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 differently this quarter. But I want to also tell you as investors the three things I want you to take away from 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, 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, and what is our outlook for the quarter and for the future. Then we'll take a short intermission and transition into our investor forum. The investor forum will focus on, 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 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'll enjoy her perspective, and we'll close. So it's going to be a packed agenda. I know many of you have been sending questions. We'll take some questions in the Q&A section as well. 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 and 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, let me make sure you have the disclaimer slide on forward-looking statements and the safe harbor. Please review the safe harbor, non-GAAP disclosure in today's presentation and our SEC filings with applicable risks, assumptions and reconciliations. We will be filing the 10-Q tomorrow, and you can read that when you get that. Let me talk about the company we have today. The company we have today is impressive. We have deployed globally across around 200 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. Now we're excited to welcome the Australian team, headquartered in Melbourne, with coverage 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 working hard to make AI successful in the enterprise market we're in. Before 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 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 significant strides in the last two quarters. Chris will talk about those customer case studies, but it's been strong. CXAI 2.0 expands that opportunity. We're moving primarily from understanding places, which is our Flow product — where and how people work — to person, which we call Beat: what an individual and team need to accomplish and what should happen next in a worker's day. Now with EngineRoom, we're moving into business: how companies acquire customers, convert demand and grow. That business context is significantly strengthened by EngineRoom. Any of those experiences use 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 now have an agentic platform that we can leverage across multiple verticals, and importantly, we have a new distribution mechanism through EngineRoom. This is the transformation I'm talking about and executing on, and we're 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. As you can see in our highlights for the quarter, the six main highlights — but the biggest is the EngineRoom transaction. It is transformative. It changed the revenue trajectory for the company. Quarter-over-quarter revenue increased by about 79%, from $950,000 in Q1 to approximately $1.7 million in Q2. More important is what's underneath that growth. Enterprise retention remained strong; two major Fortune 500 customers renewed their relationships with Sky. In enterprise software, renewals matter enormously because customers evaluate the product after the initial sale. Customers continue to choose Sky. We also added a significant new win in the financial services sector: a three-year multimillion-dollar recurring revenue deal won through a competitive RFP. We're excited to have that customer on board; they began scaling with us at the start of this quarter. This is an important win for two reasons: first, it demonstrates continued demand from highly sophisticated regulated customers; second, these customers are the type where CXAI 2.0 can expand over time across additional modules, users and AI capabilities. Another big achievement this quarter is we moved CXAI 2.0 into deployment — that's a major milestone. The progression is win, deploy, adopt, expand — exactly what we want to replicate. CXAI 2.0 is an agentic AI platform that allows a user to navigate their workplace, navigate their work and navigate their experiences across the enterprise. That's very exciting. Our customers are selecting us because of CXAI 2.0; the wins and renewals we received are all because of CXAI 2.0. During the quarter, we completed the EngineRoom transaction, so for Q2 we only have one month — June — of EngineRoom revenue. It was an important month: EngineRoom continued to get new clients and delivered double-digit growth. They go through an annual process with commitments from existing clients, so it has been very positive. All six factors combined made this a successful quarter. I want to congratulate the team. It builds momentum and strengthens the foundation for CXAI 2.0 and our scale growth moving forward. Let me tell you a little about EngineRoom and then we'll roll a video. All right. Cool. That's pretty exciting. When I talk about EngineRoom, I call it transformative. As you saw from the video, they do exciting work. They've been at it for 13 years and have made amazing progress getting clients and 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. But strategically, three things matter more for me. Number one: distribution. Sky historically sold into large enterprises through an enterprise sales process. EngineRoom has structured relationships 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 is one of our moats and differentiators. EngineRoom brings customer acquisition, performance marketing and growth data that allow 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 excited — it's the right move. It positions us for the double-digit growth engine we've been discussing and gives us flexibility to innovate in an interesting market like Australia. I'll talk more about that 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, 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 a step-change 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 mentioned, approximately 79% sequential growth. Our annual recurring revenue increased from $3.6 million to $11.5 million. Net revenue retention increased from approximately 98% to 99.3%, continuing to demonstrate strong retention across our installed bases. Total assets increased from about $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 also increased 22% from $29.6 million to approximately $36 million. Cash EBITDA was approximately negative $2.7 million, which is neutral compared to a year ago. EPS improved from approximately negative $0.16 to negative $0.10 year over year. 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 the cost structure into perspective. Total operating expenses increased approximately $275,000 quarter-over-quarter, or 5.6%. However, compare that to 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 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. 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. Let me put into perspective what I see as the value of this company as we move forward. This valuation is based on numbers from KeyBanc, which surveys software benchmarks and looks at recurring-revenue software businesses. As I think of our business now, it is an AI-powered software business at a much 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 be hitting $3 million a quarter or $12 million annualized by next quarter. That shows real growth momentum and scale. Based on that, using conservative software multiples on next twelve months' revenue, the business is at an attractive stock price relative to where we are. This is illustrative, not valuation guidance — I'm using industry benchmarks to show the potential value of the company. We've now built the agentic platform and the R&D expense has been incurred. Now it's about growth and distribution, which is why we did the EngineRoom transaction. Can we sustain the growth and increase software mix and translate into greater revenue scale? Absolutely. The two businesses are complementary and will help each other scale faster. Now let me talk about probably the most important chart in the deck: the path to breakeven. This is a directional operating framework, not specific financial guidance. The EngineRoom acquisition gives a combined revenue base of more than $12 million. From here, several identifiable levers exist. 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 personal execution capabilities called Beat will increase recurring software revenue from the same platform. Fifth, prioritize the mid-market motion: standardize products and connectors, enable faster provisioning and lower cost to serve. 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 above 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 these 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 revenue scale and gives us a mid-market distribution engine. Reason two: CXAI 2.0 is now in production. This is no longer just a roadmap. We are ready to deploy across clients. We have major renewals and new multiyear enterprise wins. 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 roughly 79% sequentially while operating expense increased about 5.6%. That does not mean the work is finished, but it demonstrates the opportunity for operating leverage as we integrate the businesses. So with that, I'm going to look into some questions that have come in. Let me see. 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 June 30, 2026 is $11.7 million. Most importantly, acquisition-related costs for EngineRoom have been largely paid. Any subsequent funds owing on the EngineRoom acquisition are tied to an earn-out model.
No, that's good. To be clear, the EngineRoom acquisition was 65% cash and the rest is an earn-out. The earn-out is a two-year model with growth factors tied specifically to revenue, so the business can earn out itself. We have no other liabilities on EngineRoom except the earn-out. Overall, the asset base has increased and the 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 — the month of June. Next quarter, Q3, we will demonstrate the full combined impact across the three months of that acquisition.
Okay. One more question: what do you think of revenue growth over the next 12 months? As I illustrated earlier, we are focused on double-digit growth. We believe the scale from EngineRoom, the enterprise wins and new multiyear, multimillion-dollar contracts position us well. EngineRoom has increased its revenue profile and client count with successful renewals. We're positive about the next 12 months. Our goal remains breakeven in the second half of 2027 if we execute our plans and growth vectors. We're also focused on expense management — we're leveraging AI across our functions and seeing efficiency gains. Our team is cost-efficient. I'm confident that in the next 12 months we will achieve higher growth and progress toward our breakeven target. We're going to take a brief pause and then join you back in about 60 seconds for the investor forum. Thank you. All right. It's 3:30 p.m. Pacific, 5:30 p.m. Eastern. Welcome to the investor forum. Thank you to those who joined the earnings call a few minutes ago. We're going to be more strategic and product-focused here and talk about the products, the business, the customers, what's under the hood and the path we believe will be successful for Sky. The agenda: strategic view, then the team will tell you how we're doing it and what we plan next, and then we'll end with a fireside chat. I want to start with something I believe strongly: I've been involved in many technology transitions — mobile phones, 4G, Wi-Fi, 5G, cloud — and I believe we're at the beginning of another major transition in enterprise software. Sky started by solving how people interact with the workplace after the pandemic and hybrid work. Underneath that experience we've built enterprise integrations, proprietary context, AI orchestration, data, security and trust. We're bringing these assets together into CXAI 2.0, an agentic AI operating layer. Our strategy priorities: number one, reposition Sky around this agentic operating layer (the context layer); number two, use EngineRoom to give immediate scale, mid-market distribution and a larger customer base; number three, prioritize what we learn into repeatable vertical AI solutions. This is not simply an evolution of product — it can be an evolution of the company. Why is timing important now? Enterprise software is evolving: systems of record, then SaaS and analytics gave applications and dashboards. Visibility is no longer enough. The next generation is about action — getting stuff done. AI agents will increasingly understand context, make recommendations, coordinate workflows and complete outcomes. That is the layer I want Sky to own: the operating layer between enterprise systems, their data, people and the actions that need to happen next. Employees want fewer applications; executives want decisions rather than more dashboards; mid-market businesses want practical AI that produces value without assembling teams of AI engineers. That's the opportunity we're designing Sky around. Let me explain in more detail. We started with place: Sky Flow understands where and how people work (spaces, resources, presence). Now we're moving into person with Beat: personal and team execution — what do I need to accomplish, what should happen next, and eventually what can the platform safely do for me. Now we're adding business with EngineRoom: how a company finds customers, converts demand and grows. Think about assembling place, person and business context on one platform. Underneath these domains is the Sky agentic platform: it senses, it prioritizes, it acts, it verifies and it learns. We're about outcome: context should not be just another insight; it should lead to an outcome. We are focused on creating those outcomes for clients. Market context: we participate in three large categories — digital workplace platforms, enterprise agentic AI and marketing automation/growth intelligence. The compounding effect across these categories is significant; we see a multi-decade opportunity. The intersection matters because enterprises buy AI to make employees more productive, make better decisions, reduce cost and grow revenue — outcomes that these three areas address together. What we're building: our Silicon Valley team and the Australia team have collaborated. Focus on the Sky agentic platform: understand context, recommend action, get approval, complete outcome. Our agentic engine, BOND, is a multimodal, multi-agent orchestration system for agentic execution. Cortex provides intelligence, context, knowledge graphs, personalization and analytics. We surround that with identity, auditability, human control, connectors and governance — enterprise requirements. We're integrated with partners and cloud providers — we have a strong relationship with Google Cloud, we partner with AWS, and some clients use Azure. We're multi-cloud and access best-in-class models and infrastructure. We're not betting on one foundational AI model; models will change. Our value is enterprise context, orchestration, permissions, actions and outcomes. The model provides intelligence; Sky makes that intelligence useful inside the enterprise. Another key part is leverage: one orchestrational layer can support workplace agents, growth agents, analytics agents, automation agents and industry-specific agents. The same core capabilities — data, context, permissions, workflow orchestration, auditability and completing work in the system where that work belongs — allow scale with one platform and many specialized agents. That is our play: one platform, many agents, real outcomes. EngineRoom is strategically important: it's not just revenue. Sky provides an enterprise anchor; EngineRoom gives a mid-market customer base, recurring revenue, commercial data and people who understand how to drive measurable business outcomes. Australia is an excellent launch pad to learn and scale, working with trades, field services, construction, automotive, healthcare, professional services and manufacturing. These businesses need practical outcomes — more customers, faster response, better scheduling, lower acquisition cost and higher employee productivity. EngineRoom brings more than 50 customer relationships and a recurring revenue foundation. Our objective is to identify workflows, productize them on the Sky platform and distribute them broadly. Services help discover problems; software gives scale. The acquisition gives us scale; CXAI 2.0 must create operating leverage. We have combined revenue north of $12 million and recurring revenue, data and distribution. The next phase is pulling four levers: grow and expand existing businesses; cross-sell across the combined customer base; prioritize AI and new modules to increase software revenue; create operating leverage as we scale. This should result in revenue growing faster than infrastructure requirements. Our ambition: higher recurring revenue, higher software mix, higher gross margins, more revenue per customer and a path to breakeven, then profitable growth. The acquisition creates scale; the platform must create leverage. We have a great team: Chris runs enterprise in North America; Adam runs Australasia and EngineRoom; Melissa is interim CFO; we have a global CTO team across Silicon Valley, Canada, Australia and Southeast Asia. AI innovation is global and our structure is designed for speed, accountability and execution. We want talented people close to customers and technology globally. I'm excited to introduce the team. I'm going to transition now to Chris, who's going 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'm excited about this AI transformation. It's changing how people work. What's also exciting is we've had our best year ever. We've signed the largest deals to date. We've got our 2.0 product deployed and working. We have a new module, Events, and Khurram mentioned the new product Beat. For the next 20 minutes I'm going to take you through what's happening on the ground: how we're running the business, what customers are doing, and I'll show you live videos demonstrating the product. We have customers that are some of the largest and most respected companies in the world. They've gone through rigorous RFPs and diligence. These are tough environments to deploy in — secure and complex — and by going through deep sandbox trials and many meetings, we've come out on top. That's the pattern: we keep winning in regulated, highly secure environments. One of our biggest wins this year is a top global financial institution with dozens of sites and thousands of users to come online. This was a more-than-12-month pursuit and Sky won after going out to market. Next, a global asset manager is closing and will start at the end of this year, with a quick implementation for five-figure users. Another is a leading U.S. insurer — a few thousand users — that serves as a bridge to the mid-market. We're deploying there now with a brand-new headquarters and are in testing. These wins demonstrate how we've productized our deployments rather than building custom code. This is critical: configuration instead of custom code means faster deployments and a path for mid-market scale. We are also moving away from big one-time implementation fees to per-user software pricing tied to value and usage. Adoption drives expansion. Our installed base continues to renew and expand: a large financial services customer renewed, and a major media and entertainment company renewed and expanded. Where is this all going? More revenue and expansion. Directionally, new customers coming online represent about a one-third uplift in potential revenue. Validation is key: you can win deals but must deliver. I'll quickly walk through a timeline for the U.S. insurer deployment: enterprise agreement with detailed scope, integrations into core systems, deployment of 2.0, testing, finalization and scale to the population. The client is happy and we're on schedule. The point is we've transitioned to configuration and value-based pricing, aligning incentives for adoption and expansion. Now, product overview: Flow is our product for booking spaces, wayfinding and workplace interaction — it's deployed today. All the data from Flow feeds SkyView, our analytics platform, which turns operational data into insights. Events is a brand-new product GA in September; it addresses a major industry problem for managing workplace events and the complex orchestration required. Beat is a productivity tool coming later that helps individuals and teams by prioritizing what to do next, reducing message overload from multiple systems and keeping work moving. Underneath all of this is the same Sky agentic platform: it senses, prioritizes and acts. Agentic means doing things for the user — creating plans that can be approved and executed and then verified. Next we'll show two videos: one of Flow in a "Know Me" scenario from an employee perspective, and one of SkyView analytics from a manager's perspective. David, if you wouldn't mind, let's go to the video. Okay. I'm so glad everybody got to see that — it excites me every time. This is how people want to work: conversational and frictionless. Now I'll discuss Events, our newest module. Right now the enterprise experience for managing workplace events looks like chaos. I'm not referring to massive public events but the everyday events at the workplace: all-hands, sales kickoffs, training events. Admins managing these events get calendar invites, e-mails, tickets into catering systems, requests to security and AV, and they're managing multiple locations with specific requirements. The event changes, items keep piling up, and it's stressful for admins — one person described their role as "I am the integration layer." There's no single system that brings it all together. That's our opportunity. We have robust rules, the user interface and end-to-end integration; we are far ahead in this space. Today the state is high risk — things must happen on time, like a wedding: there must be food, a room, AV, executives and maybe external customers. We've built a workflow that orchestrates everything: the user requests what's needed, sees availability, requests approvals, and the system sends automated workflows to departments for approval. One system wraps it all up. This is exciting because it's an add-on for existing customers and a stand-alone product to market. We're launching demand campaigns and leveraging EngineRoom's capability to promote it. The enterprise market has proven our model and product-market fit. Now the mid-market is where we can scale. We're not leaving enterprise; we will continue to serve large clients. But we've productized the enterprise lessons and can rapidly provision mid-market clients with pre-configured connectors and plug-and-play capabilities. We also have resellers and marketplace partners so customers can buy through the marketplace and get fast time to value. Our operating principles: we now have one P&L and a unified set of metrics for the Board and shareholders — EBITDA, bookings, revenue, growth and scale. We must deploy and drive adoption. Events is a universally felt problem and will drive upsells, standalone product revenue and pipeline. We also have the new Beat productivity tool that will apply across enterprise and mid-market and drive expansion. Combined by Q4 we expect a new cohort of recurring revenue from customers we don't have today. In short, two engines have become one: the enterprise engine and the EngineRoom mid-market engine together create scale and leverage our Sky platform. Now I'll turn it over to Adam.
Thanks, Chris. Hello from Australia. I'm Adam Laurie, co-founder of EngineRoom and General Manager of Sky's Australian operations. EngineRoom has been a major part of my life for over 13 years, and I'm excited to introduce it to CXAI shareholders. Our purpose is to use data, digital and AI effectively to enable smarter decisions and unlock greater growth potential for our clients. We are revenue generators and a profitable business. We are established in the Australasian market, founded in 2013, multi-award-winning across performance and innovation, and built around our people. What we do is deliver fully integrated growth marketing solutions designed to capture high-intent demand and drive sustainable, profitable growth for our clients. We have three major divisions: our Martech platform, fractional CMO services, and Marketing-as-a-Service. The problem we solve is that businesses have difficulty building cost-effective, scalable customer acquisition engines. Without customers, businesses struggle to grow. Clients have trouble measuring marketing ROI, their data is disconnected and doesn’t communicate, and they struggle to convert knowledge, data and AI into a commercial advantage. We create growth marketing solutions that transform strategy into measurable, scalable and profitable outcomes. We focus on the intent market — people who are actively looking to buy — because it is the most profitable and measurable part of the market. Why customers choose EngineRoom: proven results over 13 years, cutting-edge technology, measurable ROI, an end-to-end solution and unmatched in-house expertise. Our Martech platform empowers marketers, owners and advisers to make data-driven decisions to optimize growth. The platform has three linked parts: strategize (set direction and objectives), analyze (measure performance, maximize opportunities, identify risks) and optimize (recommend actions to drive improvement and commercial advantage). Each module has a specific application and can be mixed and matched when engaging clients. How we use AI: large language models understand language, but the key is teaching AI to understand the business. We bring in the business context — goals, brand identity, customers, target markets, competitors and performance data — to form a high-quality knowledge foundation. Generic tools provide generic advice; we ground the AI in client-specific knowledge for higher-quality, actionable insights. This is a continuous learning application: we store data and month over month the output improves. The result is reasoning plus time-based learning that produces high-quality actions specific to a client's needs. We demonstrated an example of the platform with a smaller client who kindly allowed us to share their data; David then presented the modules. I hope everyone enjoyed that. Why EngineRoom and Sky? CXAI's Australasian growth engine targets customers historically in the 5 to 500 employee range. Our average client yield is around $200,000 per annum. We have 93% recurring revenue and an average client life beyond four years. We serve diversified industries including professional services, home services, manufacturing and industrial sectors. We have a proven track record of scalable, profitable growth, an experienced leadership team staying on board, and specialist capabilities in technology, AI, data engineering and growth marketing. The Australasian market has enormous growth opportunity. Although EngineRoom currently turns over about $8 million, the addressable market and growth potential in Australia are significant. Next steps are to accelerate EngineRoom with Sky by combining EngineRoom's expertise and customer relationships with Sky's agentic AI capabilities. We will speed product development and expand data and intelligence capabilities. EngineRoom's business knowledge plus CXAI's agentic AI can improve intelligence, reasoning and action outcomes. In our development architecture we have established a data source of truth and business knowledge, and we have started building reasoning and decision intelligence. The future roadmap is to strengthen reasoning and decision intelligence and then take steps toward autonomous business execution with agents, which is a big opportunity. Commercially, we are positioned to expand into new verticals, grow through partnerships and channels, innovate to increase customer value and lifetime value, use AI to drive efficiencies, and strengthen competitive differentiation. The capacity for growth is enormous if we execute well.
So I just want to say thank you. Lovely to meet everyone today and introduce the EngineRoom story. We're excited about the next steps. I'll hand it over next to Zoe, who we'll do a fireside chat with.
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 — not the strategy-deck version but the actual implementation. That's the part where someone has to tell 300 people they're losing their assigned seats and will be sharing desks. What's interesting now is that everyone in the building is talking about AI, even when the project is about physical space. I want to give you three things I think are true right now. First: automation historically started at the bottom and worked its way up — assembly lines, ATMs, self-checkout, warehouse scanners. Machines were good at repetitive physical tasks and bad at everything else. The safe advice was to get more education and move away from repetitive tasks. That worked for about 40 years, and then this wave of technology changed the pattern by automating tasks like writing, analyzing, summarizing and coding — things that took humans a long time to learn. Meanwhile, tasks that a child does intuitively — like picking up an oddly shaped object — remain very hard for machines. There's good data on this. Anthropic publishes an Economic Index analyzing millions of real conversations with their AI, mapping usage against occupational data. It shows AI adoption concentrated in mid- to high-wage information work. Jobs requiring physical dexterity like shampooers or obstetricians have low AI usage; AI is impacting information work heavily. About half of all jobs already have at least a quarter of their tasks touched by AI. The flip side is many trades and manual jobs have near-zero exposure to current AI capabilities. But being protected from AI isn't the same as being unconstrained. Think about a three-truck plumbing company: what's stopping it from becoming a ten-truck business? Not the plumbing skills but the back office: quotes, follow-ups, reviews, scheduling and admin. There's plenty of software, but it's fragmented: a tool for quoting, a tool for scheduling, a tool for reviews. That fragmentation requires someone to set things up and maintain them. Owners didn't become CRM administrators. For 20 years the choice for many small businesses has been stay small or spend evenings learning software. This wave of AI feels different because it promises outcomes without the operational burden. Small operators could get back-office capabilities that used to require scale. Second: personalized intelligence is already part of our lives — phones sort e-mails, news apps surface relevant stories, grocery apps suggest items, maps reroute us. None of it feels like technology; it's just working. When you go to the office, it stops: apps don't talk to each other, the building doesn't know you're in it, the room booking system doesn't know your team is in that day, your calendar doesn't know you're across campus. This costs small but frequent decisions — finding a room, time zone math, who is actually in today — 15-second tasks that add up and degrade focus. People want fewer decisions made for them, the way GPS removes path decisions and just gets you to your destination. Work always lags behind life: phones, video calls — the changes people adopt on their own eventually become expectations at work. Right now, there's a gap between what people use personally and what's available at work. Third: some research indicates AI pilots don't show measurable returns within short windows, and people interpret that as the technology not working. The MIT study looked at whether pilots moved P&L within about six months, which is often too short for sales and marketing cycles. Within the same study, when companies brought in a specialist to deploy, the project reached production about two-thirds of the time; when they built internally, about one-third. That's twice the success rate. The gap isn't the models — everyone has access to similar models — it's execution. Specialists have done the integration, permissions and governance work many times. Internal teams juggle multiple priorities and underestimate the plumbing: data access, edge cases, and governance. The result is pilot purgatory. This is encouraging because it's an execution problem, not a technology limit. It's solvable by teams that have done it before. One caveat: buying instead of building isn't universal. If you have proprietary data, genuinely unusual workflows, or are in a highly regulated situation, building may be right. It's not the default anymore. So three things: the pressure landed on information work, which means this wave helps businesses that need it most; personalized intelligence is common in life but not at work, and that gap is the opportunity; and the technology is proven — the challenge is execution. I'm happy to get into details.
Yes, and thank you, Zoe. That's great context. Zoe is out there talking to customers and seeing real adoption patterns. These are not just theoretical trends; they're real behavior in organizations. What we're going to do now to make this interactive is a short fireside chat. We'll each ask you a question, starting with Adam, who knows trades and home services well.
Thanks, Zoe. My question: trades and home service businesses 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 ultimately better business outcomes?
That's a great question. The data exists: every job a trades business does throws off information about what broke, how it was fixed, how customers are acquired, and which channels work. But it's scattered across systems — scheduling tools, text threads, invoices, Post-it notes, and people's heads. The owner often has to be the one to pull it together and often doesn't have the bandwidth. The patterns are there: which jobs truly make money after counting drive time and callbacks, which estimates are consistently wrong and by how much, which work to prioritize and which to decline. The opportunity is showing a business what it already knows but has never been able to see in one place and making it actionable on Monday morning. The goal is for that insight to become shared understanding across the team, not something that rests only with the owner. That's where AI can unlock scale for these businesses.
Thanks, Zoe. I appreciate that perspective.
Yes — great points. As someone who has spent 20 years working on maps and indoor maps, you struck a chord with me. Driving to a destination is easy now — you don't care about the map; you just want to arrive. The blue dot, the center-of-the-universe experience is seamless. When you come to the building, that often disappears and you become the integration layer again. You quantified the coordination tax and the overhead of the small tasks that degrade productivity. If the building knew you as well as your phone, and we eliminated that coordination friction, what would that mean for business outcomes and human experience? Any thoughts from the research perspective about the extent of that impact?
The top thing that jumps out is decision capacity. Humans have a finite pool of high-quality decisions per day. A lot of that bandwidth is wasted on small, repetitive decisions. Freeing up that cognitive reserve allows people to focus on synthesis, pattern recognition, creativity and relationship-building. Another important impact is the mental space to build connections and relationships. Hybrid work and digital tools have helped distributed work, but when people are in the same physical space, what matters is having the mental capacity to have real conversations, build community and learn together. If AI reduces the low-value decision load, people can be more present and build meaningful connections, which benefits culture and collaboration.
You just made me think about a thought experiment: if we traded ten or twenty minutes a day of coordination friction for high-value moments like meaningful conversation or strategic collaboration, what would that mean? It's subjective, but the potential outcomes — culture, connection and strategic work — could be significant.
That's a fascinating conversation. Zoe, your last point was on deployment. I want to add a different angle: younger workers today are often AI-savvy and have been coding or using AI tools for years. How does this affect scalable deployment in enterprises? New models and tools appear every day. How do you see deployment scaling in a way that avoids fragmentation and enables enterprise-wide adoption rather than millions of custom solutions?
From what I see inside companies, adoption matters more than distribution at first. The mid-market moves fastest because there are fewer stakeholders and fewer layers of approval. They often don't have an AI center of excellence, which in some ways is an advantage. The thing that must be true for mass deployment is that it cannot require a dedicated owner or champion and a long project plan. That won't scale in smaller companies. Adoption in that segment spreads the way consumer apps spread: someone uses it, the improvement is visible, and others ask what it is. That kind of viral, visible improvement drives real adoption more than orchestrated rollouts or top-down deployments. For broader enterprise deployment, packaged, low-friction workflows and visible value will matter. The behavioral pattern of visibility and ease of use is the mechanism for scale.
I agree. We see that in enterprise where referrals occur, but this is a more viral set of referrals based on visible benefit. Great point.
On that note, market dynamics have shifted to value-based selling. Mid-market customers demand proof of value quickly. We are seeing 30-day or shorter commitments for many products. People need to see value — and we have to make it easy to deploy and compelling to use. That will drive organic growth and word-of-mouth adoption.
I love the mid-market because they make decisions quickly and focus on outcomes. If something clearly delivers a business outcome — for example, spend $2 to make $10 — they will adopt it. We knew mid-market adoption and speed were advantages and structured EngineRoom accordingly. The model has to provide capacity and enablement because many mid-market customers don't have the internal support layers. We must be that capacity for them, which is built into our business model.
Thank you, Zoe — great discussion. Zoe has a busy schedule and we'll have her back. We'll move to some closing Q&A and then wrap. Khurram has a few questions coming in. Khurram, back to you.
Thanks. A couple of questions came in. First: has the Google partnership helped and how? We've been using Google for cloud infrastructure, and many of our clients are on Google Cloud, which is helpful. 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 and got the Events product built and into production rapidly — within a quarter or less — which demonstrates the partnership in action. Chris, maybe you want to expand on how Google helped with Events?
Yes. It didn't go fast because it was easy; it went fast because we have a lot of proprietary IP and deep experience in workplace orchestration. We understand how workspaces are managed and have integrations into core systems. Working with the Google team and our partners, we glued everything together into a seamless orchestrated workflow. Google helps as a hyperscaler for secure infrastructure and as a Marketplace channel for procurement. When customers can sign up and buy through a marketplace with click-through agreements and streamlined provisioning, time to value shortens dramatically. That's where we can scale rapidly.
Great. Next: what are the major synergies between the legacy Sky business and EngineRoom, and what do you expect go-forward OpEx levels to be quarterly? There's a large opportunity for synergy. We can consolidate cloud infrastructure, leverage shared Google ecosystem relationships, and combine teams in Manila and other locations. As one company, we can share infrastructure, corporate functions and delivery capabilities to drive operating leverage. Many of those realizations will happen over the next six to twelve months, with some impact already visible this quarter. Adam, do you want to talk about OpEx strategy and how EngineRoom approaches operational discipline?
Yes. Our model at EngineRoom has always prioritized profitability and sustainability. We last took capital several years ago and focused on keeping OpEx significantly below revenue so we can invest sustainably. The key now is identifying operational costs we can reduce as a percentage of revenue as we combine organizations. We are already working through those areas and will report progress as we achieve synergies.
Okay. Final question for this section: you've announced several new products — Flow, Beat, Events — what is our competitive moat? For a smaller company like CXAI, how do we create defensibility against others vying in this space? Chris, can you take that?
Yes. We have several moats. First, we've proven the technology at scale in enterprise with complex, secure deployments — thousands of users in rigorous environments. Second, we have proprietary IP and context: deep spatial awareness, workplace orchestration, and integrations. Third, our agentic architecture — BOND and CORTEX — lets us orchestrate multiple agents at a fraction of the cost compared to using large models naively. We can deliver LLM-driven functionality cost-effectively. Fourth, user experience and product-market fit: we solve real operational problems that drive adoption and referrals. Finally, distribution: EngineRoom gives us a mid-market channel and Google Marketplace provides another procurement path. Taken together, IP, integrations, agentic orchestration, cost-effective model usage and distribution create a durable advantage.
Great. Thank you. I want to close with a few remarks. I spent my career around major technology transitions, and I believe agentic AI will be one of the most consequential. I don't mean that lightly. I don't believe winners will simply be companies with the biggest models. Enormous value will be created by companies that understand context and turn it into action. That's what we're building. We started with the workplace and are expanding from place to person to business: Flow for place, Beat for person and EngineRoom for business growth. EngineRoom significantly strengthens business context. We are building the agentic operating layer for how companies work and grow. If you remember only three things from today: Number one, we changed the scale of CXAI. We moved from roughly $4 million annualized revenue at the start of the year to a combined platform with more than $12 million annualized revenue scale, serving enterprise and mid-market customers. Number two, CXAI 2.0 is moving from vision to commercial execution: the platform is in production with deployments, renewals and new multiyear enterprise wins. EngineRoom adds a distribution channel for our AI products. Number three, we have a clear operating priority: profitable growth. The next phase is not simply adding revenue; it's combined double-digit growth with increasing recurring revenue, stronger software mix, operating leverage and disciplined execution. Our directional objective is breakeven in the second half of 2027 and profitable growth beyond that. I would categorize Q2 2026 as the quarter 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 created 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 for your support. We look forward to updating you next quarter in November and plan another investor session near year-end or early 2027. We're excited about CXAI. Thank you, everybody. Operator, you may close the call.