Good afternoon, everyone, and thank you for joining us for our Q2 Earnings Call and Investor Forum. I want to start a little differently today. Q2 fundamentally changed the trajectory of Sky. For the last several years, we have been building and improving our technology inside some of the world's most demanding enterprises. We built trust. We built proprietary workplace intelligence, and we built the foundation of an agentic AI platform. Now we're entering the scaling phase, and this is where I want to do things a little different this quarter. But I want to also tell you as investors the three things I want you to take out of today's meeting. Number one, EngineRoom transforms our scale and gives us something strategically critical, mid-market distribution. Secondly, CXAI 2.0 has moved from strategy into production. And third, we now see a much clearer operating model for translating growth into operating leverage and ultimately profitable growth.
To do that, I have a very extensive agenda for today. I'm excited to have our leadership team join us. I'm Khurram Sheikh; I'm the Chairman and CEO of CXAI. With me today I'll have Chris Wiegand, who is our General Manager of North America, talking about our enterprise business and the scale we're getting there. Our newest leader, Adam Laurie, who is the General Manager of Australia and previously the Managing Director of EngineRoom, will be with us as well. And my new partner, Melissa Podruzny, who stepped up to be the interim CFO after the transaction, she was leading the finance function at EngineRoom. So Melissa, welcome. And last but not least, we'll have an industry expert guest, Zoe Chen. Zoe is very well respected in the industry, and we'd love to hear her views on the human experience in AI. So with that, let me show you the agenda for today. We're going to first have the Q2 earnings call.
Melissa and I will take that, and we'll run you through the financials. We'll run you through what were the wins, what happened this quarter, what is our outlook for the quarter and for the future. Then we'll adjourn around 5:30. We'll take an intermission. We won't actually adjourn, but we'll just take a little break. If we end early we'll transition directly into our investor forum. The investor forum will really focus on, number one, from my perspective, the vision and what I think about CXAI 2.0 and the agentic enterprise, what's the market, what's the opportunity, where we're at and where we're going. Then I'm going to hand over to Chris to talk about North America, the customers, the product and the growth and then go into more detail on the product side there. Adam will talk about the Australasian opportunity that he sees, the mid-market scale, what he's been winning and continues to win as we speak this quarter and what his future path is.
And then we'll round up with a fireside chat with Zoe, which I'm hopeful you're going to enjoy her perspective, and we'll close. So it's going to be a packed agenda. I know a lot of you have been sending some questions. We'll take some questions in the Q&A section as well. So I appreciate that. So with that, let's get going with the business. Let's talk about our Q2 earnings. As I said earlier, the three themes: number one, EngineRoom is transformative. Number two, CXAI 2.0 is real, it's available. And third, we're now seeing a clear operating model for translating growth into operating leverage and ultimately profitable growth. At Sky we are building the agentic AI operating layer. Before we go into the business numbers, please make sure you have the disclaimer slide on what the forward-looking statements are and read it, the safe harbor. Please review the safe harbor, non-GAAP disclosure in today's presentation and our SEC filings with the applicable risks, assumptions and reconciliations.
We will be filing the 10-Q tomorrow, and you can read that when you get it. Let me talk about the company we have today. The company we have today is pretty amazing. We have deployed globally in around 200-plus cities with more than 60 customers now, supporting a large installed base of users. We operate inside demanding enterprise environments where security, privacy, reliability and integration are not optional, they are necessary. This matters because our AI strategy starts from something valuable: enterprise trust and real operating context. Context is very important. We aren't beginning by building an AI application and trying to figure out where it fits. We already operate inside the enterprise. We understand people, places, workflows and enterprise systems. CXAI 2.0 is about making that context increasingly intelligent and actionable. We're headquartered in the San Francisco Bay Area.
We have teams in Toronto and Manila, and now we're excited to welcome the Australian team, headquartered in Melbourne, with people across Australia and New Zealand. We're excited to have them on board; this gives us global coverage. We have around 70 team members globally, and they're all working hard to make AI successful in the enterprise market that we're in. Before we get to the numbers, let me give you context of where we've been and where we're going. CXAI 1.0 established the enterprise foundation. It showed us that we have great workplace software that has people and place intelligence. We have Fortune 500 customers. They have high trust, high-complexity deployments. This remains an important part of our business. We made some significant strides in the last two quarters. Chris is going to talk about those customer case studies and things, but it's been amazing there. CXAI 2.0 really expands that opportunity.
We're moving primarily from understanding places, which is really the Flow product, which is where and how people work, to person, which is what we're calling Beat—what an individual and team need to accomplish and what should happen next in your life as a worker. And now we're moving with EngineRoom into business: how companies acquire customers, convert demand and grow. That business context is significantly strengthened by EngineRoom. Any of those experiences run on the same Sky agentic platform. The strategy from here is straightforward: proven enterprise technology, mid-market distribution, prioritized AI and scale recurring revenue. We're going to run that flywheel because we've got an agentic platform that we can leverage across multiple verticals. More importantly, we now have a new distribution mechanism through EngineRoom. This is the transformation I'm talking about. This is what we're executing on, and we're super excited about this opportunity.
Now let me go into the business for this quarter and what happened. This is a pretty exciting time for CXAI or Sky. As you can see on our highlights for the quarter, the six main highlights—the biggest one is the EngineRoom transaction. It is transformative. I'll talk more about it in detail, but it really did change the revenue trajectory for the company. More importantly, quarter-over-quarter increase of roughly 79% revenue growth from $950,000 in Q1 to approximately $1.7 million in Q2. The important thing is what's underneath that growth. Enterprise retention remained strong; two major Fortune 500 customers renewed their relationship with Sky. In enterprise software, renewals matter enormously because customers evaluate the product after the initial sale. Customers have continued to choose Sky. We also added a significant new win in the financial services sector. This is a three-year multimillion-dollar recurring revenue deal.
We went through a very competitive RFP. We're super excited to have that customer on board, and they're scaling with us at the beginning of this quarter. It's a really important win for the team. It's a win for two reasons. First, it demonstrates continued demand from highly sophisticated regulated customers. Second, these are the type of customers where CXAI 2.0 can expand over time across additional modules, users and AI capabilities. The other big achievement for this quarter is we moved CXAI 2.0 into deployment, and that's a big win for us. The progression is win, deploy, adopt, expand—exactly what we want to replicate. With 2.0 we get an agentic AI platform that allows a user to navigate their workplace, navigate their work and navigate their experiences across the whole enterprise. That's very exciting. Our customers are selecting us because we have CXAI 2.0. The wins and renewals we got are all because of CXAI 2.0.
During the quarter we completed the EngineRoom transaction. For Q2 we only have one month because it was the month of June that EngineRoom is part of the revenue. It's been an amazing month because they've continued to get new clients. They've seen double-digit growth and they're going through an annual process where they've got commitments from existing clients. So it's been really good. All of these six factors combined have been super successful for the company. I want to congratulate the team on a job well done. It builds momentum and strengthens the foundation for CXAI 2.0 and our scale growth move forward. So let me tell you a little bit about EngineRoom, and what better than to roll a video. So operator, if you can roll the video.
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All right. Cool. That's pretty exciting. When I talk about EngineRoom, I talk about it as being transformative. As you can see from the video, they've been at it for 13 years, and they've made amazing progress in getting clients and a solid footing. EngineRoom does not simply add revenue; it changes the starting point for Sky. EngineRoom brings more than $8 million of revenue, approximately $1.6 million of adjusted EBITDA, a highly recurring revenue profile and more than 50 mid-market customer relationships. Strategically, three things matter even more. Number one, distribution. Sky historically sold into large enterprises through an enterprise sales process. EngineRoom has a structured relationship with dozens of mid-market businesses. That gives us a much faster proving ground and a future distribution channel for Sky AI products. Number two, business context. Sky already understands workplace and employee context; that's one of our moats and differentiators.
EngineRoom brings customer acquisition, performance marketing and growth data that allows CXAI 2.0 to expand from understanding how people work to understanding how businesses grow. Number three, cross-sell. We can introduce Sky capabilities into EngineRoom's customer base, and we can introduce EngineRoom's growth capability into Sky's enterprise products. The combined company has an enterprise anchor, a mid-market growth engine and a shared agentic AI platform. The combination moves Sky to more than $12 million of annualized revenue scale. This acquisition created scale. Our job now is to turn that scale into operating leverage. I'm super excited about this. I think this is the right move for the company. It positions us for the growth engine we've been talking about: double-digit growth. It gives us flexibility to innovate in a very interesting market, Australia. We'll go deeper into it in the investor forum.
This has been an amazing transaction for us. With that, I want to move on to the financials for Q2. I'm going to turn it over to Melissa to walk through the quarter in more detail. As you listen to the financial results, I would focus on one important relationship: how rapidly the revenue base is changing relative to the cost structure. Melissa, all yours.
Thanks, Khurram. The quarter-over-quarter comparisons demonstrate that a step-change is taking place in the business. Between Q1 and Q2 revenue increased from approximately $950,000 in Q1 to $1.7 million in Q2, representing, as Khurram previously mentioned, a 79% sequential growth. Our annual recurring revenue has increased from $3.6 million to $11.5 million. Net revenue retention increased from approximately 98% to 99.3%, continuing to demonstrate our strong retention across installed bases. Total assets increased from approximately $33 million to $36 million, and our cash EBITDA improved from approximately negative $3 million in Q1 to negative $2.68 million in Q2. EPS was approximately negative $0.10 compared with negative $0.09 in Q1. The key takeaway quarter-over-quarter is that the revenue base increased substantially while cash EBITDA improved modestly. We're still investing in integration and development of the combined businesses, but the operating model is beginning to show greater scale.
The year-over-year comparison also shows meaningful progress. Revenue increased approximately 42% from $1.2 million in Q2 of 2025 to $1.7 million this quarter. ARR increased from $4.5 million to $11.5 million, an increase of approximately 156%. Net revenue retention increased by more than five percentage points to approximately 99.3%. Assets increased 22% from $29.6 million to approximately $36 million. Cash EBITDA was approximately negative $2.7 million, which is similar compared to a year ago. EPS has improved from approximately negative $0.16 to negative $0.10 between the two years. The most significant change in the financial profile is the scale of the recurring revenue base, while we continue to manage investments required to support integration and future growth. Now let me put this cost structure into perspective. Total operating expenses increased approximately $275,000 quarter-over-quarter, or 5.6%.
Compare that with the approximately 79% sequential revenue growth. The increase in operating costs was driven primarily by the EngineRoom acquisition and associated operating activity. Importantly, these Q2 numbers do not yet reflect the benefit of the operating synergies we are implementing as we integrate the businesses. Our focus moving forward is straightforward: grow revenue faster than expenses. We expect to accomplish that through shared functions, tighter operating discipline, productized implementation, increasing automation and a higher recurring software contribution. That operating leverage is central to the financial strategy for the combined company. I'll turn it back to Khurram now.
Thank you, Melissa. I apologize—I was on mute. This was a really great quarter. We are showing the value of our technology platform and the EngineRoom acquisition. I want to put into perspective what I see as the value of this company as we move forward. The valuation is based on numbers that we get from KeyBanc, which does a monthly survey on software benchmarks and looks at recurring revenue-based businesses. As I think of our business now, it is an AI-powered software business that is at a larger scale. Last quarter we were roughly $1 million a quarter. This quarter we are $1.7 million a quarter. With the full EngineRoom integration, we will hit $3 million a quarter, or $12 million annualized, by next quarter. That shows real growth and momentum and scale. Based on that, and using conservative benchmarks for software businesses, we're at a roughly 9.7x multiple on next 12 months revenue.
I'm saying that revenue we have now we will have in Q3. To me, we're at an attractive stock price right now given where we're at. I believe we will continue to perform. Given our double-digit growth strategy, we believe by the second half of 2027 we will be growing toward the breakeven point. That's where our focus is. Our focus is to get to that level. The R&D expense has been done; now it's about growth and distribution. This is why we did the EngineRoom transaction; it's where we feel strongly about growth and scale. Can we sustain this growth and increase software mix and translate it into greater revenue scale? Absolutely. The two businesses are complementary and will help each other scale up faster. Now let me talk about one of the most important charts in the deck—the path to breakeven. This is a directional operating framework, not specific financial guidance. The EngineRoom acquisition gives us combined revenue of more than $12 million.
From here, there are several identifiable levers: first, organic growth—continue expanding the Sky enterprise business and EngineRoom's customer base; second, cross-sell—introduce additional Sky modules into existing enterprise customers; third, introduce Sky agentic AI products into EngineRoom's mid-market customer relationships; fourth, increased software monetization—Flow, Analytics, Events and our emerging personal execution capabilities called Beat; fifth, prioritize the mid-market motion—standardize products, standardize connectors, faster provisioning and lower cost to serve; and sixth, operating leverage—share infrastructure, technology, corporate functions and delivery capabilities across a larger revenue base. The operating model we are working towards is characterized by double-digit revenue growth, recurring revenue more than 95%, gross margin above 70%, software mix above 95%, increasing revenue per customer—we're already at $150,000 to $200,000 per client per year—and disciplined expense growth.
If we execute those levers we believe there is a credible path toward breakeven in the second half of 2027 followed by profitable growth. I want to close with why Sky. Why invest and be part of this journey? Reason one: EngineRoom is transformative—it immediately increases our revenue scale, gives us profitable operating capability and gives Sky a mid-market distribution engine that we didn't have. Reason two: CXAI 2.0 is now in production. This is no longer simply a roadmap or an AI narrative. We are ready to deploy across our clients. We're launching live with new clients and with existing clients. New enterprise logos are signing multiyear agreements; they would not be signing those unless they knew the roadmap and product were long-lasting. We are expanding the platform from workplace intelligence to personal execution (Beat) and growth intelligence (EngineRoom). Reason three: the financial model is becoming more scalable.
Q2 revenue increased approximately 79% sequentially while operating expense increased approximately 5.6%. That demonstrates opportunity for operating leverage as we integrate the businesses, grow recurring revenue and prioritize what we offer. So with that, I'm going to look into some questions that have come in. Okay. Question number one. How much cash do you have and what are your liabilities after the purchase of EngineRoom? I'm going to have Melissa take that.
Thanks, Khurram. Our cash as of 30 June 2026 is $11.7 million. Most importantly, acquisition costs related to EngineRoom have been largely paid. Any subsequent funds owing on the acquisition of EngineRoom are tied to an earn-out model.
Just to be clear, the EngineRoom acquisition was 65% cash and the rest was in earn-out. It's a two-year earn-out with growth factors tied to revenue specifically. So the earn-out will be funded by the growth itself. We have no other liabilities on EngineRoom, except the earn-out. Overall the asset base has increased, and integration has been successful so far. Next question: how quickly should shareholders expect the EngineRoom acquisition to be reflected in Sky's reported revenue? Melissa, do you want to take that?
Yes. We've already captured one month of combined revenues—that was the month of June. We will be able to demonstrate next quarter, Q3, the full combined impact over the three months of that acquisition as the combined revenue.
The next question is about revenue growth over the next 12 months. I gave some illustrative charts on our potential. We're focused on double-digit growth. We believe the scale we're getting with EngineRoom, the enterprise wins, and new logos signing multiyear multimillion-dollar contracts set us up well. EngineRoom has also increased their revenue profile and the number of clients, and their renewal process has been successful. We're focused on expense management and using AI across our functions to drive efficiencies. Our team members are cost efficient. I'm positive that in the next 12 months we will achieve much higher growth and work toward the breakeven target. We're going to take a brief pause now and join you back shortly for the investor forum. Thank you. It's 3:30 p.m. Pacific, 5:30 p.m. Eastern. Welcome to the investor forum. Thank you for people who joined the earnings call a few minutes ago.
We're going to be more strategic here, more product focused, really talk about the products, the business, the customers, the things underneath the hood that we're working on, and show you the path we believe will be successful for Sky. I think we shared the agenda before. I'm going to start off with the strategic view of the business and more strategic thinking about where we're at and where we're going. Then the team will tell you how we're doing what we're doing and what we plan to do next. We'll end with a fireside chat. I want to start with something I believe strongly. I've been involved in many technology transitions: the first mobile phones, 4G networks, Wi-Fi, 5G, cloud. I believe we are at the beginning of another major technology transition in enterprise software. Sky started by solving a real problem: how people interact with the workplace. After the pandemic, hybrid work created that need.
Underneath that experience we've built enterprise integrations, proprietary context, AI orchestration, data, security and trust. Now we're bringing these assets together into CXAI 2.0 or an agentic AI operating layer. Our strategy has three priorities: one, reposition Sky around this agentic operating layer, the context layer; two, use EngineRoom to give us immediate scale, mid-market distribution and a larger customer base; and three, prioritize what we learn into repeatable vertical AI solutions. This is not simply the evolution of our product; it can be the evolution of the company. We're going to focus on that. Why is the timing important? Enterprise software is evolving. The first generation created systems of record. SaaS and analytics added applications and visibility, but visibility is no longer enough. The next generation is about action—getting work done. AI agents will increasingly understand context, make recommendations, coordinate workflows and complete outcomes.
That's the layer I want Sky to own. Not another chatbot, not another dashboard, not another AI feature added to an application. The operating layer between enterprise systems, data, people and the actions that need to happen next. Employees want fewer applications, executives want decisions rather than more dashboards, and mid-market businesses want practical AI that produces value today without assembling teams of AI engineers. That's the opportunity we're designing Sky around. Let me put it into more detail. We started with place. Sky Flow understands where and how people work—workplaces, spaces, resources, presence and experiences. Now we are moving into person with Beat. Beat is about personal and team execution: what do I need to accomplish, what has changed, what matters most right now, what should happen next, and eventually what can the platform safely do for me in a secure enterprise environment.
Now we're adding business through EngineRoom: how the company finds customers, converts demand, where marketing is working, where it isn't, and what actions should happen next to grow the business. So think about what we're assembling. Place gives workplace context. Beat gives personal and team context. EngineRoom gives customer and growth context. Underneath all three is one shared Sky platform. It senses, prioritizes, acts, verifies and critically, it learns—and it gets things done. We're about outcomes. Context shouldn't be just another insight; context should lead to an outcome. Next, the market. We're participating in three large categories that are all growing: digital workplace platforms, enterprise agentic AI and marketing automation and growth intelligence. The compounding effect is significant over the next several years. We're exposed to three categories, and we sit at their intersection.
Enterprises buy AI to make employees more productive, make better decisions, reduce cost and grow revenue. These are precisely the outcomes these three businesses allow us to attack together. Now what are we building? Across the top you see the context domain, the work intelligence—place and person—the growth intelligence—business—and future verticals. Underneath is the Sky agentic platform. The philosophy is simple: understand context, recommend action, get the right approval, complete the outcome. BOND is our agentic engine: a multimodal, multi-agent orchestration system that provides agentic execution capability. Cortex provides intelligence, context, knowledge graphs, personalization and analytics. We surround that with enterprise requirements: identity, auditability, human control, connectors, governance and other controls enterprises care about. We are multi-cloud; we work with Google Cloud, also partner with AWS and have clients using Azure.
We use best-in-class technology and focus on enterprise context, orchestration, permissions, actions and outcomes. We're not betting on one foundational AI model; models will change. Our value is that enterprise context and orchestration layer. That is the operating layer. The architecture gives us leverage: we don't have to build a different technology stack for every use case. The same orchestration layer can support workplace agents, growth agents, analytics agents, automation agents and eventually industry-specific agents. The more repeatable the agents become, the more efficiently we can take them to the mid-market. This is where EngineRoom comes in: it changes how we can take Sky to market. Sky gives an enterprise anchor and proven technology; EngineRoom gives a mid-market customer base, recurring revenue, commercial data and people who understand how to drive measurable business outcomes.
Australia gives us an excellent launch pad. We can launch, learn and scale across trades and field services, construction, automotive, health care, professional services and manufacturing. These are real-economy businesses that need practical AI for customer acquisition, faster response, better scheduling, lower acquisition cost and higher employee productivity. EngineRoom gives us more than 50 customer relationships and a recurring revenue foundation. Our objective is to identify workflows, repeatedly create value, productize them on the Sky platform and distribute them more broadly. Services help us discover the problem; software gives us the scale. Now I'll talk a little more about the combined platform and economics. The acquisition gave us scale and $12 million-plus revenue. CXAI 2.0 must give us operating leverage. We have a combined revenue north of $12 million, enterprise customers on the Sky side, 50 mid-market relationships in EngineRoom, recurring revenue, data and distribution.
The next phase pulls four levers: grow and expand existing businesses, cross-sell and distribute across the combined customer base, prioritize AI and new modules to increase software revenue, and create operating leverage as the company scales. Q2 showed early signs of this. Our ambition is clear: higher recurring revenue, higher software mix, higher gross margins, more revenue per customer and a path to breakeven and profitable growth. The acquisition creates scale; the platform has to create the leverage. I'm excited that this is a path we are on. We have a strong team to execute: Chris running enterprise business in North America, Adam running Australasia and EngineRoom, Melissa as interim CFO, and a global CTO team across Silicon Valley, Canada, Australia and Southeast Asia. AI innovation is global, and this structure is designed around speed, accountability and execution. With that, I will hand it to Chris to talk about the North American enterprise business. Chris, go ahead.
Well, thank you, Khurram, and welcome all. I'm Chris Wiegand, General Manager of North America. I'm an entrepreneur at heart, and I couldn't be more excited about this whole AI transformation. It's truly changing things in workplaces. We've had the best year we've ever had. We've signed the largest deals I'm going to take you through. We've got our 2.0 product deployed and working. We've got a new module, Events, which I'll tell you about, and Beat is coming as well. I'll take the next 20 minutes to walk you through what's happening on the ground: how we're doing business, who the customers are, and show live videos of the product. First, our customers. These are some of the largest and biggest companies in the world—leaders in their space. They've gone through very rigorous diligence, tough RFPs and trials. These are very secure, complex environments. By winning in these customers we've proven the product in very demanding settings.
One of our biggest wins this year is a top financial institution. They are global, with dozens of sites and thousands of users coming online. This was a 12-month pursuit and they went out to market; Sky came out on top. Next, a global asset manager—we're closing with them and they're starting at year-end with a five-figure number of users. Next is a leading U.S. insurer. This is a few thousand users and represents a bridge to the mid-market. We're deploying for their brand-new headquarters and moving into testing now; they will go live in September. We also have an amazing installed base: enterprise customers choosing to stay with us. One of our largest financial services customers renewed, and our largest media and entertainment customer renewed and expanded. Where is this all going? More revenue. Directionally, new customers come online and represent roughly a one-third uplift in revenue as they turn on.
Validation is critical. You can win deals, but you have to deliver. Let me walk you through how we deliver for the leading U.S. insurer. It starts with an enterprise agreement and a detailed scope of work. We build it and integrate into their core systems. The full value of Flow is taking disparate systems and putting them into a cohesive system that an employee can use quickly and easily. We're in testing with most integrations done for a brand-new headquarters. In the next 30 days we have critical finalizing and testing steps. The client is happy; we're on schedule and will scale to the population. Important points: we have transitioned from custom code to configuration, which means deployments are faster and repeatable. We're moving away from large one-time upfront fees toward per-user software pricing. The market wants value-based pricing; value comes from people using the product. When we deploy, we expand through utilization and adoption.
We're aligned with customers: they want everyone using it so they realize value and we realize recurring software revenue. Now our products. Flow enables employees to book spaces, wayfind, interact and get news. All data goes into SkyView—our analytics platform—which isn't just dashboards; it turns operational data into actionable insights. Events is a brand-new product; we'll be GA in September. We've solved a major industry problem—workplace events management—and I think many in the audience will recognize the pain. Beat is our productivity tool at the personal level: it helps people prioritize what is most important next and prepares or does work to keep teams moving. Beat will be available in Q4. Underneath all this is the Sky agentic platform: sense, prioritize, act and verify. Agentic means it doesn't just remind you; in many cases it executes on your behalf and then verifies outcomes.
We'll show two videos: one demo of Flow in a 'Know Me' scenario—an employee's conversational view of their day—and a demo of SkyView from a manager's perspective to show insights and analytics. David, if you wouldn't mind, let's go to the video, and I'll see you back in about five minutes. (Presentation)
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I'm so glad everybody got to see that. I get excited every time I demo it. People want to work that way: conversationally, with less friction. Next, Events—our newest module. The current state of events management in enterprises often feels chaotic. I'm not referring only to large public events; I'm talking about workplace events: all-hands calls, sales kickoffs, training events. Admins managing these events deal with calendar invites or requests, e-mails, tickets to catering, security, AV, and specifics for multiple locations around the world. The event changes, demands pile up, and the admins are literally under stress because it is high-stakes and high-risk: food, rooms, executives, external customers. We built a single orchestration workflow that starts with a user requesting everything they need. They can see what's available and request space, catering, AV. Approval workflows route to the departments that need to approve.
At the end you have a single system wrapping it all up. This is an add-on for our existing customers and a stand-alone product for the market. We have demand campaigns launching now and will leverage EngineRoom's platform for promotion. I'm very excited—this is a significant product opportunity. Importantly, the enterprise market has proven our model and technology. We won't leave the enterprise market; we'll continue to serve it and win big deployments. But productizing what we've learned in enterprise allows us to move down-market and scale. The connectors will be pre-configured for fast provisioning, drop-in plug-and-play deployments. We have EngineRoom customers who are great candidates. We have resellers signed up and ready, and marketplace partners so customers can buy via cloud marketplaces like Google. Time to value will be fast, which will drive scale. Our operating principles: one P&L, common metrics—EBITDA, bookings, revenue, growth and scale.
Our #1 goal is to deploy. We're mid-flight with large projects and they're being deployed. That generates recurring revenue when customers are live and adopted. Events is a burning problem—every customer has it. We expect upsells to existing customers, stand-alone sales, and a path for customers to migrate from our previous platform. Major expansion is exciting: Beat will be a productivity tool for employees and will drive expansion across enterprise and mid-market. By Q4 we'll have a new cohort of revenue—customers we don't have today will be generating recurring revenue. Two engines have become one: Sky and EngineRoom. We are better together. Sky brings enterprise anchor and technology; EngineRoom brings mid-market customers and growth expertise. We now have a common backbone in the Sky agentic AI platform and can deliver value with a low-cost model through BOND and Cortex. I'll turn it over to Adam, who has scale and profitable growth experience.
Thanks, Chris. Hello from Australia to everyone around the world. I'm Adam Laurie, co-founder of EngineRoom and now General Manager of Sky's Australian operations. EngineRoom has been a major part of my life for more than 13 years, and I'm excited to introduce it to Sky shareholders and those joining for the first time. Our purpose is to use data, digital and AI effectively to enable smarter decisions and unlock greater growth potential for our clients. We are a revenue-generator for our clients: profitable, established and with a very strong track record in the Australasian market since 2013. We're multi-award winning across performance, innovation and people. What we do is fully integrated growth marketing solutions designed to capture high-intent demand and drive sustainable, profitable growth. We have three major divisions: our Martech platform, fractional CMO and Marketing-as-a-Service.
The problem we solve: businesses have difficulty building a cost-effective and scalable customer acquisition engine. Without customers, businesses struggle to grow. They find it hard to measure marketing ROI and demonstrate commercial impact. They have disconnected business, customer and digital data that don't communicate. They fail to convert knowledge, data and AI into commercial advantage. We solve that by building growth marketing solutions that deliver measurable scalable and profitable outcomes. We focus on the intent market—the high-intent customers who are actively looking to purchase—and that market is measurable and high converting. Why do customers choose us? Proven results over 13 years, cutting-edge technology, measurable ROI, end-to-end solutions and unmatched in-house expertise. Our Martech platform empowers marketers and business owners to make data-driven decisions. We have three linked parts: strategize—where are we going; analyze—how are we performing and what are the opportunities and risks; and optimize—what actions will drive improvement.
Each module has a specific application. How we use AI: LLMs understand language, but EngineRoom teaches AI to understand the business. We bring core data: business goals, brand identity, customer segments, competitor information and performance data. That forms a knowledge base for higher-quality insights and recommendations. This is continuous learning: storing data month over month improves outputs and delivers a living, learning tool for clients. That difference—prompt-and-answer versus knowledge-and-action—is critical. Today we'll show an example of the platform using a smaller client who kindly enabled us to showcase their data. (Presentation)
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I hope everyone enjoyed that. Why EngineRoom and Sky? CXAI's Australasian growth engine is EngineRoom. We've grown substantially year-on-year with recurring profitable growth. We target customers in the 5-to-500-employee range. Our average client yield is around $200,000 per annum. We have 93% recurring revenue and average client life beyond four years. Our customers span professional services, home services, manufacturing, industrial and more—diversified industries. We have a proven leadership team staying on board and award-winning culture. The growth opportunity in the Australasian market is significant. Combining EngineRoom expertise and customer relationships with Sky's agentic AI capabilities will accelerate product development and expand data and intelligence capabilities. EngineRoom's business knowledge plus CXAI agentic AI will improve intelligence, reasoning and action outcomes.
From a development architecture perspective, we've built a strong data source-of-truth and business knowledge base and started on reasoning and a decision engine. The next step is to strengthen reasoning and decision intelligence and then move to agentic, autonomous business execution leveraging CXAI's capabilities. Commercial opportunities: expand into new verticals, strategic partnerships and channel expansion, technology innovation to increase customer value and retention, use AI to drive efficiencies and profitability, and strengthen competitive differentiation. The capacity for growth is enormous if we execute at a high level. Thank you; I'm excited about the next steps.
Thank you, Adam. Lovely to introduce the EngineRoom story. We're very excited and we'll hand it over next to Zoe for a fireside chat.
Hi, everyone. I'm Zoe Chen, a workplace strategist at Veldhoen + Company. I spend most of my time inside companies while they're changing how they work—the actual implementation, not the strategy deck version. It's the part where somebody has to tell 300 people they're losing assigned seats and will share desks in the future. What's interesting now is that everyone in buildings is talking about AI, even in projects about physical space. I want to give you three observations I'm seeing in the field. These are not predictions, just patterns I keep encountering. First: automation historically started at the bottom and worked its way up—the assembly line, ATMs, self-checkout. Machines were good at repeating physical tasks and bad at everything else. The safe advice was to get education and move away from repetitive work. That held for decades until this wave of AI. Tasks we thought took long to learn—writing, analyzing, summarizing, coding—are now easy to replicate.
Tasks a child can do without thinking—walking into an unfamiliar room and picking up an oddly shaped object—remain hard. There's data now. Anthropic publishes an Economic Index analyzing millions of real AI conversations mapped to occupational data. The heaviest AI users cluster in mid- to high-wage occupations. Very low-paying and very high-paying jobs show low AI use because they involve manual dexterity—examples: shampooers and obstetricians. AI has hit information work—the desk jobs—hard and quickly. About half of jobs have already had at least a quarter of tasks touched by AI. The flip side is a large portion of the workforce in near-zero exposure: electricians, plumbers, HVAC technicians and mechanics. If a job requires being physically present and using hands, this AI wave mostly isn't coming for it. But being protected isn't the same as being unconstrained. Take a three-truck plumbing company.
What's stopping it from becoming a ten-truck business? It's not plumbing skill; it's everything away from the job site: whether a quote went out, follow-up on estimates, answering reviews, lining up the next job. That back office is where the owner is least equipped and least interested. There's no shortage of software, but every tool needs setup, connection and babysitting. Nobody started an HVAC business to become a CRM administrator. The choice for two decades has been stay small or spend evenings learning software. This wave of technology feels different: the promise isn't another tool to master; it's the outcome without the operating burden. If that lands, small operators can get the back office that used to require real scale. Second: personalized intelligence is already part of life. Your phone sorts email before you look at it. Your news app highlights the stories you care about.
Your grocery app knows you're low on coffee. These systems feel like they just work. At work, that stops. When every app does its own thing, nothing knows you and nothing talks to anything else. People become the integration layer. AI has changed how we handle information—notes, emails, transcripts are easier to work with. What it hasn't done is meet people where they are in physical space. Buildings don't know you're in them. The room booking system doesn't know your team is in today. Your calendar doesn't know you're on the other side of campus with eight minutes to get to your next meeting. The exhausting part of the day isn't the hard job you signed up for; it's all the surrounding tiny decisions: finding a room, time zone math, who's in today, where to sit when half your team is scattered. Each takes seconds, but 40 of them by 3 p.m. burns mental energy. GPS solved the path problem for travel—you didn't want another map; you wanted to arrive.
People want fewer decisions at work. Work lags behind life in these tools. People improvise and use AI on phones or tools they buy themselves because it helps. What people do on their own eventually becomes what they expect at work. Third: some research shows AI pilots don't always show a measurable return in short windows, but the interpretation matters. An MIT report looked at whether pilots moved the P&L within about six months and found many didn't. A lot of pilots, especially in sales and marketing, are mid-cycle and six months is too short to measure P&L changes. Another number from the same study is revealing: when companies brought in a specialist to deploy, the project reached production about two-thirds of the time; when built internally it reached production about one-third. Twice the success rate for specialists. Why? It's an execution gap, not a technology gap. Everybody has access to the same models.
The specialists have done integration, permissions, governance and edge cases before. Internal teams are solving each issue for the first time while doing many other things. Tools that work for individuals often stall inside a company because they don't learn the specific workflow. Internal projects underestimate the plumbing: data access, edge cases—real companies are edge cases. Specialists improve the success rate. Building in-house feels like control but often becomes slower and produces technical debt. However, build-versus-buy isn't universal. If you have proprietary data, unusual workflows or are in a regulated high-risk situation, building can be right. It's not the default anymore. To recap: the pressure landed on information work, not physical work; we have personalized intelligence everywhere except where we spend 40 hours a week, which is the opportunity; and the capability is here—what's being worked out is deployment and execution. Happy to dive into any of this.
Thanks, Zoe. That's great context. You're seeing these trends in the field—these are not just desk research. We'll do some Q&A. Adam, you're the expert on trades and home services. Zoe, where do you see the biggest opportunity for AI to turn operational and customer data from trades into better decisions and outcomes?
Thanks, Zoe. I look forward to you visiting Australia. Trades and home services generate enormous operational and customer data every day. Where do you see the biggest opportunity for AI to turn that data into better decisions and business outcomes?
Great question. The data is all there: what broke, what it took to fix it, how to get customers, which channels work. But it's scattered—in scheduling tools, a text thread, invoices, Post-it notes, and in someone's head. The owner often has to pull it together and is busy on the roof all day. The data exists, but nobody has the bandwidth to analyze it. The patterns are clear though: which jobs actually make money once you count drive time and callbacks, which estimates are consistently wrong, and which work types you should do more of. The opportunity is less exotic than it sounds: show a business what it already knows but has never seen in one place and turn that into something they can act on Monday morning. Make it a shared understanding as the business scales, not something reliant on the owner. That’s where AI provides value for trades businesses.
Thanks, Zoe. I appreciate that. I want to add one point. The next generation entering the workforce are AI natives—they've coded and used AI tools for a long time. How does deployment scale when models, tools and approaches change so fast? How can deployment be repeatable at enterprise scale rather than a million different bespoke solutions?
From adoption inside companies, the mid-market can move fastest because there are fewer stakeholders. Large enterprises have teams and long roadmaps, while a 200-person company may have someone for whom this is the fourth priority. For mass deployment it can't require a dedicated owner, extensive project plans or complex rollouts. It needs to spread the way things spread in smaller companies: someone uses it, it visibly saves time or delivers better results, and others adopt it. Viral, visible improvements—not orchestrated rollouts—drive adoption in those segments. That’s how it scales.
On that note, this aligns with value-based selling. Mid-market customers require fast time-to-value and easy deployment. They have no patience for large integrations. The product has to be easy to deploy and loved by users, and it has to remain fresh and useful so it gets used continuously. There's much more to talk about, but great insights.
I love the mid-market. They make decisions rapidly and focus on outcomes. As long as you connect what you do to a real business outcome—if I spend $2 and make $10—they will move. We chose mid-market because that's where acceleration and adoption happen. They don't have the internal support layer, so we have to be that capacity. Our model assumes that and is built to enable them.
Thank you, Zoe—great conversation. We'll have you back again. Khurram, a couple more questions have come in. Has the Google partnership helped, and how?
Good question. We use Google across both sides of the business for cloud infrastructure and to help get our clients advanced products and access. We're working on the Google Marketplace to launch to mid-market, which is a big opportunity. Our engineering team worked with the Google team to get the Events product into production within a quarter or less. Chris, do you want to expand on how Google helped?
Yes. It didn't go fast because it was easy; it went fast because we had significant IP and integrations already—our IP over the last number of years is highly sophisticated and proprietary. We understand how corporate workspaces operate. The Google team, our partners and our internal team glued it together to create a seamless orchestrated workflow. Google is a hyperscaler; we're working in secure environments with high standards. We plan to be in the Google Marketplace so customers can sign up, click through agreements, pay and get the product—reducing contracting friction and shortening time to value. That's how we go from small deployments to prime-time scale.
Next question: what are the major synergies between legacy business and EngineRoom, and what do you expect go-forward OpEx levels to be quarterly? There's a big opportunity for synergies. We'll combine infrastructure, cloud, locations—EngineRoom has operations in Manila where we also have people—which reduces costs. We'll leverage EngineRoom's distribution and Sky's enterprise access and channel. The realizations of synergy targets will happen over the next six to twelve months; some are already happening this quarter. Adam, would you like to talk about OpEx strategy?
Our business model has always prioritized profitable, sustainable growth. We last took capital several years ago and have focused on being below revenue on OpEx so we can invest sustainably. The pathway is assessing what operational costs can be reduced as a percentage of revenue as we integrate. We're actively working through that process and will report on progress as we realize synergies.
Last question for Chris: with new products like Flow, Beat and Events, what's our competitive moat? How do we create and defend it given many competitors?
We have several elements to our moat. First, IP and proven deployments: we have technology deployed in some of the world's most complex environments at scale. That's not just a demo; it's thousands of users in regulated environments. Second, differentiated agentic architecture: BOND and CORTEX allow us to do agentic orchestration and LLM usage at a fraction of the cost compared to many alternatives, which is a meaningful advantage. Third, context and spatial awareness: we understand workplace spatial context better than most. Fourth, integrations: our connectivity to core systems creates data and a superior user experience. Fifth, user experience and outcomes: we're creating agentic solutions that do things for people rather than simply being another assistant or dashboard. If users love the product, it becomes viral within organizations and drives referrals and retention. Those together form a meaningful moat.
Thank you, Chris and Adam. I want to leave you with three takeaways from today. One: we have changed the scale of CXAI—moving from roughly $4 million annualized revenue at the start of the year to a combined platform with more than $12 million of annualized revenue scale. We now serve both enterprise and mid-market customers. Two: CXAI 2.0 is moving from vision to commercial execution. The platform is in production with customer deployments, major renewals and new multiyear enterprise wins. EngineRoom's customer base is an additional channel to prove and distribute our AI products. Three: We have a clear operating priority—profitable growth. The next phase is not just adding revenue but combined double-digit growth with higher recurring revenue, stronger software mix, operating leverage and disciplined execution. Our directional objective is to move toward breakeven in the second half of 2027 and profitable growth beyond that.
Q2 2026 is the quarter in which CXAI began moving from a workplace software company with an agentic AI vision into a scaled agentic AI platform with enterprise proof, mid-market distribution and a credible path to profitable growth. The acquisition creates scale; CXAI 2.0 creates the opportunity for operating leverage; execution determines the value we create. Thank you to our customers, employees, partners and shareholders. We look forward to updating you next quarter; we plan to do that in November and are considering another investor session at year-end or early 2027. Thank you, everybody. Operator, you may close the call.
(Call closed)