Up for you.
This one?
It's a pretty big one.
Yeah.
It's not usually this big.
Nope.
All right, let's go ahead and get started. Thank you so much, Aidan and Inbal, for making it. Let's go ahead. I will start with just a broader question, and then we will kind of dive into some specific questions for each of you. Twilio has historically helped customers connect communications across a fragmented carrier ecosystem. As you increasingly connect context across different channels, applications, AI agents, where do you feel like Twilio can create the most value? Is that delivering on the communication, managing a conversation, or increasingly helping customers act on those things?
I think it is all of those things. I think our bread and butter is the channels, right?
Yeah.
Connecting our customers to the end consumer. We are increasingly trying to make those communications more valuable. I will let Inbal talk about it, or I am sure you will get to it, but we have launched a suite of Conversation products, from Conversation Memory to Conversation Orchestrator to Conversation Intelligence, in the last six months or so, and that is where we kind of see the future going in terms of agentic as well as human communication.
I think the uniqueness for Twilio is, if you think about Twilio as three layers, we have all the communication channels or where the conversation is happening, and then you have the contextual layer, which is the data. Then you make the AI agents that are operating across all these channels with the context layer so much better, more effective, more productive, more accurate, which is what you are trying to achieve. It kind of operates, as Aidan said, across all of them.
Yeah. Then, Aidan, as the product has broadened, how has Twilio been able to balance investing ahead of these new product cycles while maintaining operating discipline, particularly given that newer products can have a wide range of different financial profiles?
Yeah, I think we've actually done that pretty well. As growth has re-accelerated, if you think about 2024, 2025 into 2026, we've been quite disciplined on cost. We're flat to down in 2023 and/or 2024 and 2025. We're up a little bit this year as we launch some new products. But I think we've run the company differently, right? I think with Khozema, who's our CEO, since both of us kind of took our roles, we've really introduced a lot of financial discipline and operating rigor into how we run the company.
I'd say how that manifests itself in innovation and investments on the R&D side, I'd say if you look back over a couple of years ago, I'd say we just tried to invest in too many different things, right? We tried to place a lot of bets in different areas. We're much more focused now.
We're much more focused on platform efficiency to help us drive innovation velocity. Inbal's actually been the person driving that for all of us, and we're very ROI-focused. I'm fortunate in that I have a CEO that has a very strong financial background, right? So he's very ROI-focused as well. I think a great example of that in terms of an area where we've invested recently is our self-serve platform.
Twilio has always been developer first PLG. The reality of it is we let that process get too complicated for our customers over time, and so over the last couple of years, we've really undertaken an effort in product and engineering and go to market to simplify that experience.
Most recently, we launched what we call Twilio Console, which we can get into, which makes it even easier for customers to come in and adopt multiple products on Twilio. So that's one example where the return was just obvious, right? And we put a lot of investment behind it. Maybe just on your last point around different financial profiles, what I would say is, from a pricing model perspective, all of our products are usage-based, for the most part.
So in that sense, they're very similar. Where I'd say they're actually different is in the gross margin profile. So aside from messaging, most of our products are quite high margin. I think this new conversations layer that we just launched, I'd put in more of the high margin bucket.
Inbal, one of Twilio's historical strengths has been giving developers these module building blocks. At the same time, customers increasingly want Twilio to solve more complexity for them. How do you balance remaining flexible and being developer first with moving higher in the stack and owning more of that layer?
Yeah. I think that the first thing that we should anchor on is that the concept of developer is changing. It used to be that developers, we were able to segmentize them as like, it's a software developer. They're here to solve a complex problem. You expect them to solve the end-to-end. But the cost of building is going down. As part of that, what we're seeing from the different enterprises and the different ISVs and businesses is they want to be able to control full customization.
Also remember, we're coming from the era of SaaS, that businesses got a black box and they needed to customize it to a specific level. We see that what our customers are asking is to get more flexibility. They want to be able to create these use cases that really solve their problems.
They want to be able to build some of these bespoke solutions. Because of the cost is going down, then suddenly the developer is getting a different context. Why does that matter? Because Twilio serve both. So we're serving the existing developers, which is the software developer that want all these bits and pieces, but we're also introducing new interfaces that enable the new builders to be able to build the solutions they want on top of Twilio in the fullness of time.
Conversation Memory is a good example for that, because when you're looking into how are we making conversation better, conversation, in order to last a lifetime, it needs to have some sort of a memory. So a true Customer 360, a true journey that is happening throughout the lifetime, and you can customize it and build it by yourself, or you can use a Twilio component of that. You can introduce it to your conversation, and you can also build a brand new business, like an AI native that is building AI agents that are using our Conversation Memory on that.
You have delivered 5%+ organic revenue beats over the last two quarters. It has obviously been very well received by investors recently, but what drove that specific upside, and how should we think about that level of performance persisting over the next couple of quarters?
Yeah. It was pretty broad-based, which is what we have said. So when you look at it across product, when you look at it across sales channel or industry vertical that our customers play in, it is actually quite broad-based. Now, messaging is 60% of our revenue. So that tends to be the biggest driver of variability in any period.
When you look at that business, it grew like 18% in the first half of this year. It is a large part of why we outperformed, performed relative to our guide. With that said, we are usage-based, which is why we tend to guide and plan a bit more prudently. If you look back maybe a little bit further, last couple of years, we have really beaten in the range of our guide in the range of 2% to 4% on the top line.
I would say that is more the norm. 5%+, nothing has changed in our guidance philosophy in that sense. So I would say we are not expecting 5%+ to be the new normal. Now, when you look at gross profit and you look at our other products like voice or software add-ons, those are all very high gross margin or for every dollar of revenue they carry more gross profit than the messaging does. So when you think about what drives the gross profit strength, it is really much more balanced across the portfolio where I would say at any given period, messaging can drive revenue one way or the other.
Voice AI is something that has been top of mind, I would say, for everyone recently. But it is something that can work really well in a controlled setting, but when you introduce it into production can face challenges with latency, interruptions, accents, background noises, network variability. So how do you think about the relative importance of those issues and how Twilio helps solve those?
I think we all need to remember these are very early days in the voice AI journey. We are just at the beginning. The models are still evolving, the infrastructure is still evolving, the how we build AI agents that are voice first is still evolving. This is a problem that the industry has been trying to solve for years, but with the introduction of LLMs and AI agents, we are now in a point in time that we can solve it, but the infrastructure has not yet kind of built towards that world.
It was built for human engagement. Where it starts playing a role is where we see adoption and we see enterprises and different businesses taking these AI agents into production. There are not many of them. If they do that, they do that on a low-hanging fruit use case that they feel confident delegating to AI agents or they do that in a specific or a small amount of customers that they are taking through that flow. What are the barriers to take some of these into production?
Accuracy is number one. Accuracy, it is a combination of the infrastructure, it is a combination of the ability of the model to really interpret what is happening in the conversation, and all of them together are playing a role. Where accuracy is critical is in the infrastructure layer when we are talking about latency, when we are talking about quality, when we are talking about all the ability to do proper turn detection, and this is where Twilio is playing a significant role. The second part and the trust.
Trust today is a big blocker from taking these AI workloads into production because suddenly you have an entity that is there to complete a task. It is very task-driven. It does not have judgment. It is there to complete the task. How are you enabling that AI agent to do that work unsupervised? We are missing a lot of that layer of supervision. If it is the identity verification or it is the government or it is the kill switch, the way that Cole likes to call it, which is really about how are we monitoring what is happening in the conversation itself.
I think the third part is we need to understand that the regulation will evolve and they will change. There will need to be new policies that are introduced. As part of that, it goes to data storage, what is happening in the conversation, and how we monitor these conversations, where is the data stored, what data you are allowed to store and not allowed to store. The way we are thinking about it is really across all these facets. The other part is thinking about a conversation.
A conversation rarely happens on a single channel. It tends to be by nature a multi-channel engagement. You start on a voice, then you work to messaging. As I said, it is early days, so we see mainly the voice use case, but all the messaging and email is coming. Twilio is uniquely positioned to be able to host through our orchestration a multi-channel conversation.
How do you think about eventually becoming more of a layer that helps customers select amongst these providers in real time based on maybe quality, latency, cost, versus kind of continuing to put that in the court of maybe other platforms?
We already are doing some of that with our ConversationRelay product. Our ConversationRelay products enable customers to pick and choose and bring the models, if it is the STT or TTS model or their LLMs, to be able to customize and create their own AI agents. The premise of Twilio and why we are very adamant is about being a neutral platform. We do not think there is going to be only one. We see customers choosing different models, different AI agents, different use cases of implementation of AI agents based on their specific teams. They do not want one to rule them all.
They believe that there is going to be a multi-agent type of a world. We are giving customers the flexibility to bring these agents into production by building on top of the Conversation Memory that basically keeps the contextual on a conversation, even if there are multiple agents that are involved or being able to have a conversation across multiple channels, even if it starts on voice and then goes to messaging or throughout the lifetime of a customer,
which is maybe you start with a marketing engagement and then it goes to a sales and then there is a support issue and then it goes back to kind of a marketing opportunity. Really connecting all these together and giving the customers the flexibility to pick and choose the workload they want to bring to Twilio, we are giving them the rails to run on top of.
In voice, AI can benefit Twilio, it feels like in a lot of different ways. Maybe it is more minutes at existing customers, greater software attachment, or just overall growth in the number of customers that are leveraging Twilio. Which one of those are you thinking having the biggest impact on Twilio's gross profit growth right now?
Yeah. I'd say the two primary drivers of the gross profit growth or the revenue growth in voice is increased minutes, so just more volume on the channel, and the software add-ons. When we look at Q2, the voice product grew 20% plus. When you look at the year-over-year growth, in dollars, 50% of it came from the channels, and 50% of it came from the software add-ons. So pretty evenly distributed between those two things. We used an example, the AI natives you kind of referenced as well.
Yeah.
Some of that is driven by the AI natives coming into our self-serve channel, kind of building on our platform. Still a relatively smaller part of the business. But what we see, as an example, we have a horizontal agentic builder customer. When we look back about a year and a half ago, they came onto Twilio's platform. They were spending low hundreds of thousands of dollars per quarter. All of it was voice connectivity.
Last quarter, that's a $1.5 million revenue per quarter customer. A third of it is actually the software add-ons, two-thirds of it is the connectivity. So as they grow, we scale with them, and they're starting to add these software add-ons, whether that's conferencing or Media Streams or Answering Machine Detection, things like that make their product and their agents smarter and more valuable.
Yeah. It feels like a lot of people are increasingly using voice as a channel to communicate with these LLMs. How do you think about that as a growth driver going forward?
Yeah, I think that today, I'd say not a big driver.
Yeah.
We have had a little bit of it here and there, but to the extent that they are using a channel, whether that is voice or in some cases it might be an OTT channel where they are using WhatsApp voice or whatever it might be, they can definitely leverage Twilio's rails. Where we see some of the model companies today tends to be a bit more of a traditional use case, which is verification, authentication coming to our platform. As customers come to their platform, they need to verify who they are, so they will leverage Twilio to do that, whether that is through 2FA or something like that.
Yeah. We touched on this a little bit when we talked about the out-performance on revenue over the past couple of quarters, but when we look at it from a gross profit perspective, how do you think about the different drivers of gross profit with being above your expectations over the last few quarters, and how do you think about that momentum continuing through the back half of the year?
Yeah. I would say the biggest driver is obviously revenue, right? Yeah, revenue outperformed, gross profit followed. But when I think about the fact that in Q2, gross profit grew faster than revenue, I would say there were two primary drivers. One is product mix. So voice, the software add-ons, our self-serve channel, those are all high margin, right? As they grow faster, and in particular, as they grow faster than messaging, we have what we call favorable mix, right? So that is helpful.
They just carry more. For every dollar of revenue, they have more gross profit. The other is cost actions that we are taking. So there is a lot that we are doing to actually reduce our COGS or cost of goods sold. So that is things like establishing more direct connections with carriers around the world so there is no middleman or aggregator in between.
We have taken a lot of initiatives between my team and Inbal's team on our hosting costs. We have also shifted some of our products that were still on-prem to the cloud, and we are seeing some benefits from that. So we are trying to come at it from a couple different angles. Growing gross profit dollars sustainably has been a big focus for us as a company, and I think you are kind of seeing that play out in the numbers.
Yeah. Another thing you brought up when we were talking about the revenue out-performance, but messaging I think has been a big contributor to the first half of the year, and I think that's where people have a little bit less understanding of exactly what's been driving the relative strength there. Would love to get a little bit deeper into what the most important drivers of that inflection have been and how to think of even just the mix between maybe new customers adopting messaging versus increasing volumes and those different things.
Yeah. So messaging's about 60% of our revenue. So it's a big business, right? It grew 18%. So in any given period, just given the size of the installed base or the existing customer base, volume with our existing customers is the biggest driver. So new customers are growing quite well, actually very well, but it's just off a smaller base. So why is our existing customer volume growing? It's actually not one thing, which I know is a very unsatisfying answer.
It's a lot of things. It's when we look at it across industry verticals, like our top five industries where our customers play are growing really well. When we look at it across sales channel, whether it is self-serve or our enterprises or ISVs, again, all growing quite well. So it's not one thing. Some of it's macro. It could be our competitive positioning. It's a number of different things that are driving it.
Yeah. When you think about the carrier fee side of things, obviously that's just a pass-through at the end of the day, but has that changed how you're seeing customers evaluate the ROI of messaging as a channel versus other channels, or their behavior continued to be very similar despite that?
Yeah, not yet. I think you see that in the growth rate. 18% is ex the carrier fees, right? It is 28% with the new carrier fees in place. We shouldn't look at it that way. The operational number is 18%. So no. Have we seen a shift in demand because of the increased U.S. carrier fees? We haven't yet, but customers talk a lot about it, and they certainly don't like it.
It's higher price to them. We've been very transparent with them around the fact that these are coming. We try to message it as early as possible. We also are very clear about the fact that if, for a small or mid-size business that may be under more financial pressure or something like that, there are other channels available to them. We support WhatsApp.
You can go to email if it's just you need another form of written communication, or other OTT channels. To date, like I said, no real change in demand as a result of it, but certainly not something our customers love.
Yeah. Makes sense. You recently made Conversation Orchestrator, Conversation Memory, and Conversation Intelligence generally available. What did you learn from the beta customers that most influenced how those products were designed, and then any other early comments you have on them?
Yeah. I think beta customers is such an underrated asset that a company has because what we've seen when we started going after the new conversations, we have a thesis that conversational AI, especially the voice one, is what customers want to solve right now. Some of our beta customers really help us figure out what exactly are the set of capabilities that we need to build into the product and prioritize. Every R&D leader has above the line and below the line.
For us, really figuring out what are some of the features that we have deprioritized with the assumption we have time to build them. Our beta customers help us understand that we need to solve it right now. A warm handoff is a good example for that. How are you handing off a conversation between an AI agent and a human?
How to detect an escalation happening was a top priority feature that our customers indicated that is very critical for them, so we prioritize that. The second thing that we learn from our customers, and that is true for every infrastructure or platform company, is you are building something with a specific set of use cases in mind.
Then we are starting to see customers adopting. Sometimes they will come up with new use cases that you never thought about. For us, we prioritize conversational AI through the lens of support, but some of our beta customers started using that for sales use case. So identifying leads, especially when the store is closed, and how to do a warm handoff to a sales agent once the store is opening and kind of understanding insights from that conversation, what is the likelihood of buying.
That enable us to do another set of thinking through what are the next set of features we need to build into our platform to unlock these new set of use cases that we not necessarily prioritize when we went into the beta. I think that the last thing is that it helped us refine our go-to-markets from a pricing perspective, for example. We had the chance to survey some of our customers and kind of test different pricing model with them. What are you likely to want to be able to see?
What will you be willing to pay, and how to think about it, what are the areas that will keep you up at night if suddenly you see them on the bill, but also help us refine our go-to-market motion because we had a chance to test a lot of these use cases and a lot of these sales pitch on our existing customers in a safe environment where they can give us an unsolicited feedback and help us refine how are we thinking about taking this to the market.
I do want to give the audience a chance to ask questions, so I will circle back on that in a second, but start to think through any questions you might have. Want to double-click on the Conversation Memory piece of this. How do you determine what information should be remembered, where the information should live, and how it should be governed across Twilio versus maybe some of the other tools in the customer stack like the CRM?
Everything, the way we are kind of dividing it right now is that the Conversation Memory is everything that needs to make the current conversation better. We're refining the transcript as we go. We're giving insights and indication on the conversation as it goes, so the agent will be able to complete their task in a better way.
Then we're kind of taking that transcript and we're storing it in our longer-term memory with the idea that eventually customers will be able to query that memory. They will be able to train their agents to be better in the fullness of time. But we're not asking customers to kind of copy any of the data that exist in their CRM or exist in their data warehouse.
We're kind of building on top of that, where we have connectors to all these data storage, and we're trying to keep the most relevant information to be able to keep the agent, the human or the AI agent, more successful in handling that specific conversation. That's kind of the delineation that we have between the long-term data storage that are not real-time and what is needed for real-time conversation.
Yeah. Any questions from the audience quickly? All right, I can keep going. I want to kind of touch on what you mentioned earlier, which is the self-serve motion and the momentum that you've seen as a result of the new Twilio Console. Can you just walk through maybe some of the things that you've seen over the past quarter and some early signs of success with that?
Yeah, maybe better for Inbal since she did all the work. I can start, though. Not all the work. Well, most of the work. I certainly didn't do any of it. So, really it's what we call Twilio Console. We launched it in May at our Signal conference, and basically allows customers to come in, developers to come in to one place, access all of our products, I know it sounds novel, but they couldn't do that before, get things like one bill, that makes it much simpler for them.
And we've leveraged AI to make the experience much better, to help them along the journey. So when a customer comes in and wants to, say, adopt messaging, there's actually hurdles they have to go through to do that. They have to register and all that, but they actually have to provide what is their campaign, what is their use case, and carriers want to make sure that it is a legitimate use case, not spam, essentially.
We help them through that process, and AI is there to kind of tee up, like, hey, as they are filling out the forms, as they are doing all this, "Hey, be careful. That looks like you got it wrong," or, "Maybe think about this instead." It also tees up new products based on the use case. We have tried to make this as easy for our customers as possible, and AI is a big part of that. As a result, what we have seen is conversion rates for our customers in that platform is up like 90% relative to our old platform. Great success. It has only been three or four months or whatever it is, so it is still really early days.
Yeah, I think basically what we have done is we kind of gave the cognitive load of figuring out how to work with Twilio to our customers. The first thing we did is take it back. It is like our customers do not need to be an expert in our product. They do not need to guess their way into using Twilio. By bringing all the products into a single Console, so it is available for all our customers to see in one place, they do not need to log into several different consoles just to see what they are doing with Twilio.
The second thing is removing some of that friction of experiencing, experimenting with our product. We created kind of a playground. You can think about every developer coming to the Console, they now get a credit. They can try it out. They can upgrade very fast. They see everything that is happening. The third bit of that is really giving them insights, if it is on our billing or how to use the product or how to start a new campaign, or how to onboard into a specific new product.
Removing a lot of that friction on how to use Twilio, and then in addition to that, guiding them through the process with AI agents was the biggest differentiator that we have seen from customer satisfaction starting with the new Console.
Yeah. How do you think about maybe other parts of the product portfolio where you can maybe achieve similar results, like identifying the next self-serve or new Twilio Console unlock?
For us, it is a lot of working backwards from the customer. Everyone knows I came originally from AWS at some point in my life, so there is a working backwards. How do you identify what are these customers' problem that we are trying to solve? How do we look into these signals of where the market is heading? What is the next set of problems that customers need to solve?
Then looking into our portfolio and assessing, do we have a solution like that, but it is not used to solve that specific problem, or are we missing some components? Are we missing the ability for customers to connect the dots? I talked previously about some of the adoption blockers in terms of taking AI agents into production. Some of that is trust. What does that mean, trust?
Trust is compliance, so how are you filing for all this information that needs to be shared? The second thing is how do you validate that whoever is engaging in that conversation is a legit player or a consumer or AI agent. The third part is that something is happening in that conversation the way it should be. So we are combining all of that together is like what makes a conversation trusted is a big opportunity to unlock, and that is an area we are investing in.
Yeah. You mentioned this before, you allow customers to choose their models, what cloud platform they use, data warehouses, other business applications. How do you think about the strategy of remaining open and neutral versus still owning enough of the architecture to ensure that you have a durable differentiation going forward?
I think the biggest differentiation is threefold. First one is being the largest telco in the world. It is what it is. We are the largest telco in the world. We connect so many customers, so many carriers into a single network that can serve customers globally, worldwide. We can terminate a message in almost every country that exists today.
We have direct connects with some of the carriers. In addition to that, we have voice connectivity. In addition to that, we have an email solution. We have an OTT channel. If you think about the largest network of communication is a strong moat that Twilio has. It is not just the connectivity, it is the ability to operate in a highly regulated industry that the compliance policies are changing over time again and again, and being able to catch up and meet that compliance.
That is the basic Twilio premise. On top of that, it fits the contextual data. The contextual data is not just a data storage. It is not something that happens after the conversation is done. It is what is happening in that moment. How are you making the conversation better in that moment, but also how are you making that conversation better in the fullness of time?
Some of that is really focusing on creating that contextual layer that solve the problem in the moment, but the other part is refining through transcripts and training data and connecting to a knowledge base that the company has to maybe improve their documentation because something the customer has been reading is not really clear. Or maybe some of that is how you train your human agents to operate in a better way because we have seen that engagement not operating.
When you think about that glue layer, the one that sticks the conversation together from the communication to the ability to have a contextual information and then make AI agents better and human agents better or any engagement better, it does not matter if it is scheduling an appointment. That is kind of a unique moat that Twilio has, and it is the right choice for us to be AI agent agnostic and a model agnostic because we do not know if it is going to be only one.
We do not think there is going to be only one. There is going to be multiple agents. There is going to be multiple models. We see some of these transform like the frontier models to open source models, and we want to cater for the customers wherever they are, what is the use case they are trying to solve, which model they are trying to use. That is becoming kind of, I would say, the commodity layer of the AI world while the infrastructure itself is the sticky part.
Yeah. Given the breadth of your customer base, the breadth of products you have, the number of new products that you've come out to market with, how do you think about directing incremental investment into each of those areas and kind of balancing different priorities, whether it's enterprise versus SMB or kind of other ways of splitting the business?
Yeah. Do you want to talk about how you have-
I know how-
kind of run the team on-
Yeah
Air, Food, Water.
Sure
Horizon One, Horizon Two.
One kind of mechanism we have introduced in the past three years is annual planning. This is something that historically we have not been doing as part of the R&D organization, and the idea is to look into everything that we have on the backlog. If it is keeping our stack alive or making sure that we are improving the core business to focusing on core excellence.
How are we making channels and data better? Then innovation. What are some of these innovations that are Horizon One, Horizon Two, and then what are these experimentation we want to run on Horizon Three? We see some signals. It is not yet fully mature. We do not necessarily have a clear customer demand.
We have introduced the practice of taking all the backlog once a year, looking into what is our budget foundations, what are we working with? What is the headcount allocated? What is the cost of our infrastructure? Making sure that we are prioritizing across each one of these buckets our headcount allocation.
Sometimes it is more towards getting rid of tech debt in specific channels because we see, for example, voice AI taking off. There is some tech debt we need to pay there to make voice quality better or investing in a new set of products because our customers are signaling to us that they really need new solutions. Really being very much focused on solving into these big buckets and prioritizing our investment based on what will get the biggest ROI for the company.
Yeah. There's a lot of debate right now in the software ecosystem of what the impact AI is going to have on application software. Curious from your perspective, it feels like you're in a position to benefit no matter who ultimately wins at that layer. How does that dictate how you think about where to invest in the product and how to think about what are the key priorities for Twilio going forward?
Yeah, I think when we think about AI and the impact on Twilio, Inbal's talked a lot about what our competitive differentiators are, where we play in the infrastructure layer. We're seeing voice as the place where it's all starting, right? As companies build agentic solutions, voice is the most natural place for it to start. It tends to be oriented toward more customer support.
Again, voice makes sense there. I think voice is definitely seeing a benefit. Ultimately, as we talked about earlier, we think it goes multi-channel, right? That's two-way messaging, email, et cetera. Communications will become both synchronous and asynchronous. In terms of AI in the company and how we think about it, we're certainly leveraging all the AI tools. Self-serve's a great example where we've gotten a lot of ROI by leaning into AI.
I think our global operations, our global support, our customer support is another area where we've leveraged AI heavily. Then within Inbal's team, with our very technical folks, all the coding tools and everything that we have. Definitely leveraging it a lot. Very still, I would say, focused on getting operating leverage. I would say we were not one of those companies that was ever token maxing. It just goes against our financial discipline and operating discipline culture. We're leveraging it broadly. We're just trying to do it in the right way.
Yeah. Makes sense. Well, thank you so much. Everyone join me in thanking Inbal and Aidan for their time.
Thank you.
Thank you. Thanks for having us.