CXApp (CXAI) Q2 2026 earnings call transcript

Good afternoon everyone and thank you for joining us for our Q2 earnings call investor forum. I want to bring, I want to start a little differently today. Q2 fundamentally changed the trajectory of SCI. For the last several years we have been building, 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 a genetic AI platform. Now we're entering this 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 to take out of today's meeting. Number one, Engine Room transforms our scale and gives us something strategically critical, mid-market distribution.

Secondly, Sky 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. And I'm Khoreem Sheikh. I'm the chairman and CEO of CXAI. And with me today, I'll have Chris Wiegand, who's our general manager of North America, talking about our enterprise business and the scale we're getting there. Our newest leader, Adam Laurie, who's the general manager of Australia, previously the managing director of Engine Room, will be with us as well. And my new partner, Melissa Prudzini, who stepped up to be the interim CFO after the transaction.

She was leading the finance function at Engine Room. 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 and AI.

So with that, let me show you the agenda for today. So, 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 530. We'll take an intermission. We won't actually adjourn, but we'll just take a little break. If we end early or we'll transition directly into our investor forum, and the investor forum will really focus on, number one, from my perspective, the vision and what I think about Sky 2.0 and the agendic 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. I'm going to go more detail on the product side there. And then 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 Fireside Chair with Zoe, which hopefully 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. So let's talk about our Q2 earnings. And as I said earlier, the three themes, number one, Engine Room is transformative. Number two, Sky 2.0 is real, is available. And third, we're now seeing a clear operating model for translating growth into operating leverage and ultimately profitable growth. And we at Sky are building the agentic AI operating layer. So before we go into the business numbers, let me talk to you guys about the, just make sure you have the disclaimer slide on what is, you know, what are the forward-looking statements. Make sure you read the Safe Harbor. Please review the Safe Harbor non-GAAP disclosures in today's presentation and our SEC filings for the applicable risk assumptions and reconciliations.

We will be filing the 10Q tomorrow, and so you can read that when you get that. So let me talk about, you know, the company we have today, right? And the company we have today is pretty amazing. You know, we are deployed globally around 200-plus cities with more than 60-plus customers now, supporting a large installed base of users. We operate inside demanding enterprise environments where security, privacy, reliability, integration are not optional. They're necessary. This matters because our AI strategy starts from something valuable, enterprise trust and real operating context. Context is very important We aren 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 And Sky2.0 is about making that context increasingly intelligent and actionable.

So we're headquartered in San Francisco Bay Area. As you know, we have teams in Toronto and Manila. And now we're excited to welcome the Australian team, which is headquartered out of Melbourne, but they're all across Australia as well as New Zealand. We're excited to have them on board, and this gives us the global coverage. You know, we have around 70 team members now globally, and they're all working hard in making AI successful in the enterprise market that we're in.

So before we get to the numbers, let me just give you context of where we're being and where we're going, right? So Sky 1.0 established the Enterprise Foundation. It showed us that we have, you know, great software, 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 business. We made some significant strides in the last two quarters. You know, Chris is going to talk about those customer case studies and stuff, but it's been amazing there. But Sky 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 Engine Room into business, how companies acquire customers, convert demand, and grow. That business context is significantly strengthened by Engine Room, as you know. And underneath those experiences is the same Sky Agenda platform. The strategy for me here is very straightforward. Proven enterprise technology, mid-market distribution, prioritized AI, and scale recurring revenue. We're going to run that flywheel cycle because we've got now an agentic platform that we can leverage across multiple verticals. And more importantly, we now have a new distribution mechanism through Engine Room. So this is the transformation I'm talking about. This is what we're executing on, and we're super excited about this opportunity.

So let me go into the business for this quarter and what happened this quarter. So this is a pretty exciting time for CXCI or Sky. As you can see on our highlights for the quarter, there are six main highlights, but the biggest one is the engine room transaction. It is transformative. I'm going to talk more about it in detail, but it really did change the revenue trajectory for the company.

And more importantly, quarter over quarter increased approximately 79% revenue growth from $950,000 in Q1 to approximately $1.7 million in Q2. The more important thing is what sits underneath that growth. Enterprise retention remains strong. Two major Fortune 500 customers renewed their relationship with Sky. In enterprise software, renewals matter enormously because they validate the product after the initial sale. So customers that continue 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. It 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, and it's a really, really important win for the team.

And it's won for two reasons. First, it demonstrates continued demand from highly sophisticated regulated customers. Secondly, these are the types of customers where Sky 2.0 can expand over time across additional modules, users, and AI capabilities.

The other big achievement for this quarter is we moved Sky 2.0 into deployment, and that's a big win for us. And the progression is win, deploy, adopt, expand exactly what we want to replicate. And with 2.0, really, what we get is an agentic air platform that allows a user to, you know, navigate their workplace, navigate their work, and navigate their experiences across the whole enterprise. And that's very exciting. And our customers, the reason why they're selecting us is because we have Sky 2.0. That's the wins we got. That's the renewals we got are all because of Sky 2.0. And, you know, during the quarter, we complete the engine room transaction. So for Q2, we only have one month because it was the month of June that engine room is part of the revenue.

And it's been an amazing one month because they've continued to get new clients. They've got double-digit growth. They're going through this annuals process where they've got commitments from existing clients. So it's been really good. And so all of these six factors combined really have been super successful for the company I want to congratulate the team on the job well done and it builds the momentum It strengthens the foundation for Sky 2 and our scale growth moving forward So let me tell you, you know, a little bit about Engine Room, and what better than just to roll a video. So operator, if you can roll the video.

I'm going to turn the call over to management for a while. All right, cool. That's pretty exciting. So when I talk about Engine Room, I talk about it being transformative. And as you can see from the video, it's pretty exciting stuff to do, and they've been out of the way. They've been working for 13 years, and they've made amazing progress in getting clients and making sure that they have really solid footing.

So let me tell you why this is transformative. 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 even more for me. Number one, distribution. Sky historically sold into large enterprises through an enterprise sales process. Engine Room gives us a structured relationship with dozens of mid-market businesses. That gives us a much faster proving ground and future distribution challenge for Sky AI products.

Number two, business context. As I said earlier, Sky already understands workplace and employee context, and that's one of our modes in differentiation. EngineRoom brings customer acquisition, performance, marketing, and growth data. That allows Sky 2.0 to expand from understanding how people work to understanding how businesses grow. Number three, cross-sell. We can introduce Sky capabilities into Engine Room's customer base, and we can introduce Engine Room's growth capabilities into Sky's enterprise products. So the combined company has an enterprise anchor, a mid-market growth engine, and a shared agentic AI platform. And the combination moves Sky to more than $12 million of analyzed revenue scale. This acquisition created scale. Our job now is to turn that scale into operating leverage.

So I'm super excited about this. I think this is the right move for the company. It positions us ready for the growth engine that we've been talking about, the double-digit growth. It gives us that flexibility in terms of having the ability to innovate in a very interesting market, Australia. I'll talk more about that in the investor forum. We will go more deeper into it. But I just want to share this story with you and share with you that this has been an amazing, amazing transaction for us.

So with that, I want to move on to the financials for Q2. You know, 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, Kram. So, the quarter over quarter comparisons demonstrate that steps change is taking place in the business. Between Q1 and Q2, revenue has increased approximately $950,000 in Q1 to $1.7 million in Q2, representing, as Krem previously mentioned, a 79%. Our annual recurring revenue has increased from $3.6 million to $11.5 million. One second, we've jumped. Let me just find that slide again Sorry about that Net revenue retention has increased from approximately 98 to 99 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 quarter one to negative $2.68 million in quarter two.

EPS was approximately negative 0.10 compared with negative 0.09 in Q1. So the key takeaway quarter over quarter is that 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 increases from $4.5 million to again the $11.5 million, an increase of approximately 156%. Net revenue retention increased by more than 5 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 a neutral position compared to a year ago.

And 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 therefore the scale of the recurring revenue base, while we continue to manage investments required to support integration and future growth.

And now let me put this cost structure into perspective. Total operating expenses increased approximately $275,000 quarter over quarter, or 5.6%. However, we do need to compare that with the approximately 79% sequential revenue growth. The increase in operating costs was driven primarily by the engine room 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. And I'll turn it back to Karam now.

Karmie, you're muted.

Thank you, Melissa. I apologize I was on mute. This was a really great quarter. As you can see, we are finally showing the value of our technology platform, but also the engineering acquisition. But I want to put in perspective what I see the value of this company as we move forward. And this is the valuation is based on numbers that we get from KeyBank, which does a monthly survey on software benchmarks and looks at all the recurring revenue-based businesses and software businesses. So as I think of our business now, it is an AI-powered software business that is at a much larger scale. And the scale is, you know, last quarter we were a million a quarter.

This quarter we are now 1.7 million a quarter. And with the full engine room integration, we'll be hitting 3 million a quarter or 12 million analyzed by next quarter. And that shows real growth as well as shows real momentum and scale. And based on that, when you think about that business and you think about that software business with the metrics we have, you know, just on a conservative basis, it's a 9.7x multiple, right? That's more on next 12 months revenue. I'm just saying that revenue we have now. We will have now by Q3. So to me, we're at a very attractive, strong, Price right now, given where we're at in terms of the valuation that we should command, I do believe that we will continue to perform, and given our double-digit growth strategy, we believe by, you know, second half of 2027, we will be, you know, growing and getting to the breakeven point, and that's where our focus is.

Our focus is really to get to that level, and you can see the metrics based on that. This is all illustrated, by the way. This is not a valuation guidance. I'm just taking industry benchmarks and showing you what the value of this company is and the fact that we've now built that agenda-to-gap platform, that R&D expense has been done, and now it's about growth and distribution. And this is where we did the engineering transaction, and this is where we feel very strongly about the growth and scale of the business.

You know, can we sustain this growth? Can we increase software mix? Can we translate into greater revenue scale? Absolutely. And that's where, you know, the two businesses have been complementary, but we're going to help each other scale up faster.

Now, let me talk about, you know, probably the most important, one of the most important charts in the slide deck here is about the path to break even, right? And again, this is a directional operating framework, not specific financial guidance.

The engine room acquisition gives us a combined revenue of more than $12 million. From here, there are several identifiable levers. First, organic growth. Continue expanding the Sky Enterprise business and Engine Room's customer base. That's obvious. Second is cross-sell. Introduce additional Sky modules into existing enterprise customers. Chris is going to talk a lot about that today. Number two is introduce Sky Agenda KI products into the Engine Room's mid-market customer relationship. That's the second cross-sell that we think is very important. Thirdly, increase software monetization. We'll talk about flow, we'll talk about analytics, events, and our emerging personal execution capabilities called BEAT. All increase our opportunity to generate recurring software revenue from the same platform. And fourth, prioritize that mid-market motion.

For mid-market customers, we don't want to recreate a long enterprise implementation. Our objective is to standardize the products, standardize the connectors, faster provisioning, and lower cost to serve. And fifth, operating leverage. We now have opportunities to share infrastructure, technology, corporate functions, and delivery capabilities across a much larger revenue base. That's the synergy that Melissa talked about. The operating model we are working towards is characterized by, number one, double-digit revenue growth, which is part of our strategy as well as what Engine Room is already on. Number two, 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, which is really great.

And the discipline expense growth, which now we can do with the larger scale. If we execute against those levers, we believe there's a credible path towards break-even in the second half of 2027, followed by profitable growth.

So I want to now close with why Sky? Why do you want to continue to invest and be part of this journey?

Why is this moment different for Sky? Why? Reason number one, engine room is transformative. It immediately increases our revenue scale. It gives us profitable operating capability, and perhaps more importantly, it gives Sky a mid-market distribution engine that we did not previously have. Reason number two, Sky 2.0 is now in production. This is no longer simply a roadmap or an AI narrative. We are ready now to deploy this across our clients. We've been successful in the demonstrations and prototypes and getting it through our clients. They're doing a lot of validation, but now it is launching. It is launching live with a new client. It's also launching live with existing clients. And now we have these new enterprise logos signing multi-year agreements.

They would not be signing multi-year agreements with us unless they knew that the roadmap and the product we have is going to be long-lasting and for the future. And we are expanding the platform from workplace intelligence to personal execution, which is the beat, as well as the growth intelligence, which is engine room. And reason number three, the financial model is becoming more scalable. Q2 revenue increased approximately 79 sequentially while operating expense increased approximately 5 That does not mean the work is finished Far from it But it demonstrates the opportunity for operating leverage as we integrate the businesses grow recurring revenue and prioritize more of what we offer So with that, I'm going to look into some questions that have come in.

Let me see. Okay. Question number one. Good question. How much cash do you have? What are your liabilities after the purchase of engine room? I'm going to have Melissa take that. Thanks, Kram. So our cash as of 30 June 2026 is $11.7 million.

Most importantly, the acquisition costs have been acquisition costs related to engine room have been largely paid. And any subsequent funds owing on the acquisition of Engine Room are tied to an earn-out model.

No, that's good. So I think just to be clear, you know, the Engine Room acquisition was 65% cash and the rest was in earn-out. So the team is focused on it. It's a two-year earn-out with growth factors in revenue specifically. So that's going to earn out itself. So we have no other liabilities on engine room except the earn out. And overall, as you can see, the asset base has increased and it has been a very successful integration up to now. So, okay. Next question I see is how quickly should shareholders expect the engine room acquisition to be reflected in Sky's report revenue? Mel, you want to take that?

Yep, so we've actually already captured one month of combined revenues, that being the month of June. We will be able to demonstrate next quarter, so Q3, the full combined impact over the three months of that acquisition and the combined revenue. Thank you. Yeah. Okay. I think the next question was one more question. What do you think of the revenue growth over the next 12 months? So look, I gave you some illustrative graph on our potential. As you know, we are focused on double-digit growth. We really believe that the scale we're getting with Engine Room, the wins we have with our existing enterprise business and new logos coming in that are multi-year, multi-million dollar contract, we're pretty positive on that.

And we're also positive on Engine Room because they've also increased their revenue profile, the number of clients, and their annuals process has been super successful.

So anyway, we are pretty positive on that. I think our goal is, like I said, is to have breakeven by second half of 2027. If we execute our plans that we have and the growth vectors that we're working on, I'm pretty confident in that. And we're also focused on expense management. And, you know, with Agentec AI, we're leveraging AI across our enterprise. All our functions are using AI. So we're seeing a lot of efficiency there, as you can see, in terms of our team members. We're very cost efficient, and so I'm pretty positive that in the next 12 months, we will achieve much higher growth, and we will get to the breakeven target that we have.

Okay, we're running to the end of the call here, so thank you, everybody. I appreciate it. We're going to take a brief pause, and we'll join you back in 60 seconds or so for the Investor Forum. Thank you.

Thank you. I will now turn the call 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 to you about the products, the business, the customers, the things underneath the hood that we're working on, and show you the path that we believe is going to be super successful for Sky. And so I think we shared the agenda before. I'm going to start off with the strategic view of the business and more strategically thinking about where we're at, where we're going, and then the team is going to tell you how we're doing, what we're doing, what we plan to do next, and then we'll end up with a very interesting fireside chat.

So thank you for being here, and we wish to do this next time live in person, but we're going to do our best to do these demonstrations and these discussions on the webcast. So number one, I want to start with something I believe very strongly, and I've been involved in lots of technology transitions. I was involved in the first mobile phone. I was involved in the first 4G network. I was involved in the first Wi-Fi systems, and then I was involved in 5G and cloud and all this interesting technology developments that have happened.

And I believe we are at the beginning of another major technology transition in enterprise software. And what I mean by that is, you know, Sky started by solving a very real problem, how people interact with the workplace. And that happened after the pandemic, as you know, COVID kind of created this hybrid environment. But we have built underneath that experience is becoming much bigger than a workplace application.

We have enterprise integrations. We have proprietary contacts. We have AI orchestration. We have data. And we have security and trust. And now we're bringing all these assets together into Sky 2.0, our agentic AI operating layer. So our strategy has three priorities. Number one, reposition sky around this agentic operating layer, which I talked about as a context layer.

Second, use engine room to give us immediate scale, mid-market distribution, and a much larger customer base. And thirdly, is prioritize what we learn into repeatable vertical AI solutions. This is not simply an evolution of our product. I believe it can be an evolution of the company. So we're going to talk about that. We're going to go through some real strategic focus on why we're doing this. So, you know, let me explain why the timing is important in the industry right now. You know, enterprise software is evolving as we speak. The first generation of enterprise software created systems of record. Then SaaS and analytics gave us applications, dashboards, and visibility. But visibility is no longer enough.

The next generation of enterprise software is about action, about, you know, getting stuff done, basically. AI agents will increasingly understand context, make recommendations, coordinate workflows, and actually complete outcomes. And that is the layer I want Sky to own. Not another chatbot, not another dashboard, not another AI feature added onto an application. The operating layer between the enterprise systems, its data, its 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 having to assemble teams of AI engineers to build it themselves. This is the opportunity that we are designing Skyrun. So we're building the agending AI layer for enterprises to work and grow, and that is our focus.

Let me put it into a little bit more detail and show you what I mean by that, right? This slide shows me where we came from and where we're going. We started with place. Skyflow understands where and how people work. Workplaces, spaces, resources, presence, and experiences, and maps, and locations. 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? How am I going to become more productive? And eventually what can the platform safely do for me In an enterprise you want to be in a secure environment You want to be able to get your stuff done Now we adding business through Engine Room How does a company find customers How does it convert demand Where is the marketing working Where is it not working What is the revenue being lost All those questions need to be answered And what action should happen next to grow the business So think about what we assembling Place gives us workplace context.

Beat gives us personal and team context. Engine room gives us customer and growth context. And underneath all these three is one shared Sky platform. It senses, it prioritizes, it acts, it verifies, and critically, it learns. And also, it gets it done. We're about the outcome business. We're about context should not just another insight. Context should lead to an outcome. And this is what we're focused on in really creating those amazing outcomes for our clients.

So next, I'm going to talk about the market, right? So, you know, this is studies taken from one of the vendors, the Grandview Research. And, you know, you look at the markets running this trend, it's amazing. We're participating in three large categories that are all growing. Digital workplace platforms, enterprise agenda, KI, and now marketing automation and growth intelligence. The individual market growth shown here are significant, but the bigger the number is the compounding effect. It's the 75X plus compounding effect of growth over the next couple of years into 2030. It is not claimed that our addressable market sometimes becomes 75%, but the opportunity for us is 75X.

So we're not exposed to only one category now. We're exposed to three categories. We sit in the intersection of workplace intelligence, agentic AI, and business growth intelligence. And my conviction is that the intersection matters because enterprises don't ultimately buy AI because AI is interesting. They buy it to make employees more productive, make better decisions, reduce costs, and grow revenue. These are precisely the outcomes that these three businesses allow us to attack together.

So let me talk about, you know, what are we building, right? And we spend a lot of energy and time with our Silicon Valley team, and as we integrate our folks in Australia, they've also been thinking about it. And the reality is all great minds think together, and we've had a great interaction with our teams. You know, this gets to the heart of what we're building.

Across the top, you see the context domains, the work intelligence, the place in person, the growth intelligence, the business, and the future verticals that we can add over time. But I want you to focus on what's underneath. This is the Sky Agenda platform. The philosophy is simple. Understand the context, recommend the action, get the right approval, complete the outcome. As I mentioned in previous events, Bond is our agentic engine. Bond is a multimodal, multi-agent orchestration system that provides the agentic execution capability. Cortex provides intelligence, context, knowledge graphs, personalization, analytics, and we surround that with the requirements that enterprises actually care about. They care about identity. They care about auditability. They care about human control. They care about connectors.

They care about governance. They care about all the things that are important to make an enterprise successful, and that's why we have designed this for the enterprise. We've designed it with all those controls, and we've also integrated with all our partners, all our cloud partners. As you know, we have a strong relation with Google Cloud, also partnered with AWS, and we have also one of our clients using Azure. So we are multi-cloud. We have access to all their models, all their information, and we're using the best-in-class technology to deliver this agentic operating layer. And the most important part of that discussion is we're not betting on Sky on one foundational AI model.

Models will change. Models will get cheaper. Models will become more powerful. Our value is the enterprise context, orchestration, permissions, actions, and outcomes layer from these models. That's why I call it the operating layer. The model can provide intelligence. Sky makes that intelligence useful inside the enterprise. We are the outcomes. We are the actions. And that's what we're focused on. Another key part of this, as we looked at Engine Room and other opportunities to partner with folks is that this architecture that we built gives us tremendous leverage. We don't have to build a completely different technology stack every time we enter a new use case. The same orchestration looks like They can support workplace agents, can support growth agents, analytics agents, automation agents, and eventually industry-specific agents.

A meeting agent and an attribution agent may solve way different customer problems, but underneath, they need many of the same capabilities. They need the data, they need the context, they need the permissions, they need the workflow orchestration, they need auditability, and the ability to complete work in the system where that work belongs. That is where I believe the leverage comes from. One platform, many specialized agents, real business outcomes.

And the more repeatable those agents become, the more efficiently we can take them to the mid-market. So that's where we're really focused on is we built a really strong technology architecture, and now we're looking for, with that amazing product, we're looking for the distribution model. And this is where our new friends at Engine Room come in. So, as I think about Engine Room, I think I mentioned in the earnings call, but I want to reiterate, it is strategically super important for us. It is simply not an acquisition that added revenue. Engine Room changes how we can take Sky to market. Sky gives us the enterprise anchor, technology proven in complex environments. Engine Room gives us a mid-market customer base, recurring revenue, commercial data, and people who understand how to drive measurable business outcomes.

And Australia gives us an excellent launchpad. We can launch, learn, and scale. We can work directly with businesses in trades and fuel services, construction, automotive, healthcare, professional services, and manufacturing. These are incredibly important parts of the real economy. A plumber doesn't need another chatbot. A construction company doesn't need another AI demonstration. A healthcare operator doesn't need another dashboard. They're very practical folks. They need more customers. They need faster response. They need better scheduling. They need lower acquisition costs. They need higher employee productivity. They need better visibility in what is driving grand revenue. This is where practical vertical AI becomes incredibly powerful. Engine Room gives us more than 50 customer relationships and a recurring revenue foundation.

Our objective is to identify the workflows and repeatedly create value, prioritize them on the Sky platform, and distribute them more broadly. Services help us discover the problem. Software gives us the skill. So this is why I'm super excited about Engine Room. It's getting us a head start into the mid-market strategy that we've had. We're working closely with them this quarter to start identifying customers. They've already been a lot of great interest. And as we get flow and beat and events and other products that Chris is going to go through, there's a huge opportunity to leverage that channel. All right. So next, let me talk about a little bit more detail about the combined platform, right?

You know, all of these strategies and these product visions ultimately have to translate into economics. And I'm going to go a little bit more deeper. I know I went a little bit on the earnings call, but the acquisition gave us the scale. It gave us that volume in revenue. Now, Sky 2.0 has to give us operating leverage. Today, we have that combined revenue north of $12 million. We have enterprise customers on the sky side. We're growing those customers. We have those 50 mid-market relationships to engine room. We have recurring revenue. We have data. We have distribution. The next phase is about pulling four levers. Number one lever is grow and expand existing businesses. And both businesses are, you know, really working really well.

The enterprise business is expanding, and engine room customer growth is happening. Secondly, cross-sell and distribute across the combined customer. That's job one, and we're doing that right now as we speak. Third is the exciting part of prioritizing the AI in new modules, increasing the software component of our revenue, which is already 95%, but now growing it in the new AI economy. And fourth, creating the operating leverage as the company scales. And we talked about it's already starting to show the signs in Q2 here. However, this ultimately means that revenue should grow faster than the infrastructure required to support it.

Our ambition is very clear. Higher recurring revenue, higher software mix, higher gross margins, more revenue per customer, and a path to a break-even and then profitable growth. So the acquisition created the scale. The platform has to create the leverage. And that what we focused on executing And I pretty excited that this is a path that we on As I said on the earnings call we have all those metrics We diligently working on them as part of the integration You know I showing some directional synergy targets the team has We already realizing some of those synergy targets in the second half this year Next year, we believe there will be more, not only on cost synergy, but also revenue synergy.

And then finally, the flywheel effect with the Sky, you know, Sky 2.0 product will really implement much higher growth factors here.

So all of this is great plans, but none of it really happens unless I have a great team to execute. I'm super excited to introduce some of the team members here. We're working on a very lean operating model. You know, Chris is running, you know, enterprise business in North America for us. Adam has taken on Australasia as well as the engineering business, and then Melissa stepped up as being our interim CFO.

We're also proud to have a global CTO team that brings together technical leadership across Silicon Valley, Canada, Australia, and Southeast Asia, and that is important. AI innovation is global. Our customers are global. Our engineering capabilities should be global as well. This structure is designed around speed, accountability, and execution. We don't want any unnecessary organizational areas. We want talented people close to the customers globally, close to technology, and close to the results. So that's where we believe, you know, we have a huge opportunity. And so anyway, I'm super excited to introduce the team. So 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, Karim, and welcome all. I'm Chris Wiegand, and I'm general manager of North America. I'll let you know, I am an entrepreneur at heart, and I couldn't tell you how excited I am about this whole AI transformation. It's truly changing things, as I'm sure you have in your personal life, but especially in the workplaces. And what's also exciting, just on the tail of that, 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 I want to tell you about in our events. And then Kerm also told you a little bit about the new product, Beat. So, Kurt's been telling you all about strategy and what our vision is. What I want to do for the next 20 minutes, I'm going to tell you through what's happening on the ground. How are we doing our business? What are the customers all about? And most importantly, we've got some live videos that we've recorded to show you the product itself. And then we're going to go through and talk about what's the rest of the year look and how are we going to do it.

Okay, so what I really want to emphasize here is, you know, there's not names on the page, but these are the largest and biggest companies in the world, some of them. They're leaders in their space. And I'll tell you this, they have gone through super diligence, we'll call it. These are some of the toughest RFPs and diligence processes you can go through, and that's how it should be, right? We're working with very secure, complex environments. And so by these customers doing this, they've really gone out to market. They have the resources they can choose whoever they want to. And by sandbox trials, by many, many questions and answers and meetings, we've come out on top.

So that's the pattern I really want you to know is that we keep winning in the regulated environments, the enterprise environments. And these are, again, some of the toughest places to get your products deployed. So I'll take you through, you know, sort of from left to right here. One of our biggest wins this year is a top financial institution. They are actually global. They've got dozens of sites around the world, thousands of users that are going to be coming online. This is actually more than a 12-month pursuit. And again, they went out to market and Sky came it on top.

Next, we have a global asset manager. So we're closing this right now. They're starting at the end of this year. So there's going to be a quick turn on our implementation. Again, five-figure number of users, very similar use cases in terms of what they're doing. And again, they're starting with an entire population that we're going to go live with. Next is a leading U. S. insurer, and I'm going to spend a little bit more time on this because it's going to tell you about how we deploy.

This is an interesting customer for many reasons. One, it's really our bridge to the mid-market. So it's a few thousand users. We're still in an enterprise environment, but this is how we really took our product and learned how are we going to productize this so we're configuring, not making custom code. And I'm going to talk more about that. And they're going to go live in September. They got a brand new headquarters and we in deployment testing for that right now And let not forget our amazing install base We got enterprise customers today that are choosing to stay with us Same rigorous environments We had one of our largest financial services customers renew We had our largest media and entertainment company They renewed and expanded And so where is this all going This is all leading to more revenue That the goal And so, you know, not giving you guidance, but directionally, the new customers are coming online, the things that we're not even seeing yet in terms of revenue, it's about a third uplift.

So that's pretty significant when you think about it. And in addition to the other things I'm going to talk you through here.

All right. So validation is really the key here. You can go win these deals, but you have to deliver them. And that's really what our customers are expecting is exactly what we're doing. So this is the company. I'm going to take you through sort of a quick timeline of how we're delivering for this leading U. S. insurer. And again, it's a brand new headquarters for them. So it starts out, we have an enterprise agreement. And this is very detailed scope of work. We understand exactly what we're delivering, how we're going to do it, and then we go and build it.

So with that, we have integrations into their core systems. Remember, the whole value of what we do here in Flow Sky is we're taking disparate systems and we're putting it all into one cohesive system that an employee can just get what they need quickly and easily, and you're going to see that in the demo. Now we're in testing. So we deployed 2.0 to them. We've got most of the integrations done with a brand new headquarters. As you can imagine, there's things that are staged. So right in the next 30 days, we've got really important next steps in terms of finalizing testing. But I can tell you the client is extremely happy. We're right on schedule.

And then we're going to scale. And so that goes live to the population. And there's a few really important points I want you to remember here. However, this is our point that we've really transitioned from custom code. This is not, you know, custom development for a customer. This is configuration, which means it goes faster.

Okay, so this is how we're going to deploy to the mid-market as well. We've also, if you've been on our earnings calls before, we've talked about how we're moving away from the big one-time upfront fees and campus fees to per-user software. The market is demanding value-based pricing, and value comes from people using the product. So when we deploy it, we expand it through utilization and through people actually adopting the product. So we've got a common goal with our customers to get everybody using it so they realize value, we realize revenue. And so, you know, again, a key message here is that, you know, we've taken what we've learned in the enterprise, the really complicated environments, and we're taking that into more deployment and into a repeatable, scalable model that I'll talk you through a little bit further here.

Okay, so what I want to also, as I'm going to introduce you to the products, and Kerm has already, I want to highlight a point that, you know, we've proven ourselves in the enterprise, and that's revenue. That's great. But what it's also doing is it's giving us the ability to productize what we've done and go down market. So I'm going to work through, you know, the product that is out there today, Flow, the ones that our customers are using to book spaces, to way find, to interact, to get news.

This is what's out there. That's what's Flow, and we've got this transition now that we've built 2.0 to transition those customers and new customers onto that. All the data that they're creating goes into SkyView. View is our analytics platform. So this is what's not just dashboards, and this is going to be one of the demos as well. You're going to see it's taking that operational data and turning it into insights. That's really exciting.

Next, events module. This is a brand new product. It's coming out in just a few weeks. We'll be GA in September, and we've solved a major industry problem, and I bet you everybody who works in enterprise is going to recognize this right away. I'm not going to talk about it now because I've got a couple minutes. I'm going to just go into more detail on that. And then Karim had also told you about the new product BEAT coming. So BEAT is really this tool that keeps everything on track, starting with the personal level. This is a productivity tool. And I'll tell you that people working in companies today are overwhelmed with the number of messages they're getting.

They've got systems for JIRA, Slack, Teams. They've got things coming at them. There's duplication all over the place. And it becomes anxiety. They don't know what to do next. So we've created a system that keeps everything moving, keeps you prioritized on what's most important next, and also prepares or does the work for you to keep the team moving. So stand by for that one. That's coming in Q4, and we'll certainly tell you all about that as soon as it's live. What I also want to make sure that we recognize is that we got the same agentic platform driving all of this So underneath all of this is the sky agentic platform So this is the thing that going to really as you can see here sense prior like what the proximity what happening what the priority act meaning do something for you That's what Agentic means.

It's not just giving you a message and reminding you. It's actually, in a lot of cases, executing on something. And then verifying. So it may give you a plan and you can approve that plan. So what we're going to do now, we're going to go into two videos. They are live demos of both flow, we'll call this the know me scenario. This is an employee's view of how they're going to go with their day. You're going to hear me talking conversationally with the platform to do several use cases. That's about two or three minutes. And then we're going to transition to the next video, which is view or Skyview analytics. And this is from the manager's perspective.

So they're going to be there asking the strategic and insights that they want to get out of the data that they have. So with that, David, if you wouldn't mind, let's go to the videos and I'll see you all back in about five minutes. This is our Sky Enterprise platform, Flow, and it's what's live in production at some of the largest enterprises in America and around the globe. So one thing to note before we start, the solution is powered by actually years of workplace data, getting analyzed continuously by the platform to drive my employee experience, where I like to sit, who I like to work with, how I use the building. And so all this intelligence you're about to see isn't actually just configured, it's learned.

And this is a key differentiator for Sky. Now, before I jump into the agentic part of this, what we're looking at here is the web desktop version. We'd see the exact same thing on mobile, the way a feature parity between all of them. So no matter where somebody is or what device they're using, they're going to get the same information. And I've configured this to be personalized to me, the things that I want to know about, the things that I want to get to quickly. And all of this is on my dashboard, which each person can configure themselves. So let's jump into the agentic use cases.

And what I love about this is it's totally conversational, just how I like to ask questions and get answers right away. It's doing all the heavy lifting in the back end. So I'll go through a few use cases that are pretty typical in a workplace.

I'm coming into the office tomorrow to meet with Tyler to work on a project. Book me a desk with a charging port near him. Done. I booked desk SRW03 for tomorrow, August 13th. Would you like me to schedule your meeting with Tyler as well? So, notice a couple of things here that I did in the demo. I didn't tell it who Tyler was. I'm meeting with Tyler Robbins, but it knew that because I always work with Tyler, or I'll work with him pretty often. And it was able to search for where is he? Proximity matters. So find a spot that's near him. But it also had another variable. It needed to have a charging port.

So all of this stuff is going on in the background. Let's just see how the system handles it when I want to make a change.

I now need to sit with Dana tomorrow. We're working on an urgent project. Can you change my desk? I've moved you from SRW-03 to CXE-01 for tomorrow. Okay, once again, I didn't say who Dana was. It knows who Dana is because I usually work with her. It went in in the background. It went to the calendar system. It canceled the meeting that I, or the desk that I had already reserved, and it booked me a new one and kept the attribute of having a charging port. So this, again, really demonstrate contextual and spatial awareness. Here's another important question I might ask. How do I get to the men's bathroom from my desk?

It's on the map. From CXE01, go straight ahead, and the men's bathroom is just down the hall on level three. So what we have that's really special and different, I would call this a key differentiator, is the mapping platform fully embedded into the system. So when we need real spatial context or even wayfinding directions, we can do it like this, just as a thumbnail and show you contextually. Or you could start a full wayfinding experience and get turn by turn, like you drive in your car, tells you when to turn left, when to turn right. So it really understands the building and it definitely understands where you are.

And that's the power of SkyFlow. It's constantly learning your behaviors. It's constantly getting smarter and it just becomes more and more personalized. This is why people love to use SkyFlow every day.

So this is our analytics workspace, SkyView, and it's a real workplace. What we're looking at here, what we're going to go through is four campuses above 27,000 employees, and every desk, room, and booking is feeding it live. And you can see this from the numbers. There's about 10,000 active users, 38% desk utilization. It's a nice dashboard, but dashboards don't make decisions. So instead of clicking through reports, I'm just going to go and ask it questions out loud the way a manager actually would.

Show me the weekly active users over the last six months. Adoption is strong and steady. Around 17,000 employees use the Workplace app every week, and it has held there for six straight months, peaking at 18,918.

Okay, so I got there's 17,000 people a week, and it's held for six months. That's real adoption. That's great. But a chart doesn't tell me what to do about it. So let's push on it a little bit. Which floors were consistently underused the last quarter?

Seven floors at CXA HQ sat almost empty last quarter, even at their busiest moment of the day. R&D 2T peaked at just 2% full and HQ 1B at 5%. The rest ran between 10% and 19%. Anything under the 40% line is space that isn't earning its keep. Chart. Want me to dig into why those floors are so quiet?

Before we do that, so seven floors just like that. Notice I didn't tell it where to look. It went into our own operating data and found the problem floors on its own. So obviously the next question is why? What's driving the low utilization on those floors? It's a day of week pattern. Fridays sit nearly empty at 9% occupancy and Mondays are light at 17%, While Tuesday through Thursday carry the campus at 23 to 26 percent. The underuse is concentrated at the start and end of the week, exactly the days a consolidation would reclaim.

Okay, so now we know that Mondays and Fridays are the culprit. That's not a reporting room. Now we know it's a real diagnosis. We've got the data. All right, now the question a CFO would ask. If we consolidated those floors, could the rest be absorbed for the demand we have? Yes, comfortably. The seven near-empty floors carry only about 161 desks of real peak demand, and the healthier floors have roughly 1,180 desks of headroom to take them. You could hand back all seven floors and still leave about 1,000 desks of slack, so nearly half the floors examined read a surplus with no hit-to-employee experience. All right, that's the part that I love. I didn't just say yes.

It checked real peak demand, checked the headroom on the other floors, and made sure employees wouldn't feel it. All in one answers. Okay, you went from, is anyone even using this, to here's what to do about it in just a couple of minutes, just by asking, just in this conversation. That's what we mean by agentic analytics. The company owns data and turns it into decisions.

Okay, I'm so glad everybody got to see that. And I get excited every time I get to demo that. Certainly feels like we've got the hotcakes. I say that because, you know, this is how people want to work. They don't want the friction of having to dive into stuff. And even if you make something super easy and user-friendly, you'd rather ask it through a conversation. So a parking lot that, and I'm going to move on to events. And so this is our newest module. And what we see on the screen here looks like chaos because it is. This is what people are living in the enterprise environments when they're talking about managing events. And I'm not really referring to thousands of people at a large public event.

Yeah, those are confusing too, but that's not what we're doing here. What we're doing is we're helping people manage events that are happening at the workplace. They happen every single day in all of our customers, both mid-level and in the enterprise. They're going to be all-hands calls. They're going to be sales kickoffs. They're going to be training events. And this is really managing the part that you need a room that you can't request yourself. It's not a reservable space.

And so for the people that manage this on the other side, the admins, they're dealing with everything you see on the screen. They're going to get a calendar invite or request. They're going to get an email. They're going to have to open up a ticket in the catering system or even just send an email. They're going to have to send an email or a ticket to security and AV and all of these things. And not to mention, this is not just one room in one place. This is happening potentially around the world in 10 or more places, and they have specific requirements for every location. And so what happens is that the event changes things keep on piling up and there somebody there and when we talk to them they literally in tears almost because it so stressful We working with somebody that we going to launch right now and their quote was I am the integration layer sigh There is no system that brings it all together, which is great news for us because we have done that now.

And I believe that nobody has done this before because they don't understand corporate workspace like we do. We've got robust rules. We already have the user interface. We've already got the integration. So we are so far ahead of that. And so what we leave people with in this current state is high risk. You've got things that have to happen on time. It's like a wedding, right? There has to be food. There has to be a room. There's executives involved. There's maybe external customers. So it's really high stakes, high risk stuff. So what we've done to answer that, we've got it all into one workflow. This is all one orchestration. You start with the user requesting everything they need.

They can see what's available. They can request what they want in terms of space, catering, AV. It doesn't mean they're going to get it. It has to be approved. So there's an approval workflow, which is the real key here. It's going to send automatic workflows for approval to the various departments that need to approve it. And at the end of the day, you've got one system here that's wrapping all that up. So this is super exciting for us because it's an add-on, not just for our customers we have today. We're going to market with this as a standalone as well. And we've got campaigns that are starting right now, full demand campaigns. We're also leveraging the Engine Rooms platform that will help us even further promote this.

So I'll talk more about that in a second, but as you can tell, I'm very excited. And so if there's a slide here or a message that I want you to take away today, it's really this. It's the enterprise market for us has proven our model. It's proven our technology. We know we have product market fit. We know we can operate in very complex environments. It's now the mid-market, the scales. And I want to be really clear about something. We're not leaving the enterprise market. We've got great customers, and we're going to continue. I'm sure we're the best in the world when they go looking for it, but as I described, it's a grueling process.

It takes a while, but when you win, you win big. So what we've done here is we're taking everything we've learned in enterprise, we've productized it, and we're going to now take this to market and deploy it in a rapid provisioning. So this means that the connectors, as Kerm described, they're pre-configured. They don't have to be built out. They're going to be plug and play, basically. And then we have opportunities we've never had before. This is truly different. We have Engine Room as their customer base. So they've got growth-minded customers that this would apply to as well, the products that we're talking about. Not only that, we've got their technology to super promote from a lead generation perspective that we've never had before.

We've got resellers that are signed up and ready to go for this, and we've got our marketplace partners where somebody's going to be able to go into the marketplace where they're already buying software, AWS or Google, buy it, start using it. So the time to value is extremely fast, which, of course, is going to result in scale.

So an important point about this is that we're not just recreating what we did in the enterprise and doing it down market. That would mean, you know, hey, we're just doing smaller deals and more of them in the same hard way. It's not that. We have productized what we're doing. We can do it fast. We can do it easy, and that's what's going to allow us to scale up.

Okay, so what I wake up and think about every day, here's our operating principles. And, you know, the same goes for Adam, who's running Australia. We now have one P&L. We've got the same set of metrics, you know, for the board and for all the U. S. shareholders, you know, EBITDA, bookings, revenue, growth, scale. And the way we do that, and the way that we're going to do that in our business here, is we've got to deploy. So we are mid-flight. We've got huge projects going. They're getting deployed flawlessly. We're getting them out there. That's the number one goal. All those customers I mentioned to you, they're going to get out there. And what does that mean?

That means we're going to start generating recurring revenue as soon as they're out and being used. We've got adoption. So I mentioned to you events, literally everybody we talked to, they have this problem, and it's a burning problem. So we believe that we're going to do a lot of upsells with our existing customers. We're also going to do, as I mentioned, a standalone product. But some of our customers, most of our customers are still on our previous platform, and it represents a great opportunity to enrich their experience, but also for upsells and add-ons. So things like agentic AI and other modules like the events and other things that may not have today, those represent opportunities to increase revenue at the base.

Then we talking about major expansion This is what I get really excited about We have everything I just talked about We got pipeline deals for enterprise We got add but we got this new productivity tool at the employee level that will go up and down market This is something that going to apply the product beat for enterprise customers It going to apply for mid It going to help people do their jobs better and teams deliver which is going to really result in expansion for us So all of that combined by Q4, we're going to have a new cohort of revenue. These are customers that we don't have today that will be generating new recurring revenue, and that's the goal here.

All right, so to wrap things up for me, you know, two engine has just become one, and I think that's really what Kerm has been talking about today is that we are one company and we are way better for it. So what we've learned in the enterprise, we're now taking into the mid-market. I mentioned to you that, you know, Engine Room already has customers today that are going to be great candidates for us. We're going to use their tools to grow. And really, the whole second part of the flywheel here is Engine Room. And I think a really important message we also want to get through to everybody today is that we now have a common backbone.

So the SkyAgentic AI platform, although servicing very different use cases and workflows, we are leveraging a low-cost model through Bonding Cortex that allows us to deliver maximum value to our customers. And so I'm going to turn it over to Adam here in just a second, and this is great news for us as a company. It's such a lift because, you know, Adam already has scale. He's already got profitable growth, and he's got a great product. So I'm excited to turn it over to Adam.

Adam?

Thanks, Chris. I appreciate that. And a big hello from Australia to everyone joining us from around the world. My name is Adam Laurie, and I'm the co-founder of Engine Room, and now General Manager of Sky's Australian Operations. Speaking personally, Engine Room has been such a major part of my life for more than 13 years, so I'm really excited today to have the opportunity to introduce it to all Sky shareholders and people who are on this call for the first time.

So the purpose of today, I want to bring you into the world of Engine Room, show you what we've built, why it works, Give you an understanding of what skies are quiet, and most importantly, to showcase the opportunity we have to build something collectively bigger together.

Okay, so what's our purpose, and what are we here to achieve as a business? We use data digital and AI effectively to enable smarter decisions and unlock greater growth potential for our clients. That's what we do. We are revenue generators, profitable revenue generators for our clients, and that's why they utilise what we provide.

We're an established business, and we have a very strong proven track record. We're Australian-based. We serve businesses across the Australasian marketplace at this point in time. We have a 13-year history, we were first established back in 2013. We're multi-award winning across multiple facets, including performance, innovation, and most importantly for any business, our people.

What we do as a business, we're fully integrated growth marketing solutions designed to capture high intent demand and drive sustainable, profitable growth for our clients. So we have three major divisions. We have our MarTech platform, which we'll go through shortly, fractional CMO, and then marketing as a service.

What problem do we solve? And everything that we do always comes back to the genesis of what's our purpose and what problem I'll be solving for a business. So why do they want to spend with us? So a business these days has difficulty building a cost-effective and scalable customer acquisition engine. And without customers, businesses obviously struggle with growth.

They have difficulty measuring marketing ROI and demonstrating commercial impact. They have disconnected business, customer, and digital data that don't communicate and don't speak. And they have a failure to convert knowledge, data, and AI into a commercial advantage. And that's the problem that we solve. We create growth marketing solutions that transform these strategies into measurable, scalable, and profitable outcomes. We build cost-effective acquisition engines for our clients. We deliver clear, measurable ROI and commercial performance insights. We uniform data strategy and execution into a single source of truth. And we transform the knowledge and data into a sustained commercial and competitive advantage for them.

The key is also what we do and just as importantly what we don do So in the world of digital execution there two primary markets There awareness and there intent Awareness is obviously when I am not aware or I not thinking about making a transaction or purchasing a transaction but I might be induced by a commercial or something to think about it We don focus on that market We focus on the intent market for our clients And those are people who are actively already out there looking for a product and service that our client provides. And we do that because that's the most profitable part of a market that a customer can access.

They're high intent customers. They're not necessarily discount orientated and they're high converting. And that's the market that we focus on for our customers. It's also very measurable because it's towards the end of their journey. Why do customers choose us? We have proven results in delivering for over 13 years now. We have cutting edge technology that powers smarter data driven decisions. We have solutions that achieve substantial and measurable ROI. We have a fully integrated end to end solution profile. And we have unmatched in house expertise and support. So if we look at the MarTech platform that I touched on personally, which forms the foundation of everything that we do and so critical to our success.

So our technology is designed to empower marketers, owners and advisors to make data driven decisions that optimize growth and drive success.

We effectively have three main parts to our platform. We have strategize, analyze, and optimize. And they're all meant to be interlinked to form a cohesive end-to-end solution. So the purpose of strategize, where are we going? What's the purpose of what we're doing here? So we have context, set the direction, define objectives. Analyze, how are we performing? Measure and understand performance, maximize opportunities, identify risks. And then optimize, what should we do next? What's the actions that will drive improvement and gain me a commercial advantage?

And those three parts are all important because ultimately, if you don't have all three parts, then you're going to be missing out on a key part of growth.

Each module that we then build within the platform has a specific application within those sectors. And we have a whole host of different modules. We won't go into detail today, but they have very specific applications that we can call and draw on as required when we're engaging with our clients or the clients who are working through the platform.

So what's important to understand about how we utilize AI and how our technology gains a commercial advantage? LLMs understand language. We all know that. The key with the engineering platform is it teaches AI to understand the business, and that's the key difference. And to do that, we really have to base our AI in a strong foundation. So what we do is we bring in core parts of data for business.

We bring in their business aspects, so what their goals and objectives are, what their brand identity is. We bring in their customers, understand their target market, understand how they're trying to engage with them, understand their commercial advantages. We bring in information in relation to the competitors, what the market's doing. And then we bring in, obviously, information in relation to their individual performance.

That forms that framework for us. And then when we are driving through AI, it gives us the foundation to effectively produce stronger outcomes and stronger recommendations. And you would know that when you're using a lot of tools in the marketplace that maybe use AI, very generic in nature, and they'll basically say these two businesses, because they're in the same sector, want the same things, but they really don't, because they're all individual businesses who have different needs, different competitors, different sectors, different profit margins, et cetera. And without that context, you know, it's going to be lower quality insights and lower quality information. Us grounding it in this real core knowledge database enables our customers to gain a significant commercial advantage.

The other thing important is that when we're using AI, it's a continuous learning application. So we store all this data, and every month that we're storing this data for our clients, we are improving the functionality and the output of what it can deliver. So it's a living, breathing, learning tool that enables us to continuously move forward with our clients. And that's a really important part, you know, both the knowledge centre and the time aspect to continuously gain that commercial advantage for our clients. And that's where you get back to ultimately, you know, a prompt and answer versus some knowledge and action. So traditional AI, give it a prompt, and then it'll be like, here's the answer that, you know, we recommend.

And it's a very generic prompt and generic answer because it's done in the context of everything and everyone. Whereas in engineering, we have that specific knowledge and we have that specific reasoning from learning. And that enables us to do a very high quality action specific to that client's needs.

So today we're going to show you an example of the platform, which is going to go through a few modules. The client I'm going to show you is a smaller client, but they've kindly enabled us to showcase their data. But it doesn't matter whether it's a smaller or larger client, but the same principles apply. And I'm going to give you an example into just some of the modules in how we apply them and how we discuss them when we talk with our client.

So, David, if you'd like to press play. Thanks. Hello and welcome to Engineering. Today I'll show you how Engineering works in practice through a real customer example, So, we'll be back to the CXApp Inc, and we'll be back to the CXApp Inc. $4,000 a month, and the majority of it was from existing customers in a smaller proportion from new. And we can see over the course of the last year and a half that they've grown significantly relative to their starting size, about four and a half times in revenue, and the majority of that growth has come from their new customer revenue. So they're all good signals. But then if we look back and what does that ultimately mean in the context of what the business is seeking to achieve, we can have a look at the plans that we put in originally prior to the start of the year, and now looking to achieve about $1.6 million in revenue, and what that translated through to in new customers and ultimately conversions and their priorities and challenges.

And then now that we've finished the financial year, we can look back and say, well, what have we achieved? What worked? What could be optimised? What risk could be minimised? And we can see that for all the major metrics being revenue, yield and customers, they outperformed their original targets. And if we drill down into more micro information, we can see different elements and then ultimately go, how do we maximise these areas?

Other things that we look at is things like how are they going relative to their competitors. So if we look at the last financial year, and ultimately one of the things they're looking to do is gain market share versus their competitors, and that's our particular customer. We can see that they're growing, and we can look to identify and go, well, who are they actually capturing market share from? That would be an important consideration.

Other things we'll be discussing with them is what's happening in relation to their lead. So if we can drill down into an individual lead, we can see for this particular customer, which aspects are they looking at and inquiring about from their business and which parts of their websites are they consuming to ultimately make a decision to contact them.

And then from a macro point of view, we can look at their new customer opportunities. Once again, if we have a look at last financial year, and we can drill down and see that things like, OK, so what was the main thing they're looking for? What business segments were they inquiring about? All these considerations when deciding the next phase of a program. We'll also be looking at things like attribution. And so once again, if we look at not the last month, the last venture year, we can look and go, OK, well, that was their overall market investment and that was the attributed revenue. But then how did that break down for first versus secondary transactions by period in time, ultimately all the way down the channel?

We can look at individual customers, for example, the conversion that they had journeyed they talk or their invoice that they purchased with. Also things like which campaigns were performing better in Google Ads, etc. , relative to revenue and spend. So there's just a sample of the things that we look at. There's a whole plethora of information on the left-hand side that we could drill down and look at, but ultimately, you know, we would determine that on a case-by-case basis, but there's just an insight. Thank you I hope everyone enjoyed that Okay so why Engine Room and Sky So CXAI Ostrolation we are looking to be CXAI Ostrolation Growth Engine Karama really touched on that before We are a business that has grown substantially year on year That has generated ongoing growth strategies.

We target customers who are in that five to five hundred historical customer range. Average client yield is around about $200,000 per annum. We have 93% recurring revenue and average client extends beyond four years in life. Customers across diversified industry sectors, professional services, home services, manufacturing, industrial, etc. So we have a strong diversified mix, which is a great foundation for the next stage of our growth.

We have a proven track record of scalable growth year on year. As you can see over the last five years, we have consistently grown, and that's profitable growth we consistently move forward with. Importantly, for any company, we have a proven team and we have leadership who are staying on board. We have an award-winning culture, which has been recognised, I think, for the last five years, an experienced leadership, and we have a very strong specialist capability, a capacity who are being retained across technology, AI, data engineering, and growth marketing expertise. What does it mean for the future? Well, you know, there is enormous growth opportunity, even in the Australasian marketplace. We are in a sector, we are fortunate to be in a sector that has high growth in all capacity, whether it be MarTech, the fractional CMO, or marketing as a service.

And even in the context of Australia, even though we turn over $8 million, the context is that the growth opportunities in Australia are so, so significant. And that's one of the things that is exciting about CXAI and Australia coming on board is that it will enable us to hopefully unlock so much of that growth opportunity that we know is available. So what's the next steps from here and the pathway forward?

So accelerating engineering with Sky. So combining engineering expertise and customer relationships with Sky's agentic AI capabilities. So we can look to improve. We already do a semblance of AI, but we know that Sky has strong agentic capabilities, And we have the capability or capacity to look where that can be integrated and improve what we do as a business.

We will be looking to accelerate our product development, once again, leveraging off Sky's expertise and expand our data and intelligence capabilities.

So to that end, engineering business knowledge plus Sky agentic AI, you know, we're looking to improve the intelligence, the reasoning and the action outcomes. Thank you.

To give you some context from a development architecture, so at the moment we have done a lot in the data, the source of truth, and the business knowledge, which I touched on before, and we've started with the reasoning and decision engine. What we look for in the future roadmap is to strengthen the reasoning and decision intelligence, but then also look at taking the next step, and this is where Sky's capabilities come into play, is with agents and autonomous business execution, which we see as a big opportunity for the next steps forward.

So the commercial opportunities, we are strategically positioned to capitalise on key market opportunities that will drive future growth and value creation. We look to expand into new verticals, growth through strategic partnerships and channel expansion, technology innovation to increase customer value, retention and lifetime value, look to use AI to drive efficiencies and scalability and profitability, and strengthen the competitive differentiation between us and other people out there in the industry. But as I touched on before, the capacity for growth is just enormous as long as we execute to that high level.

So I just want to say thank you Lovely to meet everyone today Lovely to introduce the engine room story We very excited about it and we very excited about the next steps And I hand it over next to Zoe who we going to be doing a fireside chat with Hi, everyone.

So my name is Ewe Chen. I'm a workplace strategist at Felton & Company. I spend most of my time inside companies while they're in the middle of changing how they work, not the strategy deck version, the actual version. So it's the part where somebody has to tell 300 people that they're losing their assigned seats and will be sharing desks in the future.

What's really interesting in this particular moment is that everybody in the building is talking about AI. Even when the project is about building out the physical space. So I want to give you three things I think are true right now. They're not predictions. They're just what I keep running into. And then I'll invite Chris Adam and Corinne to ask me some questions. So the first one, here's what I would have told you 10 years ago, and I would have been right. Automation starts at the bottom and works its way up. That's the pattern. The assembly line, the ATM, self-checkout, the scanners in a warehouse. Machines were good at doing the same physical thing over and over, and bad at basically everything else.

So the safe advice was get more education, get further from the repetitive stuff, and you'll stay ahead of it. That was the real deal for about 40 years, and then this technology showed up and completely ignored it. Because it turns out the things that took us the longest to learn, writing, analyzing, summarizing, coding, those are cheap ones to replicate now. And the thing that a kid can do without thinking, like walking into an unfamiliar room and picking up an oddly shaped object, those are still incredibly difficult.

And there's good data on this now, not just anecdotes. Anthropic has been publishing something called the Economic Index, where they analyze millions of real conversations with their AI to see what people are actually using it for, mapped against the government's occupational database. And what comes out is pretty clear. The heaviest uses clusters in mid-to-high-wage occupations. Both very low-paying and very high-paying jobs show low AI use, because those tend to be the ones involving a lot of manual dexterity. Their example is shampoors and obstetricians, which tells you something about how little these two have in common, except that both require hands. So it landed on information work, the desk jobs, and it's not creeping in slowly.

About half of all jobs have already seen at least a quarter of their tasks touched by AI. The flip side is that there is a large part of the workforce sitting in a near zero exposure zone. Electricians, plumbers, HVAC tech technicians, mechanics. So if the job requires you to physically be somewhere and put your hands on something, this wave mostly isn't coming for you.

But being protected isn't the same as being unconstrained. Think about the three-truck plumbing company. What's actually stopping that from becoming a 10-truck business? It's not the plumbing. They're great at plumbing. It's everything that happens away from the job site, whether the quote went out the same day or four days later, Whether somebody followed up on the estimate from two weeks ago, whether the reviews are getting answered, whether there is an X-Jop lined up when this one is done.

So there's a whole back office of a small business, and it's the part that the owner is least equipped for and probably least interested in. And that's the frustrating bit. There's no shortage of software for them. There is a tool for the quoting, a tool for the scheduling, a tool for the reviews, a tool for the follow-up. But that's the problem. Every one of those needs to be set up, connected to others, and babysat by somebody. Nobody started an HVAC business because they wanted to become a CRN administrator. So the choice they've been offered for 20 years has basically been stay small or spend your evenings learning the software. And that's why this feels different to me about this wave of tech innovation.

The promise isn't another tool to master. It's the outcome without the operating burden. And if that lands, the small operators really can get a back office that used to require real scale to afford. Okay second thing Everyone on this call probably already used personalized intelligence at least three times before breakfast and didn even notice once Your phone sorted your emails before you looked at it Your news app put out top three stories that you actually care about on the very top Your grocery apps already know that you were low on coffee And your Maps app regretted you around something before you even knew that it was there And none of it felt like technology. It just feels like things are working.

We've gotten completely used to systems that know us. And when you walk into the office, it all kind of stops.

When every app is doing its own thing, nothing knows you. Nothing talks to anything else. As Chris said earlier, you are the integration layer. You are the one holding it together.

Now, I want to be fair here. AI has already changed a huge amount about how we handle information. Notes, emails, transcripts, catching up on a meeting you missed. That part is real, and people feel it every day. What it hasn't done yet is meet people where they actually are, in the building, in the physical space. The building doesn't know you're in it. The room booking system doesn't know your whole team came in today. Your calendar doesn't know you're on the other side of campus with eight minutes to get to your next meeting. And this is a part I push back on when people talk about workplace productivity. The exhausting part of the day usually isn't the hard problem that you sign up for at your job.

It's everything around it. Finding a room, doing the time zone math, figuring out who's actually in today, working out where to sit when half of your team is scattered across three floors. So every one of these takes 15 seconds, and none of them are your job. But you do 40 of them, and by 3 in the afternoon, you've burned real mental energy on decisions that should have been made for you.

Think about what GPS actually does for you, right? It's not that you wanted a better map. You never wanted a map. You wanted to arrive. You wanted to stop thinking about the path. And that's what people want out of their workday. Not more tools, but fewer decisions. And none of this is new, really. Work always lags behind life. It did with the phones in your pockets, and it did with video calls. It did with every tool that felt normal at home for years before it felt normal at the office. So people improvise, they find a workaround, they use AI on their phones, on tools, nobody bought them, because it makes their workday a little better.

And nobody made them do that. There was no rollout, no training, no email from IT. They found something that helped, and they kept using it. Which is usually how you know what's coming. What people do on their own eventually becomes what they expect at work. And right now, there's a real gap between the two.

The last one, there's some research that's been getting passed around about how AI pilots don't show a measurable return, and people have taken that to mean that the technology doesn't work. I think it's worth knowing what that study measured. It was an MIT report. It looked at whether a pilot moved the P&L within about six months, and a lot of what it looked at was sales and marketing, where six months is still mid-cycle. So they were essentially measuring before the thing finished happening. A new hire doesn't move P&L in six months either, so it's not a damning finding. It really is just a short window. So within that same study, there's a second number that almost got no attention.

When companies brought in a specialist to deploy, it reached production about two-thirds of the time. When they built it themselves, about a third. So we're looking at twice the success rate here, and here's why it's interesting. It's not a technology gap. Everybody has access to the same models. You can buy the same capability on a credit card. The gap is entirely in the execution. A few reasons for it, and none of them are exotic. The specialist has done it before. They solved the integration problem, the permissions problem, the governance problem 100 times. The internal team is solving each one for the first time while also doing 17 other things. The tools that work great for you individually often stall inside a company because they're flexible, but they don't learn the specific workflow you dropped them into.

And internal projects almost always underestimate the boring half. The plumbing, the data access, the edge cases, demos run on clean examples. Real companies are nothing but edge cases. That's how you end up in pilot purgatory for a year and a half. There's also a control thing. Building it yourself feels like control. In practice, it usually means fewer people, slower iteration, and a pile of technical debt. But the reason I find this encouraging rather than discouraging is that none of those are technology problems. Every single one is solvable by a team that's done it before. Nobody's waiting on a breakthrough. The capability is here. The practice is just catching up. I'll say one honest thing, though, because I don't think it helps anyone if I only give you the tidy version of the story.

Buy first isn't universal. If you got proprietary data, a general model can't touch, or genuinely unusual workflow, or you're in a regulated high-risk situation, building can be the right call. It's just not the default anymore. So three things. The pressure landed on information work. Not physical work, which means the businesses that were hardest to grow might be the ones this helps first.

And the second, we all got personalized intelligence everywhere in our lives, except the place we spent 40 hours a week. That gap is the opportunity. And lastly, the technology is proven. What's still being worked out is how you put it in, and that's the variable that decides whether any of this pays off. Happy to get into any of it.

Yeah, and thank you, Zoe. That's great table setting for us in the context, and we're really lucky. Zoe is flying around probably the world, but I'll say at least the States, going to different customers, and she's really seeing what's happening out there. And so I think you mentioned that these are not just trends that you're reading about. You're actually seeing them, and I was really taken away by a lot of that. So what we're going to do now, and just to make this more interactive, we're each going to ask you a question. We have a bit of a conversation, so this is fireside chat part of things, and we're just going to kind of build on everything you just talked about.

So why don't we go in the order that you started from, and Adam, you're the expert on trades and with your business, so go ahead. I think you guys call home services over there. And Zoe, thanks for the chat, and I look forward to you taking the short flight over to Australia at some point. My question is, trades and home service businesses generate enormous amounts of 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?

Yeah, that's a great question. I think the thing is the data is all there. That's the thing. Every job a trades business does throws off information, right? What broke, what it took to fix, how to actually get customers, what venues actually work. But it's scattered. It's in a scheduling tool, in a text thread, in a stack of invoices, in somebody's post-it notes. A fair amount of it is just in somebody's head. And so the person, and also the person who has to pull all of that together and make sense of it, usually is the owner, who might be on the roof all day. And if it happens at nine, at night, or if it happens at all, it's not really analysis at that point.

It's whoever's still awake trying to remember, you know, whether the job was done successfully. And I think that's the gap. It's that the data exists, but nobody in the business had the bandwidth or the training sometimes to really sit with it and find the pattern. And the patterns are also right there, which jobs are actually making money once you count the drive time callbacks and things like that, which estimates are consistently wrong, and by how much and what kind of work you should be taking more of, and which you keep saying yes to out of habit. And I think that's the opportunity. It's maybe less fancy or exotic than it sounds. It's just showing a business what it already knows but has never been able to see in one place and turning that into something they can really act on on Monday morning.

And for this to be not something that just relies on the owner, but as the business grows and scale, it could become shared understanding that the team can mobilize together on.

Thanks, Larry. I appreciate that.

Sorry I jumped in I excited As a person that been spending the last 20 years on maps indoor maps you struck a chord with me Not just indoors when I driving nobody really cares about how to get there Remember we had the map question You had to really figure out I just want to arrive You got the blue dot and you in the center of the universe It so easy now It actually a point that we don have to think You were building on what I was talking about the person being the integration layer Then you started to quantify what that coordination tax that overhead of the things that we don even think about But yeah, it's easy.

I can just look and I can find a place to go. I can navigate things. But we've talked a lot about the neuroscience behind that, and you're really getting at it. This is actually impacting productivity.

And we also know that it's like a screeching slide into sandpaper. You're coming to work. You've got all these great tools like Waze and everything that helps you navigate seamlessly. And then you get in the building, you're like, where did it go? Right? And now I'm back to the manual stuff. So if we can get to a place where the building truly knows me as well as my phone and my personal tools, and we can get over that, you know, sort of overhead tax of people having to figure those things out, like, what do you think, what are you seeing in the research as to, like, what would that mean? I mean, I'm sure there's business outcomes, there's probably, what's the extent of it on, you know, the human experience and maybe the business outcome?

Yeah, I mean, I think the top thing that's jumping out for me is decision capacity, right? We, I mean, human beings, as just a normal, typical human being, there's a finite amount of good decisions that I have on a daily basis. And right now, I think a lot of that bandwidth is kind of wasted on the minute details that just had to surround the actual job itself. So being able to gain back that cognitive reserve to focus on the things that are more important, that are more critical, synthesizing, really understanding patterns, creating, that is, I think, what's really out there. I would add another really important aspect that's just starting to surface is also the mental space for people to focus on what makes us human, which is building connections and relationships with other people.

I think hybrid work and digital-first ways of working has been fantastic, and with all of the technologies, making sure that people can still get the work done no matter where they are. But when people are in person with each other in a physical building, you really want people to have not only the time, but also the mental capacity to do is have a real conversation with somebody and actually start to build that connection, build a community. I community builds an environment that's helping each other to learn. So I think those are all really critical moments that I, in an ideal world, where AI frees us from the minute details that I have to decide. Then I can just really focus on experiencing the present and all of the connections.

Yeah, you just made me think about something, actually, when you're sort of adding this all up. As a thought experiment, what if you said earlier, maybe it's 10 minutes a day, maybe it's 20 minutes. I don't know what the exact number is for the coordination tax, but what if I traded those minutes exactly for high-value moments? Like some of the customers call them moments that matter, right? So if I wasn't, you know, spending 10 minutes doing all this mundane friction tasks of, you know, booking meetings or whatever, and I had a meaningful conversation with you, and maybe we found out that we love the same food or something like that. Maybe we found out that, you know, talked about something about a project.

So I think that's an interesting idea just to go, if I could trade minute for minute for something that's high value, strategic, culture, connection, it's very subjective. I get that. But there could be some pretty interesting outcomes. So, all right. Well, we'll parking lot that one, and Karim, I'll turn it over to you. Yeah, it's a fascinating conversation.

Thank you, Zoe, for joining us. So, you know, I think your last trend was on the deployment, and I don't want to throw a curveball, but I want to put the context in terms of the next generation, you know, younger, my kids or others who are just coming into the workforce who have been coding for half their life. You know they 18 but they been coding for more than half their life So they also already experts of AI They already know how to code it So when you think about deployment you know every one of them is their own white coder is their own developer thinks they the best than anybody else So how do you see this evolving in terms of with you know we see new models coming every day we see new tools coming every day you know everything changes so fast right How do you see this to be a scalable motion from a deployment right How does it get scale not with like millions different things but a motion that you feel like it going to get deployed at scale for the enterprise Yeah, I'll answer that part based on what I can see from inside companies, which is more on the adoption side rather than the distribution side.

But, and I think, and here's where mid-market has become really interesting, right? Because it's the second that, it's the segment that moves the fastest. They may not be the most resourced, but they are the fastest. They might not have an AI center of excellence. They don't have a two-year roadmap, right? But that turns out to be an advantage because there are fewer people that you need to agree to make a move on something. Okay. So what they don't have is someone whose job is to make this work, right? In a big enterprise, you all have that experience. In a big enterprise, there's a team. In a 200-person company, it's somebody's fourth priority, right? So the thing that has to be true for mass deployment is that it cannot require an owner or a champion project plan, all of the complicated stuff.

It doesn't scale into that segment. And what it has to do is it has to spread the way things actually spread in a smaller company.

I love the example that you mentioned with a younger generation that are essentially AI natives. How do they know which app is the trendiest one to use? How do they know which video editing app is the best? Somebody uses it, and it's visible, right? It visibly saves them time, or it visibly delivers better results, and the person next will go ask them, oh, what is that thing that you used, right? That's the whole mechanism, not necessarily orchestrated to roll out or training session, but just one person's date getting noticeably better in front of other people. I think that would be, I think, from a behavioral perspective, what would really help with adoption.

No, that's interesting, and I agree with you. I think it's that we see that in our enterprise business where we get referrals, but I think this is a more viral set of referrals that happen just because somebody uses it and finds it amazing, and then the next person sees the same thing. Cool.

I think, Coram, on that note also, this is where I was talking about value-based selling. The whole market has shifted. We are in 30-day or less commitments in a lot of cases on the products that are coming out. People have to see value. In mid-market, I think the threshold is that much higher. It's like, yeah, this is working for me. Nobody's forcing me to use this. I'm going to use it. I'm going to love it. I'm going to tell people. And I think the onus is on companies like us, the ones that are deploying it. We have to make it first easy to deploy. There's no patience for these giant integrations at smaller companies. And people have to love it.

I mean, that's the whole thing. And it has to stay fresh and being used all the time. So, yeah, anyways, there's so much more we could talk about. I want to close off with Adam because when I first met Adam, he and I have a joint background of working at a large telco, like you were at Telstra, I was a Sprint, and then progressing through our careers, we've gone through the mid-market.

And so maybe, Adam, you want to close off to say, what is your experience with the mid-market, and how do you see this evolving? I love the mid-market because they make decisions rapidly and they're very much focused on the outcome. So as long as everything you do is related back to an outcome, not a metric, it's an actual business outcome, then they don't have to sit there and go, well, it's not an issue, it's budgets, whatever. It's like, well, that makes sense. If I spend $2, I make $10 to go, right? Yeah. So, you know, I could have happily built something for enterprise, but I was like, no, mid-market is where the acceleration is. Mid-market is where the adoption is.

Mid-market is where the speed is. So, but you are right, Zoe, in that part of it is recognising that they don't have that support layer internally. Like for us, we're ever dealing with the owner or one level down perhaps, right, who indirectly deals with the owner, right, or directly deals with the owner. So that's why the model that we do, we mark it. You have to basically be that capacity, right, to enable them to do it, and you just have to accept that, and that's built into the model.

All right. Well, thank you so much, Zoe. Zoe, we could talk for hours. I'm sure we will. Great conversation, great insights, and I'm sure we'll have you back as a guest again, and thank you very much. I think we're going to now move to, you know, Karm has some closing words, and we have some more Q&A, so Adam and I will stick around.

Yep so I got a couple questions that came in so I going to get you guys help on that So one question came in and I start with it and then Chris you can double down Has the Google partnership helped at all, and how? It's a very good question, and, you know, we've been using Google on both sides, helping us with, you know, the cloud infrastructure and helping with, you know, getting all our clients are on, And most of them are on Google Cloud, which is great, and getting them the advanced products and access.

And then we've also, as Chris mentioned, working on the Google Marketplace to launch the mid-market, which we think is a huge opportunity. So we work regularly with the Google team. But I'll tell you one thing that happened. I think Chris was showing you the events module. And I'm proud to say our engineering team working with the Google team got that product done literally in Chris's exact numbers. But I think it's within a quarter or less. So it is an amazing ground zero to now, you know, production. So maybe Chris, you want to expand how Google helped you there.

Yeah, so I also want to make something clear that it didn't go so fast because it's easy. It went so fast. We've got a lot of IP around this. I mentioned in my presentation. And the Google's engine over the last number of years is highly sophisticated, highly proprietary, and what is why we win these complex deals. So we understand how things are managed in the workspace. So we had all of these parts and pieces.

Don't forget, we also have integrations with these customers that are already into their core systems, their ticketing systems, their directory systems. And so the Google team, working with our partners and our internal team, we glued it all together and created what is a seamless orchestrated workflow. And so it's really, yes, it went fast and it's new, but it's really a combination of our features and modules that's very specific to how our customers do business. Now, where does Google come in next? Well, it's a hyperscaler, right? First of all, we're working in secure environments. We have ultra-high security on our own platforms and for our customers.

And next, we're going to be in the marketplace. So when people, part of the whole buying cycle is going through contracting and going through all those motions and then delivering, well, that's all now going to be through the marketplace so that somebody can just sign up, buy it, do a click-through EULA, pay for it, get the product. You know, there might be a couple of interrogations afterwards, but the time to value is super important. And, you know, for all those reasons, like this is really where we go from, you know, small quantity to prime time scale.

Yeah, that's great. That's great. Okay, next question I have is, what are the major synergies between legacy biz, and what do you expect to go for OPEX levels to be quarterly? So I'll start then, and I'll hand it off to Adam to give his input as well. But I can tell you right now, and Adam can tell his philosophy of how he runs his OPEX models, right? But I would tell you right now is that there's a huge opportunity for synergy. You know, as we combine the businesses, we have the common infrastructure, the cloud infrastructure we talked about. You know, Adam also, by the way, uses Google ecosystem extensively, not only for, you know, the services, but also for Google ads and other things he does with his clients.

So there's an expanded relationship there. So there's a synergy factor there in terms of relationship. But all in all, when we think about our business, we're going to leverage Adam's distribution channel. We're in Manila. We're in Manila as well. There's synergies there. As we think about growing the teams, it's one team under the sky umbrella and leveraging all the shared infrastructure costs. We think there's significant opportunity there. So those realizations, as I mentioned in my chart, are going to happen over the next six to 12 months. Some actually are happening this quarter, as you can see some of the impact, but they're going to start happening very quickly in Q3 and Q4 and early next year as well.

So I'm excited about that. But maybe, Adam, you want to talk about your OPEX strategy, how you're managing your OPEX?

Yeah, and I think the important thing is that business model has always been about profitability, sustainability moving forward. We last took capital on board, I think, seven or eight years ago from memory. So, you know, we have always focused on, from an OPEX point of view, that we would be significantly below the revenue side so we can invest in a sustainable way. So look, I think the key is what Karam touched on before, and this is the pathway that we're going through at the moment. What's the operational cost that can be reduced as a percentage that we cross- And I think that's really the process that we're going through at the moment, you know, because there's a lot of aspects of that.

But I'm sure we'll be reporting on those in the future as we succeed in that area.

Okay, last question. I'll give it to Chris. So, Chris, the question is, you announced a lot of great new products. You've got the flow and beat and events and stuff. What is your competitive mode for a small company like CXA? How are you creating the competitive mode? Because a lot of people are wanting for those kind of products. So what is the Sky mode?

Yeah, we've got a number of different things here, and I touched on it earlier, and one of them is just IP. I'll start with, and I'm not relying on this, but we've got enterprise customers. We've proven technology. We have been deployed in some of the world's toughest, most complex environments at scale. This is not just a demo happening. We're talking about thousands and thousands of users at these organizations and reliably delivering that. You know, beyond that, when I really think about today's market, it's all about our ability to differentiate. And how do we do that? Well, one of them is just even on the cost side. When we talked about our Argentic AI, bond and cortex is truly different in the market.

We are able to do LLM at a fraction of the cost. So something that somebody is doing in one of the main LLMs is costing a cent and not, you know, tens of dollars. So that's a huge moat. We got the IP around context and spatial awareness This is something that other companies do not have We understand what going on in space We got all these integrations which is giving us connectivity and it enhancing the user experience It also creating all that data So when you saw me demoing about asking these questions by conversation and getting highly analytical or insightful answers that coming through all of those integrations And so we're really at the center of this.

I mean, the company grew up bringing together events, maps, workplace, and when this became CXAI, it was based on delivering agentic AI. So I think we're leaps and bounds ahead of understanding how to deliver agentic AI. And you said it right at the beginning of the presentation. This is not another chatbot or an assistant. This is an agentic solution that does things for you. And that's what people want. We don't want the overhead of having to, you know, answer everything manually or set up a meeting manually. It's going to do it for us. So at the end of the day, if you ask me, I think it's user experience. And we talked about it all through this.

People are loving the product, and they tell people about it, and it becomes viral within those organizations. And of course, all the referrals that we get because people are super happy with the products.

Yeah. Great. Well, thank you, gents. Really appreciate it. We're going to head to my last slide or last two slides, and I know we're at the top of the hour, but we'll go a few more minutes here. Thanks, Chris. Thanks, Adam. All right, so let me leave you all with this. I spend my career around major technology transitions, and I believe Agentec AI will be one of the most consequential. And I don mean that lightly And I don believe the winners will simply be the companies with the biggest models I believe enormous value will be created by companies that understand context and turn that context into action That is the company we are building. That is what CXA is about.

We started with the workplace. Now we're expanding from place to person to business. These are the three forms of context. Place through our flow product, which tells you where and how people work. Person through beat, what an individual and team need to accomplish, and what should happen next. And business, how companies acquire customers, convert demand, and grow.

That business context is significantly strengthened by Engine Room as we talk today. We are building the agentic operating layer for how companies work and grow. Full stop. And we're focused on it. We're really excited about it, as you can see. And I want to leave you with three things. You know, I said at the start of their call, and I thank you for your patience to be with us for nearly two hours here. But if you remember only three things from today's call, I want them to be these. Number one, we have changed the scale of CXAI, Sky. We have moved from roughly a 4 million analyzed revenue company at the beginning of this year to a combined platform with more than 12 million of analyzed revenue scale.

And now we serve both enterprise and mid-market customers. Number two Sky 2 is moving from vision to commercial execution The platform is in production We have customer deployment starting We have major renewals We have new multi enterprise wins And now we have Engine Room customer base as an additional channel to which to prove and distribute our new AI products Three, we have a clear operating priority, profitable growth. The next phase is not simply about adding revenue. It is about combined double-digit growth with increasing recurring revenue, stronger software mix, operating leverage, and disciplined execution. Our directional objective is to move toward break-even in the second half of 2027 and profitable growth beyond that point. So I would categorize Q2 this way.

Q2 is the quarter, Q2 2026 is the quarter in which Sky 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. Sky2.0 creates the opportunity for operating leverage. And execution from here determines the value we create. Thank you to our customers, our employees, our partners and shareholders for your continued support. We look forward to updating you on our progress next quarter. We plan to do that in November. And then we are also looking at another investor session in the end of the year or start of the year of 2027. But we're excited about Sky and to the Sky and beyond.

Thank you everybody. Opera, you may close the call. Thanks.