label: Colin Heilbut - Rime

text: Well, Well, Colin, thanks for joining.


text: Think you met everybody on the call. You need? Just met RAs, which


text: is great.


text: Purpose of today is to give you a chance to


text: share your skills, talk about how to pretend the pitch to us,


text: There's no way to answer. That's what we've kind of talked about.


text: How do you think about things? How do you approach a conversation? We'll play different stakeholders within the


text: account, that and we'll just hand it over to you.


text: Excellent. It's a pleasure to make your coincidence.


text: I can, perhaps kick it off with a bit, quick introduction to my


label: Colin

text: My name is Colin Hubbard. I'm in fact the newest salesperson here at Rhyme. New to the company, I've been in Speech AI going on


text: three, four years now, actually first employee at Deepgram.


text: You might have heard of, and then intelligent voice and high security speech recognition. So if you had a whole bunch of


text: legal audio from a a court case and it go through the cloud, you could give it to us


text: We were one of the first doing GPU inferencing before it was cool to process that real fast.


text: I entered a student not long ago to French AI Lab, also in the space


text: but really excited about Rhyme. If you wouldn't mind, could we go


text: just go around the room very briefly to introduce yourselves and understand your rules at Assurant?


text: Sure. I can go first. I am the chief Product Officer at Insurance. I will come through


text: all of our product surface areas for


text: our direct to consumer business,


text: and yeah.


text: Excited to hear about Ron, her friends, and, yeah, looking forward to the conversation.


label: Voice 3

text: Yeah. I'm the head of on the hours here. And so you know, we take your product and put it in front of our customers and kinda like the first thing that they hear is, like, the voice. Right? So it has to be really good.


text: Because they're essentially gonna be selling


text: you know, selling it


text: for you guys. So so, yeah, we've been trying to


text: lot of different things. Like, we just kinda want the dust that's out there. So


text: I think you've come to the right place. Good.


label: Patrick

text: Okay. And I'm Patrick.


text: Nice to meet you. Hey. Good to you.


text: I lead team of engineers.


text: Focused on


text: implementation


text: Cool. And so


label: Voice 4

text: I've been doing a little bit of research about your


text: amazing company. But do you always


text: of


text: focus on a particular part of the business or


text: For us, like, we're we're all focused on the customer experience out of the house.


text: Oh, I mean, like you


text: have some automobile,


text: but you do a lot of, the phone warranty stuff or a little bit of everything?


text: Most of us focus like, the primary business universe is, the


text: warranty


text: side.


text: At Assurant.


text: Mhmm. So we have a bunch of different clients, right, within that


text: business.


text: C


text: mobile or


text: Yeah, like T Mobile, Geico's, like another one, right? Like we do


text: like we work across


text: all the different clients.


text: Most of us around the table represent, like, a core kinda, like,


text: C Max product group that looks after this, like,


text: digital interactions with customers.


text: Yeah. Walt, did you wanna introduce yourself?


text: Yeah. Sure.


text: I


text: am in charge of the kind of contact


label: Voice 5

text: center operations here. Oh, wow. So I manage


text: 2,000 seats right now.


text: And we used to have 3,000.


text: We're kind of like


text: trying to balance you know, teeth versus


text: voice agents.


text: So two thousand first distill human seats.


text: Yep. Exactly.


text: Cool. So I wanted to propose an agenda, but, look, we we can always add or subtract as need be. We've


text: done some introductions. I wanted to briefly talk about a format that works well for us. Then


text: just touch very briefly, you know, what what makes Rhyme different to some of the TTS you make


text: currently be using or or heard about


text: Obviously, the bulk of today, I really just wanted to talk about VoiceAI at


text: at Assurant. I understand that you you have a very successful operation at the moment.


text: There are some concerns, I think, in my notes from our partner NICE, who introduced us.


text: Something about voice quality, but that could mean anything from


text: latency to expressiveness and prosody. So I really wanted just a deep dive on that.


text: Then, again, very briefly, I'd want to show you a little taste


text: of what we can do.


text: And really along the way, please stop me if you have questions or want to


text: click on anything, and then I'll leave some special time for questions at the end and talk


text: about the next steps. So Nick, everyone else, does that sound okay?


text: Yeah. Sounds great.


text: We're


text: Great. So one small thing. I I just have the worst handwriting in the world.


text: So, rather than trying to capture the important things that you're seeing, I have a secure encrypted system here that's


text: just gonna be making notes if you talk about a pain point or success criteria. It'll pull that out


text: just help me organize the thoughts for the meeting. We'll get rid of it as soon as the meeting's over. I just want to make sure that I


text: hear everything you say. That's why I'm here today. Makes sense?


text: Great.


text: Gosh. So very briefly, I introduced myself, but


text: tell you about rhymexic, it's a huge coincidence, our we actually have an audience here from


text: engineering program at at Stanford where he met Lilly who's also from there dropped out of the PhD program to help build this company since 2022.


text: And Brooke as well, also from a linguistic background like Lilly,


text: And we commented a little bit differently. So rather than sort of pure


text: computational AI approach, have a lot of folks doing that too, but we were


text: linguist first and foremost. And so we actually started by building a recording studio.


text: So some of the other companies in the industry will use


text: pretty much the same data set of podcasts or actors, and it actually works well, you know,


text: if you look at the results, there are very strong competitors out there.


text: We think our approach is differentiated by spending thousands of hours having real people off the street, have natural, like full duplex conversations. You'd be


text: shocked at the results that can get. And I think we actually have a fair amount of proof


text: to that effect. We're now doing tens of millions of calls every month


text: some of the largest restaurants in the country. Healthcare organizations, telecom and enterprise.


text: Some other data that's just very briefly before we get into


text: the heart of the conversation about what's going on at Assurant,


text: We recently did a 100,000 phone calls. Well, we but there's a


text: impartial third party that we commissioned to do that. They did about a third of them with our


text: technology, then they used two of the competitors as well.


text: The big headline is no one likes receiving cold calls. So there's a lot of hang ups on on humans and


text: even best in class AI.


text: But there's still some some really important differences. And when you see it,


text: the scale here, these small numbers actually add up. So we actually have the lowest hang up rate out


text: 30,000 calls to


text: Compared to the other ones, people were less likely to hang up on us. They were the fastest


text: to actually engage in


text: through the surveys, those that responded positively. And, we


text: I think that speaks volumes.


text: Perhaps more relevant to your business, a different type of data is just some of our large customers.


text: We could


text: potentially share some case studies on these with actual names, but


text: 25% increase in call containment.


text: Was a a big one that we've we've noticed usually with a telecommunications provider, 20% increase


text: sales, Nick. So so you're writing there. Does this seems somewhat relevant or interesting?


text: Yeah. I think, like,


text: it's relevant. I think that for us,


text: we're also trying to figure


text: out,


text: right,


text: you know, like, more of an apples to apples comparison. Right? Like,


text: we


text: are


text: somewhat nascent on our voice AI journey


text: We are still, like, in this transition, like Walt mentioned, from


text: call centers to voice agents. Right. In the middle, we have this,


text: IPR that we


text: deployed today. That's kind of like a smart IVR.


text: But


text: yeah. So definitely development, like everything you've walked through and, like, that's


text: why we're here. And so I'm just writing notes for myself.


text: Cool. I'm glad that we're on the right track. Mhmm.


text: Question kind of on


text: before when you're talking about your data collection.


text: I imagine my people just having kind of


text: conversations, like that's one kind of speech too, but you know, when you think about the kinds


text: talking


text: to somebody in the call center,


text: that's a different kind of


text: know,


text: Yeah. Style. Is that also stuff that you guys collect as well or


text: So I'll I'll talk a little bit later once I actually let you hear to some of our


text: how we we think about this. But for example, for our latest model, CODA, we went through


text: had a thousand potential voices that were generated from the process that I described, and then we've sort of pair matching to sort


text: break it down into certain categories that would be hyper relevant


text: to you. And then we have a tool, through our UI that allows you to expose obviously the ones that we've whittled down


text: can then pick if you want a formal or professional


text: American versus other different languages


text: So there's quite a sophisticated process behind it.


text: I can always have an expert get back to him a little bit new here, but that's sort of my high level understanding.


text: Gosh, so let's just dive in. Nick, don't know if you wanna


text: start just to you mentioned you have a solution. Are you able to share the solution provider's


text: today through NICE who your TTS provider is?


text: Yeah. I mean, I think Pat should share a little bit more, but, like,


text: we currently use NICE's cotton energy product.


text: Sure.


text: Someone familiar


text: with


text: that. Is it there?


text: It's like


text: I think they bought it a few years ago. We've been a nice customer for a


text: long time.


text: But we wanna create, like,


text: fully conversational voice agents for us.


text: The experience is still like somewhat like deterministic and


text: static and so we started to do like some use cases


text: fully, like, identity, but still it's, like, only scratching the surface.


text: Think from a modeling standpoint, Patrick can keep me honest,


text: but I believe like we're just using Microsoft models


text: like


text: hyperscaler because


text: They're an amazing company, but their models are not


text: generally considered stated there. So, yeah, that means there's


text: there's definitely some some quick ones there. From what you've heard of the the ones you're using


text: so far, I I I just noted again voice quality, but what's


text: important to you there and why does that matter?


text: Yeah, mean, from our perspective, like,


text: I mean, look, it's no surprise.


text: Like,


text: Microsoft voices sound


text: good enough.


text: Like,


text: think they don't they don't sound great. We've had some


text: customer complaints and things come


text: in,


text: but You've had


text: customer complaints about the voice quality.


text: Okay?


text: Yeah. It kinda showed up in


text: some of the,


text: like, surveys we run, but


text: look, like, it it's working. And I think one of the things we really care about is, like,


text: accuracy and,


text: we've really dialed in the pronunciation and stuff like that with Microsoft. So, like, we feel


text: like


text: any change we would make, we would need to make sure that, like, that accuracy is maintained to


text: You know, when a child learns a new word, they're often only told once.


text: And they can start to replicate that. So that's something our team is sort of


text: had in mind when we created our system. And certainly with a missed one of our


text: cutting edge models, it's not coded, but it has a lot of advantages as well. You can teach it once


text: with throughout the system we built, and it will pronounce things extremely precisely according to


text: their proper pronunciations. They're minutes on an


text: API.


text: So the the sound quality or the pronunciation is very important to you.


text: I think from my perspective, especially building these systems and, like,


text: our team is kind of not only in charge of


text: the initial implementation, but, you know, inevitably, like, something comes up on behalf


text: of the one


text: that goes and fixes it.


text: And then you have sort of a way of measuring sort


text: of


text: WER and that sort of thing, a word error rate,


text: anyways? Yeah.


text: Yeah.


text: I think, like,


text: I mean, just candidly, like, this


text: is still a very kind


text: of material


text: process, and I've got somebody on my team that's, like, going through. And I think, you know, I just spend a little bit


text: cautious about moving from the kind of static


text: tree where everything is varied


label: Voice 6

text: kind of fixed into more of these agentic solutions. But I know this is where


text: we're gonna


text: end up. So I just wanna make sure that


text: as we migrate from where we've done a lot of manual testing to the


text: things get a little bit out of our control, then you can kind of


text: help us think


text: through


text: But I don't think it'd be a bit retrend in suggesting anything, but certainly, Myst is


text: kind of our more precise tool at the moment and Coda is getting precise and has


text: most human voices. So there are some great options for you with


text: both.


text: Arianne, anything to add in terms of the Asia we wanted to


text: you know, would really make a big improvement


text: for your new program.


text: Yeah. I mean, think like,


text: you know,


text: I was saying we have this 2,000 human agents who really


text: you know, we try to pass everybody on


text: to


text: one of the human agents eventually. And so, like, for the system that


text: we're


text: building.


text: So there there might be a


text: they would speak with an AI and then eventually maybe be transferred to the expert.


text: Exactly. So, like, we're just trying to keep them


text: on the the genetic part of the foot for as long as possible, we can kinda gather all the information.


text: That we then pass on to the human agent. And so if they're collecting


text: you know, speak to an agent earlier, right, then the human agent has to, like, take on that work.


text: And well, no one likes more work, but then in terms of the the bottom line to the business, why does that matter?


text: I mean, it's just kind of capacity, right? It's like


text: Oh, gosh. So someone I would have to be potentially be on hold if I'm not putting words in your mouth.


text: Yeah. Put on hold,


text: like, that stuff might be toxic


text: to you. Or you just have to staff more, and then I guess that's


text: Exactly. Yeah. Yeah. All those reasons.


text: So some of the success criteria would be


text: minimizing the or maximizing the the the time the folks


text: spend giving information to the agents and minimizing the amount of time folks Yeah. Are talking to me.


text: Yeah.


text: Success metrics or task completion and call


text: completion and


text: Task


text: completion and call containment.


text: Yeah. That's


text: what I meant. Thank you.


text: Okay. I


text: figured


text: it out there.


text: Walt, sorry. We we've kind of been neglecting you here.


text: A lot of folks in the room. Anything you'd like to add before we kind of dive into


text: some more specific examples of why Rime might be able to help with some of the the problems we've identified today?


text: No. I think it's okay.


text: I think


text: my team is just


text: of


text: getting laid off.


text: Or or put put one I see, like,


text: angry customers arriving


text: on


text: wants to ask. Right? Like,


text: we don't want a bad experience of some of these emotional having conversations be


text: super frustrated by the end of the voice agent conversation that


text: Yeah, no, mean, I I I certainly we


text: we


text: not involved in in any of that, but what what we are involved in is


text: hearing and doing studies on some of these conversations and


text: having worked in a few AI labs, a rhyme is one of the first places where where folks will actually hit


text: thank you to our 10% of his rent


text: was being asked for him because he lost his temper because the AI was so bad that I


text: assure you that was not our


text: system.


text: And, yeah, I


text: I reached out to the meeting and said, like, this is not, you know, what it the future.


text: So we we care a lot about sort of the


text: the AI human experiences.


text: So if you mind, I'm gonna try a a live


text: demo


text: We can always go back if unless there's any sort of pinpoints that I didn't capture here. It is


text: important that


text: Well, I think just like the


text: infrastructure side to, like,


text: we


text: you know, operate in a compliant environment and know what are the benefits of using.


text: The Azure solution is, like, we're in the Microsoft data centers and so


text: just wanna understand, like, the different options for for serving


label: I'm

text: Sure. I'm I'm


text: not


text: super technical myself but I do understand that we have


text: APIs available in different regions. But if you wanted to use a cloud that we're not natively on, we have


text: a


text: full on prem deployment that's actually


text: but it's


text: semi automated in


text: how it's


text: it's managed, and that would absolutely, with a simple Docker container, be


text: available to you on Microsoft or on your own


text: hardware or lots of different ways to do that


text: extremely securely, SOC two Type two working with some of the largest most of ending companies in in the country. So we're definitely


text: that road before. Any other questions from a technical perspective?


text: I think so. I mean, like, how are they kind of just,


text: or what I hit or other type kind of metrics, like latency and


text: I'm also latency is certainly one place that I hadn't memorized the exact stats and some of the benchmarks


text: change day by day, but we're we're towards the the one of the fastest models in the world at at the moment.


text: I I don't


text: think that's the


text: the only thing. If something's really fast and doesn't sound great, then


text: but we we I I think you'll you'll see for yourself in a moment. We are both fast


text: responsive, and I think very human.


text: Cool.


text: I'm excited to get them done.


text: Okay.


text: Was just calling my


text: dentist. Let me try this one.


label: Voice 7

text: Hello, and thanks for calling NorthStar Device Protection.


text: Is a demo, and the call may be recorded.


text: I'm


text: NorthStar's virtual claims assistant.


text: How can I help you today?


text: Hey, good afternoon. I'm so sorry to report that I have a


text: problem with my iPhone. I'd like to apply for warranty.


text: I'm sorry to hear about the trouble with your iPhone.


text: Assist you with the device claim, I first need to verify your identity.


text: Could you please remind the five digits ZIP code on your policy?


text: Thanks for that, Colin. I know we'll check your policy now to


text: see what coverage you have for your iPhone.


text: Colin, your active policy includes


text: accidental damage coverage for your iPhone 16 Pro.


text: To start a claim, when did the damage


text: happen? I think it happened on July 11.


text: So embarrassing. Thanks, July.


text: So, what were you saying?


text: Was the


text: damage accidental, like dropping or popping the


text: I was getting ready for this really important presentation.


text: I was so excited thinking about these folks I was going to meet and I was building this phone app and I just


text: I knocked the darn thing right onto the floor and cracked the screen.


text: What a dummy I am.


text: It sounds like it was accidental damage then.


text: Did your iPhone still power on and work despite the cracked screen?


text: It sure does.


text: Thanks for confirming that your iPhone still powers on.


text: Do you still have the device with you now?


text: I do.


text: Just to summarize, you had accidental damage on July 11. Your iPhone is still power gone.


text: Is that right?


text: Yes,


text: ma'am.


text: Your claim submitted with claim number CLMDemo2049


text: Sounds like you're


text: gonna put a


text: Sorry. Didn't mind him. What were you saying?


text: I'm saying I can send a secure document uplift link to your phone to help finish the claim if you'd like.


text: Like.


text: Would you like me to send that now? Or is there anything else I


text: can do for


text: you? Yeah, you can send that.


text: You said usually you transfer at this point to a like a


text: Oh, don't know if our system can do that.


text: If you need me more talk to a specialist,


text: just let me


text: know.


text: Yeah. Can I just can you transfer me to an expert?


text: Alright. I'll transfer you now to a device claim specialist who already has your full summary.


text: One moment, please.


text: Hi, Colin. I have your full claim summary


text: here.


text: How can I assist you with moving your claim forward today?


text: Est-ce que vous pouvez m'aider en français s'il vous plaît


text: Bonjour Colin, j'ai marqué votre réclamation pour dommage accidentel


text: sur iPhone sales pro a bien été soumis le haut juillet.


text: C'est tellement gentil, mais je pense we get the point.


text: Merci et bonne après-midi au revoir.


text: Yeah. I apologize. I didn't wanna use your brand without your permission, so we


text: created Northstar for that purpose. But


text: how was the voice quality?


text: Voice quality is out of reach. Yeah. I'm curious how you did the


text: language switching. Like, how did


text: you navigate that?


text: Well, we we have folks that just as I was spinning, like, our first office had to


text: recording studio in it.


text: We we have folks that are doing recordings for us overseas. So we have native French speakers and


text: quite important that you have the speakers speaking their language else. The English model


text: can the voices can do it, it does not sound, like like she does.


text: And so the live kicks and the logic they're


text: the knowledge to switch when it picks up in Deepgram, our STT provider,


text: identifies language and


text: we go. We support something like eight languages, not the most, but


text: I think certainly for this country, we we have a large amount of coverage, and we're always expanding it. And if there are other languages


text: were very important to you, that's something I'd always take back to my team.


text: I guess, like, what what are the next steps for us to, like,


text: try and like, I'll tell you on our side,


text: The purpose of today is just to like understand


text: a little bit more about rhyme. Right? Think I hear the demo, which sounds great.


text: Think the next step for us is just


text: like,


text: how can we get started and bring more awareness and


text: test out the models in our environment.


text: So


text: this is a just to skipping ahead here because you asked, this is kind of what that would look like.


text: You know, as much as we care about and we're happy to show you our latency and the


text: know, word error rate


text: I think in in our experience, a really successful pilot can actually would would work


text: once you're confident on a small amount of real production calls.


text: This is what our our customers generally do.


text: And we would set success criteria out in advance. So


text: for me to tell you what what matters, but looking at the the notes here, it looks like we have


text: you know, call completion. Maybe it could be something when we sit down to scope a call or


text: you know, the the


text: sort of brand pronunciation.


text: Really, what whatever says


text: there are important to you, we would just scope out over whatever period of time. Nick, do you have a


text: time period that would make sense?


text: I think for us, like, we're looking to make a decision over the next


text: like, three to four months. Now, I think there's a


text: couple phases


text: for us. Three to four months


text: for next steps. Okay.


text: Yeah.


text: I think, like, in the interim,


text: we


text: have, like, a


text: dev environment that's, not a production environment, and we'd love to be able


text: to implement rhyme and test just like


text: with our own ears how it sounds and how it integrates


text: Like how can we get started in the short run just like


text: getting it set up in that test environment?


text: Yeah. Obviously, this is sort of sequential and a


text: wouldn't ask you to put something in even in a small production run until you've gone through that phase. So


text: you


text: know, we're also we we host APIs on on Amazon, so you could be up and running in


text: five minutes, technically, maybe a little longer with NICE, but we have a native integration with NICE.


text: So that's still SOC two type two.


text: Depending on your security requirements. So we can actually maybe get into that


text: a little bit later, but


text: what is kind of the the process for for going


text: even into a a pilot, or how how complicated is that to get approval even to user APIs and


text: the the dev environment?


text: Yeah. I think, like,


text: step one to just get, like, a demo set up right Mhmm. In a


text: dev environment is pretty


text: easy. Oh, great.


text: Try selling to a French bank.


text: Not easy. No.


text: Definitely not. Or US


text: So


text: like we have the authority like


text: we have to do, like, some baseline security checks,


text: right,


text: on,


text: you know, your your documentation and whatnot. But, like, we'd love to start testing


text: your call API Mhmm. In some constrained demo environment.


text: And get it set up.


text: We love test.


text: And then just being able to allow people to, like, hear it. I don't know if


text: you have


text: suggestions on voices to use.


text: We our RRI is


text: one of our experts there, and he can


text: help guide


text: that. So he still can't So,


text: that's to me, like, we wanna figure out what we


text: is of the best. We want to test it and, like,


text: I think we wanna lean on you guys to help us figure out, like, how do we set up the best


text: demo test environment Mhmm.


text: Then, like,


text: you know, that's step one, right, for us.


text: Does it sound good in our own, like, environment? Like,


text: to our own ears, right?


text: Step two is we want to understand what does it take technically to roll this out


text: on prem


text: for some pilot period.


text: Right? And probably, you know, Patrick and his team to kinda, like,


text: take that action item, that like, next step.


text: And then I would, like, work with


text: artists involved on the commercial side to figure out, like,


text: kind


text: of


text: That


text: that all sounds good. Yeah. We're we're pretty experienced with


text: process. Tell me though, Ed, you said you're using Microsoft at the moment. Did they go through this whole process with you? Did they help you


text: sort of evaluate which of their voices was best or how hands on was Microsoft?


text: They kinda suggested one oh, I should we go with, but, you know, it's


text: we just thought it because


text: I mean, listen. No no one ever gets


text: from


text: for buying Microsoft, but I'd like to think of Rime that's, you know, we


text: have a more crafts person approach. It's one of our values and we take a lot of


text: pride in


text: sort of customer service and we're quite accessible, even myself. I


text: cover many different territories and usually most hours of the day


text: available with the Slack or text.


text: Okay. Can I actually get your number now while I I


text: just mentioned it? Or


text: Let's wrap, and then I can I can give you my


text: Great? No. It's good that I guess in my my line of work we call it a champion, you know,


text: just one person that I can reach out so that can always be accessible


text: anytime there. And I


text: Yeah, for sure.


text: As any other kind of stuff we


text: should be aware of down the road, things we can start preparing on our end. Whenever you ask us for a


text: security review or anything, we should, have any big surprises.


text: I think the general, like, security stuff. Right? Like,


text: I'm assuming you've worked with other, like, financial


text: services. Airlines, financial services. Yeah. So


text: talk to all the data


text: productions or requirements.


text: I don't think it's something I have at the moment. We have this, like, GenAI form that we have to fill out to, but


text: that's


text: just for any


text: Yeah, look, unfortunately, the nature of the TTS system, especially one provided by our partner in Orchestration Layer,


text: very familiar with, it connecting is just not


text: technically complicated. Certainly,


text: maybe


text: adding some of the terminology that you might want. There's a little bit of technical stuff there.


text: Picking the the But for as as implementations go, I think you'll


text: be


text: pleasantly surprised.


text: So


text: I guess just to kind of wrap up what we've talked about, it sounds like you do have some


text: major limitations with the Microsoft Voices that the call completion is a problem. We have some of the others


text: written down that the experience for your employees


text: and the staffing levels, the cost involved,


text: significant and there's a lot of areas to


text: improve there.


text: And does it sound like Walt, maybe I haven't heard from you lately.


text: What a lot of what we've discussed today could could address those concerns, or is there anything that left


text: unaddressed?


text: No. I think all the


text: Makes sense.


text: Any other questions at all Walter? I


text: know if you read about the industry. I'm happy we have just a couple minutes left on the the time allotted. I wanna manage that.


text: Carefully. But


text: anything at all,


text: I'm I'm here. Otherwise, you know, if you'd like, to


text: Nick, do do you know what your team's availability is, say, next week for a a scoping call?


text: I can check my calendar. I think I've probably


text: Wednesday or Thursday.


text: Happy to accommodate your schedule,


text: or


text: open.


text: I just moved from France.


text: Awesome. How's France?


text: Hot.


text: And excited for


text: their birthday


text: today.


text: National birthday. Right. Yeah.


text: That's exciting.


text: Cool. Well, thank you. Yeah. Mean, I'm happy to to


text: give you a a couple minutes back unless


text: already it's Patrick, there's anything at all that I failed to address or


text: last minute questions, weird voice AI stuff you'd like to know about?


text: No. Sorry to


text: dig in


text: more difficult.


text: Absolutely. Looking forward to it next Tuesday or Thursday.


text: Perfect. It's been a pleasure.


text: Great. Thanks. Cool.