eGain Q4 and Full Year Fiscal 2026 Earnings Call Transcript

eGain

• Ashutosh (Ashu) Roy, Chief Executive Officer
• Eric Smit, Chief Financial Officer
• Jim Byers, Investor Relations

Analysts

• Vijay Homan, Craig-Hallum (on behalf of Jeff Van Rhee)
• Ethan Widell, B. Riley Securities (on behalf of Erik Suppiger)

Gartner names eGain a Leader in the first Magic Quadrant for Customer Service Knowledge Management Systems

Ashutosh Roy — Chief Executive Officer: Thank you, Jim. Good afternoon, everyone. Right at the end of fiscal 2026, the category we’ve been building toward for years got a name. In July this year, Gartner published its first-ever Magic Quadrant for Customer Service Knowledge Management Systems and named eGain a Leader, positioned highest for Ability to Execute and furthest for Completeness of Vision.

This inaugural Magic Quadrant matters more than just our position in it. This is the first time a top analyst firm has drawn a sharp boundary around this market and explicitly called out knowledge management for customer service as its own category of enterprise infrastructure. They base it on the volume and kind of client inquiries they get in this area, and therefore they have chosen to invest Magic Quadrant–level resources and attention to it. It’s a very important signal for the market and the category that’s building around it.

Why knowledge is now instruction for AI: “Wrong knowledge equals wrong AI”

As we have said, there’s good reason this buying category is emerging now. Generative AI has collapsed the old separation between instruction and data. What an AI agent or agentic workflow does in any live customer or employee assistance conversation is determined entirely by the policies, procedures and know-how it is fed. When that knowledge is wrong, the AI is confidently wrong. When it’s stale, the AI doesn’t know it’s out of date. So knowledge is no longer documentation just for humans to optionally use. It is instruction for AI.

Wrong knowledge equals wrong AI. Engineering that instruction layer, governing it and operating it continuously is what we call AI Knowledge Ops, a term that Gartner reflected in their Magic Quadrant report as something unique and important that eGain brings to this solution. It is the discipline enterprises are now realizing they cannot skip if they want AI to work reliably in production, not just in pilot.

Fiscal 2026 results: total revenue $91.1 million, AI customer revenue up 20%

With this market trend and the analyst acknowledgment, let me walk through how fiscal 2026 came together. Before I do that, let me define a term that we will use moving forward, and that is AI customer. An AI customer is an eGain customer who utilizes one or more of our AI offerings.

So with that said, let’s look at full-year fiscal 2026. Our total revenue grew 3% to $91.1 million. Our AI customer revenue grew 20% year over year. AI customer ARR grew 13% and represented 72% of total SaaS ARR at year-end, up from 63% at the [end of fiscal 2025]. This is an intentional shift in the shape of our customer base. A growing majority of our SaaS ARR now sits with customers who are using one or more of our AI capabilities.

New customer wins and paid pilots

Turning to new business, our momentum continued to build. In the fourth quarter, we won several new logos. A couple of examples here.

First, a leading European insurance company. They set out to automate their service operation with AI and recognized that they needed to put in place a governed knowledge foundation before they could deploy AI automation at scale. So they selected eGain to modernize their knowledge environment and establish that foundation.

Second, a global multi-energy operator serving millions of customers. They faced a familiar barrier to scaling service: fragmented knowledge leading to inconsistent service quality. They are deploying our knowledge platform and AI agent in one contact center. Based on the successful blueprint from that deployment, they will extend to the rest of their contact centers. They also plan to activate self-service channels and leverage the Knowledge Hub across the entire business.

In addition, we added several new paid pilots this quarter. Increasingly, we see buyers wanting to extensively validate our platform in their own environment before committing to a full rollout, and they’re willing to pay for it. This is a shift from where we used to be, when we were doing a lot of free quick trials and pilots as part of our 30-day, no-risk pilot program. Converting these paid pilots into at-scale production rollouts is a focus for us this fiscal year. A few examples:

One of the world’s largest pharmaceutical companies. As experienced scientists and specialists in their R&D teams retire or change roles, the company risks losing a lot of deep tacit expertise. They’re using our AI Knowledge Hub to capture that tacit knowledge on a continuous basis and turn it into valuable knowledge for their AI engine.

Second, a global leader in testing, inspection and certification. They were facing a hard regulatory deadline, and they needed accurate, instant guidance in a compliance-heavy environment. Early pilot results indicate that the AI agents deliver 95% self-service resolution, with 80% user satisfaction in surveys.

Third, a global leader in gaming technology. They operate in a complex environment where every answer has to be guided and correct.

Two market trends: knowledge as AI infrastructure and growth in self-service

Stepping back to the market, I want to share two trends that we see emerging in the last couple of quarters. First, businesses are treating knowledge as core AI infrastructure, and their tech and AI teams are actively building on top of this infrastructure. That drives demand for richer platform capabilities like real-time knowledge APIs and stringent service levels, so our growing developer-facing capabilities on our Composer platform are being well received.

The second trend is growing interest in customer self-service projects. Several new logos in recent quarters have started out with self-service deployments, something we did not see a year ago, when it was more common to start with contact center–based use cases. While contact center productivity is still of great interest, we sense that businesses are increasingly driving for ROI at scale on their AI investments.

Business momentum: new logo wins up 27%, large-deal pipeline doubled

Moving to business momentum in fiscal 2026: our new logo wins increased 27% year over year. As I mentioned earlier, several of the new logos we acquired in fiscal 2026 have paid pilots in Global 2000 accounts, and they have significant upside, something we intend to pursue this fiscal year. Our pipeline opportunities valued at $500,000 ARR or more doubled in count year over year. And in our core verticals, which are compliance-heavy, like banking, financial services, insurance and health care, we grew our opportunities in the pipeline by 40% year over year, exactly where a trusted knowledge foundation matters the most.

New products: eGain IVA, Agentic Studio, Evaluator and AI Knowledge Suite for Health Care

Turning to products, our innovation continues to accelerate with focus. Everything we launched last quarter, in Q4, at our eGain Solve event in London in May, fueled the cycle of knowledge and AI. We’re increasingly deploying AI in our platform to dramatically automate knowledge management. Generating and maintaining trusted knowledge with low effort then drives better instruction to the AI that is being used to reliably automate customer service and customer operations.

A few noteworthy announcements of new capabilities we made in May:

• eGain IVA, an intelligent voice agent. What’s unique about it is that it uses the same trusted knowledge platform we use for all our digital self-service tools. That consistency and quality is something we can now offer as a complete omnichannel self-service offering.
• eGain Agentic Studio, a zero-code application-building environment that lets business users assemble service use cases end to end: multistep, complex processes with every step grounded in verified knowledge, using assured tools and actions, and invoking human oversight when needed. It’s a complete platform for service automation using agentic capabilities.
• eGain Evaluator, our continuous evaluation tool for AI pipelines, which we announced previously and have now made generally available. It’s getting a lot of interest from large customers who want continuous quality assurance of their agentic pipelines.
• eGain AI Knowledge Suite for Health Care, a governed knowledge foundation purpose-built for health plans and health systems.

We will build on this momentum at our upcoming Solve event in Chicago on October 13 and 14 this year. We’ll lay out our view of the year ahead, the shift from knowledge management to knowledge automation, and the value of agentic AI assembly on top of trusted knowledge. And of course, we’ll announce new capabilities and hear from our customers and partners.

In conclusion, our sustained bet on AI knowledge, the market and products in fiscal 2026 is showing results. So we are doubling down, and we intend to lead this market. With that, I’ll turn it over to Eric Smit, our CFO, to take you through the financial details. Eric?

Fourth quarter fiscal 2026 results: revenue of $22.2 million, above guidance

Starting with the fourth quarter results and starting with revenue. Total revenue was $22.2 million, exceeding both our guidance and Street consensus, compared with $23.2 million in the prior-year quarter. The year-over-year decline in total revenue primarily reflected lower revenue from our legacy conversation and analytics customers. AI customer revenue grew 11% year over year in the fourth quarter.

Looking at gross margins, non-GAAP total gross margin for the quarter was 72%, compared to 73% a year ago. Non-GAAP SaaS gross margin was 78%, compared to 80% a year ago.

Turning to operating expenses, non-GAAP operating costs were $14.1 million, up 6% year over year and 2% sequentially. Sales and marketing expenses were $5.5 million, up 21% sequentially, reflecting our planned investments in go-to-market initiatives, including the eGain Solve event we held in London.

Looking at our bottom line, GAAP net income was $1.3 million, or $0.05 per basic and diluted share, compared with GAAP net income of $30.9 million, or $1.13 per basic share and $1.11 per diluted share, in the prior-year quarter. The prior-year results included an approximately $29 million tax benefit from the release of the majority of our valuation allowance.

Non-GAAP net income was $2.1 million, or $0.08 per basic and diluted share, exceeding our guidance and Street consensus. This compares with $2.4 million, or $0.09 per basic and diluted share, in the year-ago quarter. Adjusted EBITDA was $2.2 million, representing a 10% margin and exceeding our expectations, compared to $4.5 million and a 19% margin a year ago. During the quarter, we repurchased 1.4 million shares for $10.1 million at an average price of $7.32 per share.

Full-year fiscal 2026 results: record $21.2 million operating cash flow, $73.3 million cash

Turning to our full-year results. Total revenue was $91.1 million, exceeding our guidance and up 3% year over year. Within total revenue, AI customer revenue grew 20% year over year. AI customer ARR grew 13% year over year and represented 72% of total SaaS ARR at year-end.

Looking at gross margins and operating expenses, non-GAAP total gross margin was 74%, up from 71% in fiscal 2025. Non-GAAP operating costs were $55.3 million, compared to $56 million in the prior year.

Turning to the bottom line, balance sheet and cash flows. GAAP net income was $8.9 million, or $0.33 per basic share and $0.32 per diluted share, compared with $32.3 million, or $1.15 per basic share and $1.13 per diluted share, in fiscal 2025. As I mentioned, the prior-year results included an approximately $29 million tax benefit.

Non-GAAP net income was $13 million, or $0.48 per basic share and $0.47 per diluted share, up from non-GAAP net income of $5.7 million, or $0.20 per basic and diluted share, in the prior fiscal year. Adjusted EBITDA increased to $13.6 million, representing a 15% margin, up from $8.6 million and a 10% margin in fiscal 2025.

Cash flow from operations reached a record $21.2 million, representing a 23% operating cash flow margin, up from $5.3 million, or a 6% operating cash flow margin, in fiscal 2025. Cash and cash equivalents totaled $73.3 million at June 30, 2026, compared to $62.9 million at June 30, 2025.

During fiscal 2026, we repurchased 1.6 million shares for $11.5 million at an average price of $7.16 per share. At year-end, we had $9.7 million remaining available under the $60 million buyback authorization.

New reporting framework: AI customer ARR and AI customer revenue

Now turning to our AI customer metrics. As Ashu mentioned, instead of reporting by product hub, going forward we’re reporting based on whether a customer is actively using one or more of our AI offerings. We call this AI customer ARR and AI customer revenue. We believe it’s a cleaner, more forward-looking way to show AI adoption spreading across our installed base, since many customers now use AI capabilities across multiple parts of our platform rather than within a single hub. This is the framework we’ll use going forward.

The strategic rationale is straightforward. We have found that a customer’s overall adoption of our AI capabilities, not the specific product SKU or hub they originally purchased, is the strongest predictor of long-term retention and expansion. To better measure and ultimately maximize that dynamic, we completed a full review of our customer base this year and segmented it into two groups: AI customers, meaning those actively engaged with our AI platform, and all other customers.

This is a meaningful shift in how we think about the business. Our reporting focus is now on growing ARR per account, which we view as a primary measure of success; the specific mix of products a given customer consumes becomes secondary. We believe this customer-based view better reflects how customers deploy our integrated platform, how we manage these relationships, and the broader retention and expansion opportunity within our AI customer base. It is now our primary lens for measuring the health of our AI business.

AI customer ARR is defined as total SaaS ARR from customers who are actively utilizing one or more of our AI offerings. This amount includes all offerings associated with the customer, not solely the AI offerings.

AI customer revenue is defined as the total revenue generated from customers who actively utilize one or more of our AI offerings, inclusive of their SaaS and professional services revenue. This amount also includes all offerings associated with the customer, not solely the AI offerings.

AI customer ARR, net retention and remaining performance obligation

With that context, here are the metrics. AI customer ARR increased 13% year over year and represented 72% of total SaaS ARR at year-end. Total SaaS ARR declined 1% year over year, driven by the decline among our legacy non-AI customers.

Turning to our retention rates, trailing 12-month dollar-based net retention for AI customers was 104%, compared to 120% a year ago. As a reminder, we closed a significant expansion deal with JPMC in Q4 of last fiscal year, which drove that increase in net retention. Net retention for all customers was 93%, compared to 105% a year ago. Total remaining performance obligation, or RPO, of $87 million was down 5% year over year, and short-term RPO of $62 million was down 2% year over year.

First quarter fiscal 2027 guidance: revenue $20.9 million to $21.4 million

Eric Smit — Chief Financial Officer: Now turning to our outlook, starting with guidance for the first quarter of fiscal 2027. We expect AI customer revenue of between $13.7 million and $14 million and total revenue of between $20.9 million and $21.4 million.

Turning to the bottom line, for Q1 we expect GAAP net income of $500,000 to $1 million, or $0.02 to $0.04 per share, which includes stock-based compensation expense of approximately $900,000. We expect non-GAAP net income of $1.4 million to $2 million, or $0.05 to $0.08 per share, and adjusted EBITDA of $1.4 million to $1.9 million, or a margin of 7% to 9%.

Fiscal 2027 guidance: AI customer revenue up 8% to 10% while legacy revenue runs off

For the fiscal year ending June 30, 2027, we expect AI customer revenue of between $59.5 million and $60.5 million, representing growth of approximately 8% to 10%, and total revenue of between $84.5 million and $86 million.

Our outlook reflects two different trends within the business. We expect continued growth from AI customers alongside an estimated 40% [30%] decline in revenue from our profitable legacy customers. We are using the cash generation from this non-core business to fund investments in the larger AI opportunity. We expect ARR from AI customers to grow approximately 20% in fiscal 2027, while ARR from legacy customers is expected to decline by 60%.

On the bottom line, we expect a GAAP net loss of $2 million to $3 million, or $0.08 to $0.11 per share. This includes stock-based compensation expense of approximately $4 million. We expect non-GAAP net income of $1 million to $2 million, or $0.04 to $0.07 per share, and adjusted EBITDA of $650,000 to $1.4 million, or a margin of 1% to 2%. We expect weighted average shares outstanding of approximately 26.6 million for the first quarter and 26.8 million for full fiscal 2027.

Fiscal 2030 long-term financial model: AI customer ARR of $100 million to $120 million

Today we are also introducing a long-term financial model that lays out our targets through fiscal 2030. As we complete our transition to a higher-growth, AI-led business, we see fiscal 2027 through fiscal 2029 as a transition period, with total revenue growth increasingly converging with AI customer revenue growth. Fiscal 2030 is the year that convergence is largely complete.

For fiscal 2030 relative to fiscal 2026, we are targeting:

• AI customer ARR of $100 million to $120 million, up from $54 million in fiscal 2026, a 17% to 22% CAGR as AI ARR compounds toward scale.
• Total SaaS ARR of $100 million to $120 million, up from $75 million in fiscal 2026, reflecting substantially complete runoff of non-AI ARR and migration to AI.
• AI customer ARR at approximately 100% of total SaaS ARR, up from 72% in fiscal 2026, effectively a pure-play AI ARR base.
• AI customer revenue of $105 million to $115 million, a 17% to 20% CAGR from the $55 million generated in fiscal 2026, with growth of more than 20% year over year by fiscal 2030, making underlying AI revenue growth increasingly visible in total results.
• Total revenue of $110 million to $120 million, representing approximately 15% to 20% growth year over year by fiscal 2030, with total company growth closely mirroring AI growth.
• AI customer revenue at approximately 95% of total revenue, up from 60% in fiscal 2026, supporting a higher-quality valuation framework.
• SaaS gross margins of approximately 80%, maintaining an attractive software margin profile.
• Adjusted EBITDA margin that remains positive while we fund AI growth, a deliberate balance between growth investment and profitability discipline.

We believe our leadership in AI-powered knowledge management, expanding market opportunity and increased go-to-market investment position eGain to pursue durable growth while maintaining an attractive profitability profile.

CFO summary and Investor Day

So to summarize: AI customer revenue and ARR both grew at double-digit rates in fiscal 2026, and we completed the shift to customer-level reporting that we believe gives investors a clearer view of the business and strengthens our positioning following Gartner’s naming of eGain as a Leader in the inaugural Magic Quadrant for Customer Service Knowledge Management Systems. We also delivered total revenue growth, strong profitability and record operating cash flow in fiscal 2026. With our strong balance sheet and cash generation, including the cash we generate from our declining but profitable legacy offerings, we are all in on the AI knowledge opportunity, investing to build on that position and pursue sustainable long-term growth.

Lastly, as Ashu mentioned, we will be hosting an Investor Day and Analyst Day in conjunction with our upcoming eGain Solve customer event on October 13 in Chicago. Additional information and registration details are available on our website. This event is a great opportunity for prospective investors and analysts to meet with customers and learn more about our business. We hope you can join us. With that, I would like to open the call for questions. Operator?

Craig-Hallum: What is the timeline for running off non-AI ARR?

Vijay Homan — Craig-Hallum: This is Vijay on for Jeff. First one for me: in the target model and in the prepared remarks, you talked a little bit about running off the non-AI ARR. Is there a timeline for that in mind, similar to what you had with the messaging business? And how does the profitability of those businesses compare to the rest of the business?

Eric Smit — Chief Financial Officer: Thanks for that. For clarification, as we described in the model, the goal, obviously, is to convert some of the non-AI business into the AI business. But from a modeling standpoint, we’d expect it to go to zero as we get to the 2030 time frame.

Craig-Hallum: Is AI creating pricing pressure on eGain’s SaaS products?

Vijay Homan — Craig-Hallum: Got it. On previous earnings calls, you talked about some of the potential impacts of AI more generally on the business, such as pricing pressure on SaaS products. Are you seeing that show up in the business at all, or is that still expected later down the line?

Ashutosh Roy — Chief Executive Officer: This is Ashu. I would say that we are seeing some of that pressure, but we are also seeing our ability to create new product offerings, which layer on additional revenue from these value-added AI capabilities. So all in all, the effect has not been as significant as I would have feared. We are prepared for it. My sense is one or two points of pressure over the next two to three years. Eric, do you have anything to add?

Eric Smit — Chief Financial Officer: Exactly. I think that’s aligned at this stage. Given the instruction layer this is building, it’s creating opportunities that are different from what we would have seen historically as well, which will obviously affect the way we approach pricing.

Craig-Hallum: What will drive growth in the long-term model?

Vijay Homan — Craig-Hallum: Got it. For the target model, I appreciate having that out there. As you look at the growth profile, if you expect 15% or 20% growth, how much of that will be price, new customer adds, adding seats to existing customers or reducing churn? What do you think the biggest drivers will be?

Eric Smit — Chief Financial Officer: Most of the driver will come from new logo acquisition. When we look at the opportunity in front of us, especially with the backdrop we’re now seeing with the Gartner Magic Quadrant, this investment to drive brand awareness and scale up the customer base will be the primary driver. Obviously, we will work hard to move customers that are in the legacy bucket, but that will not be the primary driver of this growth.

Operator: (Operator instructions) Our next question comes from Erik Suppiger with B. Riley.

B. Riley: Does a multi-model AI ecosystem increase demand for knowledge management?

Ethan Widell — B. Riley Securities: This is Ethan Widell calling in for Erik. Just one question from me. As companies adopt an ecosystem of AI models rather than just using one of the frontier models, does that dynamic create more demand for a knowledge management solution? Can you speak to that dynamic a little more?

Ashutosh Roy — Chief Executive Officer: Yes, I’ll take that. You’re right. In the last month or so, as I’m sure you’ve seen as well, there’s been a lot of talk about people running into runaway token costs and the overall cost of AI as adoption has been pushed hard in enterprises. What we see with our approach is that just by being sharper in what you feed into these AI tools, you can keep costs down significantly, sometimes by a factor of 10. So there’s a big advantage in being more precise in how you instruct and guide, rather than throwing the kitchen sink of content and context into these models. That’s one very interesting advantage we bring to the party.

The second is that, internally inside the platform, we tend to be smart about using horses for courses: the right models for the right need. We see that as another way of managing AI token cost for our clients.

B. Riley: Does the choice between frontier and open-source models matter?

Ethan Widell — B. Riley Securities: One little follow-up. Does that dynamic matter at all for the big frontier models versus open source?

Ashutosh Roy — Chief Executive Officer: It does matter to some extent. The quality advantage in terms of benchmarks is probably not more than 10% to 15% for most of the relevant benchmarks we are looking at, and the cost difference can be more than a factor of 10. So yes, it does matter. As businesses do more real-time, continuous operation to ensure their knowledge and know-how is always up to date, that’s going to drive up token usage, which will then require smarter routing to the relevant capable models.

Closing remarks

Operator: At this time, there are no further questions. I would like to turn the conference back over to eGain management for any closing remarks.

Eric Smit — Chief Financial Officer: Thanks, operator, and thanks, everyone, for joining the call today. Again, I encourage all of you to consider joining us at the event in Chicago; details are on the website. Thank you.

Operator: The conference has now concluded. Thank you for attending today’s presentation. You may now disconnect.