

These forward-looking statements are subject to a wide range of risks and uncertainties that could cause actual results to differ in material respects. Information on various factors that could affect eGain's results are detailed in the company's reports filed with the Securities and Exchange Commission. EGain is making these statements as of today, November 5, 2026. And assumes no obligation to publicly update or revise any of the forward-looking information in this conference call.

In addition to GAAP results, we will also discuss certain non-GAAP financial measures such as non GAAP operating income, The tables included with eGain's earnings press release include reconciliation of the historical non-GAAP financial measures to the most directly comparable GAAP financial. eGain's earnings press release can be found by clicking News and Press within the Investor Relations page of eGain's website at egain.com along with the earnings release, we will post an updated investor presentation to the Investor Relations page. And lastly, a phone replay of this conference call will be available for 1 week. And now with that said, I would like to turn the call over to eGain's CEO, Ashutosh Roy.

Ashutosh Roy: Thank you, Jim, and good afternoon, everyone. I will keep my comments on the quarter brief. Eric will take you through the numbers. But I do want to spend some time on our market because we believe the market and the category we have been building toward for a while is now taking shape. So, our first quarter came in as we planned with revenue and profitability ahead of our guidance. Total revenue was $21.9 million above the high end of our guidance. As expected, that was down 7% year-over-year reflecting the planned runoff of our legacy business. AI customer revenue was $14.4 million up 7% year over year and represented 66% of total revenue.

AI customer ARR was $54.5 million, up 11% year-over-year and grew to 75% of total SaaS ARR from 72% at the end of fiscal 26. Now, I want to turn over to the category that I was talking about in July. Gartner published its first ever Magic Quadrant, for Customer Service Knowledge Management Systems. And named eGain a leader. We were positioned highest in ability to execute and furthest in completeness of vision. In the companion critical capabilities report, we received the highest product score for the compliance driven service center use case. I want to underline why this matters beyond our position in it. Gartner does not initiate a magic quadrant on a whim.

They do it when enough enterprises, their clients, are asking about a problem that becomes its own buying category. This is the category eGain has been building for a while. It now has a name, a boundary, and a recognized leader. But let's be candid. For many people, knowledge management, quote unquote, still conjures document libraries, intranets, and maybe SharePoint on steroids. That is not what this category is about anymore. And it is not what eGain does. So let me explain what we do. Because it is key to the opportunity. Let me start by summarizing a little bit of what we discussed at our Solve event in Chicago last month.

Which is our annual customer event in the U.S. I talked about what I call the expert paradox. Simply put, AI works well for experts and struggles otherwise. Let me explain. As you and we all know, AI is successfully used now in business for coding and personal productivity. In both cases, the user controls what goes into the AI system, and the same user can judge the AI output quality. A developer, for example, can tell whether the AI generated code works. Using tools and their expertise Customer service use cases are at the opposite end of that in terms of user expertise.

So the person being given the AI answer whether that is a service associate, a sales rep, or the end customer, is in no position to tell whether the answer is correct or compliant. And in the off chance that they do, they cannot control the input to AI anyway. This paradox largely explains the yawning gap in AI adoption for customer service adoption. Sorry, automation there. So how do we make AI work at scale for such use cases? We do 3 things together. We ensure that AI is fed trusted knowledge, We make sure it has effective guardrails. And we continuously check for output quality.

The policies, procedures, know how of business feeds its AI systems are, in effect, the instruction that AI follows. Wrong knowledge means wrong AI results. That is what eGain provides. Trusted knowledge and guidance, for enterprise AI. Whether a business runs our AI agents Salesforce's agent force, Microsoft Copilot or whatever they built themselves. We make sure every answer and every action performed by AI is grounded in knowledge the business can stand behind. Kept current as products, policies, and regulations change. And continuously check for quality. that is eGain. IDC presented new research. Sponsored by eGain. Based on a survey of 25 U. S. Organizations, with 1 thousand or more employees. 3 findings stood out.

95% of organizations surveyed are already evaluating piloting or running AI in their knowledge processes. Trying AI is universal. Getting it right is not. The leaders report roughly 2x the improvement of laggards on every business impact IDC measured. This is leaders and laggards as measured by their knowledge maturity. And the returns are measured in terms of AI ROI. What separates them is the foundation. Half of the leaders run a fully operational unified knowledge platform. Versus 5% of laggards and 94% of leaders have automated more than half their knowledge processes. Versus 36% of laggards. IDC's conclusion is the 1 we have been making for some time. AI success does not hinge on the model.

It hinges on the knowledge foundation beneath it. The most powerful part of solve for us was hearing our customers describe their AI ROI success. In their own words. I will mention 3: Country Financial, a Fortune 1 thousand insurer, has given more than 2.5 thousand field agents trusted answers using the eGain platform, instead of a list of documents to consult when they are sitting across a client or on the phone with them. Answer accuracy, they have seen is above 96%. Calls into their home office escalation from their field agents has fallen by 25% in the first month and adoption has reached 70% with no mandates or incentives.

There are 2 lessons for the audience was first, content beats model and second, AI is not a project. A product. You keep operating. Health first, the second 1 I want to bring up, a New York based health insurer, talked about publishing 1 government update to 6.5 thousand users in their business across 50 personalized portals. They feed that knowledge into call routing now which cuts misrouted contacts by 76%. And that was a result we saw within 30 days. Knowledge is not only answering questions, it is driving decisions. And the last 1 I want to bring out is Philip Morris International, They are now running 64 markets across multiple languages, on 1 governed knowledge base. on eGain.

Our client put it best. Knowledge has left the customer care group, he said. It is now used across IT, marketing, and field operations. This is a pattern we see again and again. We land in customer service where the bar for accuracy and compliance is high, Once the foundation is working, becomes the knowledge layer for the rest of the enterprise. Common thread in all these stories is that trusted AI is not a 1-time project. Product launch, rates change, regulations update, and the knowledge behind the AI starts getting stale pretty much the day you lock it. So at Solve, We introduced AI knowledge ops.

A new framework along with a set of capabilities for running knowledge as a continuous operation. It runs as a loop, start by learning what customers are asking from your customer conversations. Find that answer gaps. You prepare and organize the knowledge so AI can use it precisely. You test the answers and then you feedback what you learn from users into the loop We announced 5 new capabilities as part of this AI knowledge ops. To make that loop fast and low effort. First is a tool called WiCA, which stands for what your customers ask.

It analyzes customer conversations, to show which questions matter most, and runs inside the customer's own firewall This is crucial for our compliance oriented customer base. The second is K Forge, which turns content scattered across silos in SharePoint, Confluence, ServiceNow, Sales what have you. Into AI ready knowledge with experts approving anything that is high risk. The third was knowledge graphs, We added that to our knowledge platform. Gives AI agents a map of how our business works. So they can reach and navigate to the right answer at much lower token cost and much faster.

The fourth was Genie, which lets our users on the eGain knowledge platform ask the platform and the tools in conversational interface to write or update content, run reports or build workflows. And finally, the last 1 and probably the most crucial to close the loop is smart evaluator. A product that allows AI engineers to continuously test AI pipelines for answer quality. It flags the wrong answer, It pinpoints where the where it likely went wrong in the pipeline. And suggests a fix. Here's why this matters commercially for us. For a major financial services company, our team ran the AI KnowledgeOps process 1 time, the first time.

About 10 person weeks That company had been expecting that whole exercise would have taken 1 to 2 years Knowledge management has always been a great goal. But the effort it took to get knowledge ready and maintain it was the biggest barrier in this market. And we believe we have removed most of it. A question which we ask ourselves and is fair is what stopped the large platform vendors from doing it themselves? Well, 3 things. First is the depth of capability, The hard part is not search. It is governance, workflow, permissions, versioning, auditability for regulated businesses. We have solved that for many years, and this is where Gartner scored us highest.

The second is, we have deeply applied AI to our own platform to remove manual effort. To make knowledge projects that typically took years now take weeks. We have automated knowledge management so businesses can operate this loop with ease and speed. And third, we are open. We are modular. Enterprises will use many AI tools from many vendors. We sit underneath all of them. So customers can change AI models and agentic systems. Drive their objectives without lock in. Turning to customers in the quarter, we signed several new customers and expansions during the quarter. Let me walk you through some examples. They will show you where the market is going. The first 1 is Technology Credit Union.

This is a growing multibillion dollar credit union in the valley here. They recognize that their AI plans for members and employees depend on the knowledge underneath. And they are building that foundation across contact center, digital banking, branches and back office. So all employees not just contact center agents. It chose eGain, over vertical specific vendors for our governance agentic capabilities and our connectivity into their core systems. In this case, Jack Henry and Alchemy. Credit unions are now becoming a vertical for us that we are pushing in a dedicated way. And along side this new logo, we have customers like Roe Credit Union who presented at Solve Oregon Community Credit Union, Amseco among others.

Who are using eGain well beyond member service. And we are seeing that interest build in our pipeline. The next new logo I wanna talk about is a privately held multibillion dollar consumer packaged goods company. And their use case is outside customer service. They are deploying our AI agent to up to over 1 thousand plant floor operators And this agent is gonna be a virtual expert to assist them. Interestingly, this opportunity started with a self sign up on our website. This is a motion we have been building for some time. And it is starting to add opportunities to our funnel.

Another exciting new logo that we closed this 1 in early October, so not in Q1, is 1 of the oldest and largest investment management companies in America. They are deploying eGain as their trusted knowledge foundation to power all AI projects and thousands of employees. Our existing customers also continue to expand, with more use cases and more functions. A large U. S. Mutual insurer expanded the user of eGain alongside its investment in Salesforce. Using eGain Composer, our platform capability. And making their governed eGain knowledge available inside their Salesforce AI. That is the role I described earlier. If the customer chooses a different AI application, eGain still provides same consistent trusted knowledge underneath.

Another 1 I want to bring out is an international bank added our real time knowledge access capabilities. So the trusted knowledge they have built to start with for customer service now flows automatically into many more of the bank systems and user groups. The pattern is consistent. Customers first establish the knowledge foundation, and then AI application and ROI follows. Looking at demand, the Gartner recognition is showing up at the top of our funnel now. While it is early, I just want to bring out a few things. The Gartner Magic Quadrant campaign from marketing has quickly become 1 of our most productive digital programs. Bringing in new conversations across North America and Europe.

Our event sourced leads have nearly doubled year over year this quarter. As we have scaled marketing. We are seeing more RFPs from large enterprises, many of them inbound. With signals growing awareness. In addition, we now have focused go to market teams, albeit small ones, targeting banks and credit unions, healthcare, and government. And all this is complemented by our self sign up push and a growing partner network. To conclude my section, a year ago, very few people were talking about knowledge as a foundational requirement for AI. Today, Gartner has defined the customer service knowledge management category and named eGain a leader in it. IDC has quantified the AI payoff of getting the knowledge foundation right.

And our customers are showing what AI ROI can be in customer service in production at scale. Now, our numbers do not yet reflect the size of that opportunity. We understand. But we believe we are in the early innings of a large and durable market We have the products, the proof points, and the balance sheet to lead it and we are now focused on effectively scaling our go to market execution. With that, I will turn it over to Eric.

Eric N. Smit: Hey. Thanks, Ashutosh, and thanks, everyone, for joining us today. As a reminder, we are using slides to support today's call. You can find them along with our updated presentation in the Investor Relations section of our website. As Ashu mentioned, we started the year well delivering first quarter revenue and earnings above the high end of our guidance. I will review our first quarter results, take you through our AI customer metrics and touch upon our capital allocation, and then discuss our outlook for the second quarter and full fiscal year. Turning to our first quarter results. Starting with revenue, Total revenue was $21.9 million down 7% year-over-year but above the high end of our guidance in Street consensus.

AI customer revenue grew 7% year over year to 14.4 million above our guidance and represented 66% of total revenue. Year over year decline in total revenue reflects lower revenue from our legacy business, which declined approximately 28% consistent with the transition we outlined last quarter. Turning to gross margins. Non GAAP total gross margin was 74% compared with 76% a year ago. Non GAAP SaaS gross margin was 78% compared to 81% a year ago, The decline reflects the lower legacy SaaS revenue against a largely fixed hosting cost base. Turning to operating expenses. Non GAAP operating expenses were 13.6 million up 4% year over year and down 4% sequentially.

Sales and marketing expense were 4.6 million up 17% year over year and down 16% sequentially primarily reflecting the timing of program spending. Our largest events of the year Solve in Chicago that took place in October So that spending falls in our second quarter. We expect sales and marketing spending to step up over the rest of the fiscal year as we expand the go to market programs Ashutosh described. With a focus on converting growing market awareness into pipeline, new customers and recurring revenue. Turning to the bottom line, GAAP net income was $1.6 million or $0.05 per share, compared with $2.6 million or $0.08 per share a year ago.

Non GAAP net income was $2.4 million or $0.08 per share, exceeding our guidance and Street consensus compared to $4.7 million or $0.14 per share a year ago. Adjusted EBITDA was $2.7 million, a 12% margin, compared with $5 million or a 21% margin a year ago. Turning to cash flow and the balance sheet. Cash provided by operating activities was 1.8 million compared with $10.4 million a year ago. The difference is primarily due to the timing of an annual payment from a large federal customer, Last year, we received that payment in our first quarter This year, it arrived early in the fourth quarter of fiscal 26. Contributing to the strong cash flow in that quarter.

Ended the quarter with $71.3 million in cash, and cash equivalents and no debt. Moving to our capital allocation. Our capital allocation priorities are unchanged. First, invest in AI growth. Across product development and go-to-market, To convert our category leadership into AI customer ARR. Second, maintain a strong debt free balance sheet with significant liquidity relative to our scale. And third, return capital opportunistically. During the quarter, we repurchased approximately 644 thousand shares for $3.6 million at an average price of $5.64 per share. Since the start of fiscal 26, we have returned $15.2 million to shareholders through the repurchase of approximately 2.3 million shares and we have approximately $6 million remaining under the authorization.

Now turning to our AI customer metrics. As we introduced last year, we report our business based on whether a customer is on our current AI enabled platform rather than by product. AI capabilities, as Ashutosh described, are embedded across our AI enabled platform, including identifying knowledge gaps, structuring content, authoring, verifying answers and powering AI. We therefore measure AI adoption at the customer level rather than by the individual product. Our focus is on growing the number of AI customers and expanding those relationships over time. AI customers AI customer ARR includes all SaaS ARR from customers on our current AI enabled platform and AI customer revenue includes SaaS and professional service revenue generated from those customers.

With that context, AI customer ARR grew 11% year-over-year to 54.5 million as of September 30 and represented 74% of total SaaS ARR up from 65% a year ago. Total SaaS ARR was 73.4 million down 4% year-over-year, reflecting the continued declines among our legacy customers. Turning to retention. Trailing 12 month dollar based net retention for AI customers was 104% consistent with the fourth quarter compared with 117% a year ago. As we noted last quarter, the year ago period included the significant expansion with the U. S. Mega bank that we signed in the fourth quarter of fiscal 25.

Net retention for all customers was 92% compared with 102% a year ago, reflecting the expected runoff of our legacy customers. Total remaining performance obligations were 71 million down 18% year over year and short term RPO was $52 million, down 11%. The decline reflects the timing of large multiyear renewals and the continued runoff of our legacy business. Because RPO can swing significantly up or down with the timing of large multiyear renewals, We focus on AI customer ARR and net retention as a primary measure for our customer base. As AI customers become a larger share of our business, we expect their growth to increasingly drive our consolidated results. Now turning to our outlook.

Before the numbers, let me give you some context on how we are thinking about the rest of the year. First, investments. Out of the first quarter profitability outperformance, reflected the timing of sales and marketing spending. We expect that spending to increase beginning in the second quarter including the Solve event. As we expand the go to market programs, our focus is on converting increased market awareness into qualified pipeline, new customers and recurring revenue. Second, the legacy transition. We expect the runoff of legacy revenue to become more pronounced beginning the second quarter. Including the planned wind down of our legacy messaging business.

Our full year outlook continues to assume an approximately 30% decline in our legacy business revenue. We remain comfortable with our full year outlook Our balance sheet and operating cash generation gives us flexibility to fund our AI growth initiatives Our full year guidance reflects meaningful increase in sales and marketing investments over the remaining 3 quarters. As a result, first quarter profitability is not representative of the quarterly earnings levels we expect for the balance of the year.

So for the second quarter of fiscal 27, we expect total revenue of $21 million to 21.5 million AI customer revenue of $14.5 million to 14.8 million GAAP net loss of $500 thousand to net income of $200 thousand or a loss of $0.02 to an income of $0.01 per share, including approximately $700 thousand of stock based compensation expense non GAAP net income of $200 thousand to $900 thousand or $0.01 to $0.03 per share Adjusted EBITDA of breakeven to $500 thousand or a margin of 0% to 2% For the full year, ending June 30, 2027, we are reiterating our guidance We expect total revenue of $84.5 million to $86 million AI customer revenue of $59.5 million to 60.5 million representing growth of approximately 8% to 10%.

We continue to target approximately 20% year-over-year growth in AI customer ARR as of June 30, 2027. A GAAP net loss of $2 million to $3 million or $0.08 to $0.11 per share, including approximately $4 million of stock based compensation expense. Non GAAP net income of $1 million to $2 million, or $0.04 to $0.07 per share adjusted EBITDA of $650 thousand to $1.4 million, or a margin of 1% to 2%. We expect weighted average shares outstanding of approximately 25.8 million for the second quarter and 26 million for the full fiscal year. Finally, a brief reminder of the long term financial model we introduced last quarter. We see fiscal 27 through fiscal 29 as a transition period.

With total revenue growth converging towards AI customer growth as the legacy business runs off. By fiscal 2030, we expect that transition to be largely complete. Our fiscal 2030 targets are AI customer ARR of $100 million to 120 million up from approximately $54 million in fiscal 26, a 17% to 20% compounded annual growth rate. Total SaaS ARR of $100 million to $120 million with AI customers representing substantially all of it, up from 72% in fiscal 26. AI customer revenue of $105 million to $115 million, up from approximately $55 million in fiscal 26. Total revenue of $110 million to $120 million with AI customer revenue approximately 95% of the total. Up from 60% in fiscal 26.

SaaS gross margins of approximately 80% with positive adjusted EBITDA margins as we invest in growth. The result, we are building toward is a larger, faster growing AI knowledge infrastructure company with a greater share of recurring AI customer revenue and significant expansion opportunity within our installed base. We have shown we can run this business profitability, profitably at our current scale We are choosing to trade some near term profitability for growth because we believe the size of this opportunity warrants it. So to summarize, we delivered first quarter results ahead of our guidance and we are reiterating our full year outlook.

AI needs the right instructions and eGain is the instruction layer for enterprise AI recognized as the leader in the first ever Gartner Magic Quadrant for our markets. AI customers now account for 74% of our SaaS ARR, on a path to substantially all of it by fiscal 2030. And with $71.3 million in cash and no debt, we have the resources to fund the plan and turn category leadership into durable long term growth. With that, I would like to open the call for questions. Operator?

Operator: Thank you. And we will now begin the question-and-answer session. And your first question comes from the line of Jeff Van Rhee with Craig Hallum. Please go ahead.

Daniel: Hi, good evening, Ashutosh and Eric. This is Daniel on for Jeff. Maybe Ashu, if you would want to touch a little bit on the AI product suite, obviously, it is been expanding a lot. there is a whole suite of things that have been rolled out in every Solve event, there is a lot more that gets announced. Maybe whether it is AI Knowledge Ops or AI agents or a lot of products that have been announced. Maybe you could talk to what you expect to be the biggest needle movers in AI portfolio and maybe what products or product developments you would want investors to be focused on?

Ashutosh Roy: Good question. So the way we think of it is the foundation for us and that is the AI knowledge hub. What. That is where it all starts. And then all these tools and products that go around the platform then get added on top But what we have done with the AI knowledge ops as a framework is now to guide more and more people. What we have heard from customers, including at Solve, was, you have so many things to offer. We would love for you to tell us exactly how it all works together and how to go about it from where we are today to where we want to be.

So it all starts with AI knowledge hub. And then gets so if you think of it as layers, it is sort of AI knowledge hub as the core platform. And then on top of it, you have tools and you have these prebuilt AI agents, right? So in the target market we are going after, which is the enterprise, I think AI knowledge hub with a platform is where it all starts.

Daniel: Okay. Okay. that is helpful. And then maybe just on the S and M investments you are planning to make. Just walk through a little bit more of the plan there, what you are thinking to change in terms of is that about certain leaders, is that about more quota carrying reps, or we are targeting a particular geography, just how you are thinking about what those plans entail?

Ashutosh Roy: Right now, we. As I mentioned, So we have scaled up our front end marketing investments to drive more awareness and more footfall, if you will, into our digital and physical presence. Right? So that is where we are most focused on in terms of increasing our spend on sales and marketing. And then the second bit behind that is building out other capacity to engage with those early leads and turn them into opportunities. So building out the SDR team, if you will, to make sure that we have got the right level of expertise and front end engagement for those who we are able to bring in as leads.

So I think those are the top 2 things for us right now. The back end of the funnel, which is more around selling and closing, we think that we have enough capacity for that. So really about filling the top of the funnel.

Daniel: Yeah. Then just 1 modeling question for Eric. In terms of the messaging business, said, the first half of that was achieved in Q2 2026, sort of took half impact to Q2 and then another half of the impact came in Q3. And then the remaining half of the messaging business exit, I know that happened here in Q1. Just in terms of thinking whether we got a full quarter of impact or not, that exited at the beginning of the quarter near the end? How much of a quarter of impact did we get from that messaging exit?

Eric N. Smit: So that was the full quarter it is finished up at the end of the quarter. So that is why we mentioned that we expect to see that decline in Q2. Okay, that is helpful.

Daniel: Thanks, Ashu. Thanks, Eric.

Ashutosh Roy: Mhmm.

Operator: Thank you. And your next question comes from the line of Erik Suppiger with B. Riley Securities. Please go ahead.

Ethan Widell: Hi, there. This is Ethan Widell on for Erik. Thanks for taking my questions. Start off with so AI customer revenue, up 7% versus 11% for ARR. Can you maybe help bridge the gap there between those 2 numbers? And maybe what we should expect going forward?

Eric N. Smit: Yes. So I think, obviously, as you would typically see with the ARR is always the leading indicator as you grow a business, then as those contracts get signed, that would take time for that to convert into the revenue going forward. So I think for us, as we look to the guidance, I think we are targeting continued improvement in the ARR growth rates targeting the end of the year, we would be at 20%, whereas currently our guidance for the revenue is sort of roughly half of that. And that is more just the lag between the ARR booking and then the revenue that gets recognized.

Ethan Widell: Okay. That makes sense. And then so when we look at the kind of a legacy ARR decline, Of those customers rolling off, maybe can you speak to what share of that is customers turning out versus migrating to, like, the AI platform?

Eric N. Smit: And has the Gartner recognition changed kind of what that looks like?

Ashutosh Roy: it is a good question. We are working on it. But right now, not that we have seen a significant shift there.

Ethan Widell: Understood. Well, it seems like a very powerful validation of your technology. So I can. Congrats on that and that is all for me.

Ashutosh Roy: Thank you. Sure. Thanks.

Operator: Star 1 on your telephone keypad. And, again, that would be star 1. To ask a question. And I am showing no further questions at this time. I would like to hand it back to management for closing remarks.

Eric N. Smit: Great. Well, thanks, everyone, for taking the time today. Look forward to continue updating you after our Q2 results. Thanks.

Ashutosh Roy: And I guess 1 final item, I will. Will be at the ROTH investor events later this month. So it would be great if anybody in the New York area that is participating, great to meet up with you. Thank you.

Operator: Thank you. And ladies and gentlemen, this concludes today's conference call. Thank you all for joining. You may now disconnect.

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