label: Colin Heilbut - Rime

text: But, first, just so I have a little bit of context, would you mind, introducing, the


text: three of you? Just your


text: your names, your roles, and, you know,


text: the relevance your voice AI has to your work.


text: Nick, so you lead, go to market here at


text: Aren't aren't you from Assurant, though?


text: No. No. You're you've, you're doing the opposite. You're gonna play the


text: three roles from Assurance. I'm gonna play the AE from Ryan.


text: Name is Colin. I'm the founding AE.


text: Nick, director of conversation AI. Got it.


text: And okay. So


text: sorry, Sarah, go on.


text: One?


text: So, Sarah, you lead the engineering, and Mike, you're from the contact center.


text: Sarah from the contact center.


text: Got it.


text: Can you please run the exercise


text: But, again, you're gonna be the three people from Assurant, and I'm gonna be from Rye.


text: So I'm selling to you. You're the


text: listening to my demo.


text: So Sarah from marketing and oh, sorry. What were the three roles?


text: Hi, I'm Sarah from marketing


text: Hello. I'm Mike from the contact center.


text: Great. So


text: now that we've been introduced, here's a brief agenda. You know, I wanted to just


text: talk about the


text: today in a special tool. You might have seen me using a digital whiteboard that


text: very briefly talked about what makes rhyme special.


text: At a high level, how we differentiate ourselves from other TPS


text: you may have seen before. Mostly, I just wanted to focus today on Voice AI and


text: insurance, and I understand you met some concerns about


text: voice quality and a lot of things. I'm looking forward to diving into there and understanding any other


text: challenges or interests you may have. I did prepare a brief


text: very brief live demo of, that I I think should be quite relevant


text: to your work and you know throughout the whole conversation I'd just love you just to


text: stop me at any point if you have questions or we have time for questions at the end


text: then as well potential next steps if that makes sense.


text: Oh, I just wanted so this is the digital whiteboard.


text: This is this just listens to what we're saying. It doesn't


text: do a permanent reporting or anything like that. We


label: Voice 2

text: wanna


text: captain, you know, pinpoints or


text: potential success criteria we might wanna measure, things like that. People just note them here


text: for more records, and I just have the world's worst handwriting. This is my


text: solution for that if if that's okay. Hopefully, we find it helpful.


text: Great. So you're saying the first pinpoint?


text: So how is voice quality


text: what are the problems that voice quality is causing, and how do they affect those two seconds?


text: So drop box


text: affects what?


text: Containment, NCSAT.


text: Got it.


text: And what are the other problems?


text: So pain point is that the current


text: the voice quality leads to poor task completion.


text: Okay. And


text: so the the first one there that with the the CSAT issue,


text: how would you what would be the success criteria for solution there?


text: So


text: CSAT improvement, what's what's a significant CSAT improvement in your books?


text: So color sentiment, do you have any sentiment analysis tools at the moment?


text: So you do have a sentiment analysis


text: tool at the moment, so that would be one way to measure. Now what about the task


text: completion?


text: Just let me, bear with me a moment here to start getting catch up. It


text: just really important that I I track these things. No point in discussing it.


text: If we don't track it.


text: Great. So


text: what are your pinpoints about voice quality or anything else with voice AI you'd like to discuss with


text: Sorry. You said


text: latency is another problem in pronouncing grand and voice


text: What was it?


text: Grand and product name pronunciation.


text: Yeah. I do that too. So, you'd like it to be able to handle people,


text: jumping in very quickly.


text: So the


text: the the voice quality issue, how did we say we would measure success there?


text: Task completion.


text: And you said another pain point was


text: the grand pronunciation.


text: And how would he where


text: is that, like, a a five, a 10? What's the severity there?


text: And pronunciation?


text: What about latency?


text: And the containment to CSAT issues.


text: Got it. Now, any other pinpoints, we should, be aware of before I I show you,


text: how Ryan will typically help with this sort of thing?


text: He is at the point. So you've you've had issues in the past with with, he's at


text: deployment. So ease of deployment to the endpoint.


text: Yeah. I mean, that one, I I can speak to right now.


text: You know?


text: We were actually compatible out of the box with NICE We have a great relationship with them. And so it's really as simple as a


text: toggle. I mean, if you wanted to have us hosted in


text: alongside your Uber and


text: orchestration stack, that's also not a problem. At least just send you a docker file,


text: and that's completely productized too. So it's, you know, you


text: set up and and running same day.


text: Anything else before we we dive into how we might be able to help with some of these things?


text: Let me tell you a little bit about who we are. We were actually started in 2022 by, Lilly and Brooke and Ari.


text: The the way that we come about


text: the pop is a little bit unique, you other than using the same datasets with podcasts


text: and actors.


text: We actually started by building a recording studio. Fact, we have multiple now around the world.


text: And this enables us to have much higher quality training data


text: we produce with real that speak


text: actually


text: now powering.


text: Millions of phone calls every month. First, some of the biggest restaurant chains, healthcare groups,


text: Another bit of relevant data that you might find interesting, we just did a


text: 100,000 call survey through a, an objective third party. We sponsored it. They did it.


text: And we, you know, no one actually loves receiving a


text: cold call. In this case, it was for a politics survey. But


text: and, you know, even humans will have a


text: high, hang up rate. However, our hang up rate is significantly lower.


text: Than any of the other AI. We also had


text: the


text: fastest time to


text: so we were the easiest to talk to.


text: But I think that's a fair bit of data there after a 100,000 calls. These


text: you see that at my point?


text: We also


text: have some data from some of our our large


text: large companies. So we had a 25% increase in call containment from one of the largest telecommunications providers in the


text: country and a 20% increase in food sales from a national food brand.


text: So we are really seeing results from our approach these days, we're


text: we're happy for you to consider us.


text: Let me actually


text: show you rather than


text: tell you. I'm gonna start just


text: calling a


text: a demo, now.


text: You can just pause chat, JBP.


text: I'm here to help whenever you're ready.


text: Just let me know when you


text: to do next.


text: Hi. Yes. I'm so sorry. I just,


text: broke my phone, and I I need to use my extended warranty. Hi. Hi.


text: I'm sorry to hear about your phone.


text: To help you with your extended warranty, can I please start by verifying your identity?


text: May I have your ZIP code?


text: Thank you. Me check your policy coverage for the device.


text: One moment, please.


text: Colin, your policy is active


text: and covers accidental damage. To start a claim, may I ask who's data here?


text: IPhone 16 Pro


text: happened? Oh, gosh.


text: Was July 11?


text: Thanks, Colin.


text: Was the damage accidental, like dropping or bumping the phone?


text: Yeah. I mean, totally. I I was just getting ready for this really big presentation. I'm


text: so excited. I was gonna meet this amazing team, I was trying to present this


text: iPhone app. And so when I took my phone off my desk, it just followed my hands under the floor and cracked


text: screen.


text: I understand. Just to inform the damage is back to now.


text: Does the phone still power on and work?


text: Despite the screen damage?


text: Yes. It does.


text: Got it.


text: And do you still have it done with you right now?


text: Yes. I do.


text: To summarize, Colin, your iPhone 50 Pro is


text: damaged on July 11.


text: Bill powers on and works, and you have the device with you. Is that correct? Yes.


text: Your claim number is CLMDemo2049. Deductible for screen repair is $29. I can send a


text: secure upload link to the phone number we have on file so you can finish your claim. Would you like me to do that?


text: Yes.


text: I need some to secure upload link to your phone number ending in


text: 0148. Is there anything else I can help you with, or would you like to speak with a device like specialist?


text: I can speak to a specialist. Thanks.


text: Owen Colin, before I transfer you, I just wanted to say good luck with your big presentation.


text: Today.


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


text: One moment, please.


label: Voice 3

text: Hi, Colin. I have your full seller here about the accidental damage


text: to your icon.


text: 16 Pro.


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


text: Bonjour monsieur, comment allez-vous


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


text: plaît


text: C'est tout que j'ai besoin. Merci beaucoup et bon bon après midi.


text: Bye.


text: So how did that sound to you?


text: How was the latency?


text: Any other feedback on that? Does that


text: seem relevant? Just for a bit of context, this is the voice that I picked


text: We went


text: through for


text: more than a thousand voices and put them through pretty rigorous comparison


text: process. It's a little bit beyond the scope of my expertise, but


text: we


text: really it down to the ones with with the most


text: performance that that


text: people really respond the best to and


text: then categorize them accordingly. And then we provide a tool to you to


text: allow you to then


text: pick


text: the


text: voice or


text: voices that most meet your needs.


text: Some folks will


text: do it based on geography or demographics. Lots of different options there as you can see.


text: You


text: know, just a starting point to demo choice, not a production recommendation,


text: but


text: do you have do you have any


text: thoughts


text: on which voice would feel right for sure?


text: Couple more. It's really quick question.


text: Here. You know, we have a standard


text: process.


label: Voice 4

text: Generally,


text: if if you are looking at a pilot,


text: what does that decision making process look like on your end?


text: And then are are there other teams that we'd want to involve down the road?


text: Deparment and security and looking at, this sort of thing.


text: What's a realistic contract here?


text: Got


text: it. And then how would you determine if this this pilot is successful?


text: And then who else at least signs off in our


text: a project like this?


text: Got it.


text: So


text: you know, quick recap is, again, it looks like there's, latency issues.


text: That affect voice


text: pronunciation and response on


text: in a nutshell, this is something that we we deal with along. We have some


text: the lowest latency rates


text: both Mist and Coda. Mist is a little bit faster. Coda is a little bit better, but but both are


text: amongst the fastest in the


text: industry.


text: Voice quality problems


text: This is for containment in CSAT, and


text: again, I can't sit here and show you an increase in your your CSAT, but I think


text: we we can see logically how


text: we've had those results with a lot of our other large customers, and we'd more than happy to demonstrate that


text: ourselves. And then, you know, ease of deployment,


text: again, we can do more technical dekive in another call, but


text: is it should just be it could be as simple as as an hour to


text: to get


text: system working, more of a more complex employment, perhaps one day of work.


text: And, you know, hang up rates and call containment is


text: are things we we can talk about when we sit down to potentially


text: to plan a pilot. But, you know, these are all things we're really happy to prove.


text: So


text: before we we talk about final next steps, so any other questions, concerns,


text: at all that you might have?


text: So, Koda actually pronounces all, los t e


text: which can be active


text: out of the box just given the


text: amount of data and experience we have by this point. To remain specific pronunciation,


text: can't be


text: set up in quite the same random or fashion stays in Ken with Missed.


text: But we do have workarounds for that today. And then Koda in the fall will, have


text: full a full sort of inline pronunciation tool.


text: I I don't know. I I


text: we're very much aware and interested in speech to speech. I I don't we've made any


text: public announcements yet, but certainly we have our eyes on it and


text: you can imagine, given our interest in AI, it it's certainly not outside the


text: possibility that there are researchers that have as well.


text: Cool. Well, there's no more questions, let's just end on


text: a brief explanation of how we generally do highlights. You can see that here.