Ep.234 – Everyone Is Selling AI Chat — But Voice Is Where the Deals Are | Quiq’s Mike Myer on Multimodal CX

Josh Lupresto (00:00)
Welcome to the podcast designed to fuel your success selling technology solutions. I’m your host, Josh Lupresto SVP of Sales Engineering at Telarus and this is Next Level BizTech. Everybody, welcome back. Today we got a good one for you. title of the episode today is Everyone is Selling AI Chat. Voice is where the deals are. On with us today, Mike Myer founder and CEO of Quiq Mike, welcome on, man.

Mike Myer (00:27)
Thank you, Josh. I’m thrilled to be here.

Josh Lupresto (00:30)
We we we got some good stuff to talk about and we’re gonna get to the Quiq platform here in just a second. But I always love to hear everybody’s story, who you know, where you came from. So before we get to Quiq and everything and we start unpacking that, who is Mike? How did you get here today and and what what sucked you into this weird technology world?

Mike Myer (00:50)
so I’m an engineer by trade, started my you know, master’s in computer science, started my career in computer science, and somewhere along the way, I think I probably figured out that I love technology, I like solving problems, but I’m probably more of a builder than just a a pure geek. And so at a certain point in time I decided that like I can actually potentially build more if I’m not the one with the key my hands on the keyboard, but I’m actually like running a company and

I have a bunch of people who are working together and we’re building together. And so that’s what we’re about here at Quiq You know, I think companies have different DNA. You know, some companies are like marketing companies. You might think of like Salesforce. Some companies are like sales companies, maybe like Oracle, and some companies are like technology companies. And I put ourselves in the in the technology category where we’re focused on building a great product that works well.

And the relationship we deliver to our customers, both from the product as well as the people, it leaves a lasting impression of a a positive nature.

Josh Lupresto (01:55)
So let’s talk about Quiq Let’s let’s unpack this. So for anybody that’s not familiar at all with Quiq walk us through what the business is and then, you know, a as it got created, kinda how it evolved from this messaging into this fully agentic platform.

Mike Myer (02:11)
Sure. So fundamentally what we do is Quiq is a conversation platform. We handle conversations between consumers and businesses. And those conversations could be either handled by AI and a fully agentic AI, and I’m sure we’re gonna talk about that quite a bit more in the conversation, but it also could be handled by a human agent that’s working inside of the Quiq platform, and then that human agent is assisted by AI. And then when the the conversation’s over,

The platform also includes the ability to analyze the conversation, answer questions, understand kind of trends, understand like how effective your AI is, etc. Because analytics is also crucially important. You know, everybody who runs contact centers, especially kind of like you know, ten ten years ago, contact centers was all about like data and and performance management. And it’s there’s the underlying theme is still there. It might be AI agents, but you still need to be able to measure those as well.

How we got here, you know, our business started with a focus on building the next generation contact center. And that was, you know, phone and email were channels around for a long time. We started building kind of like the next generation of the contact center based on asynchronous messaging. And so we did that. And then actually before COVID, before like you really needed AI in the worst way,

We started to automate those conversations with kind of generation one natural language processing and things like Amazon Lex and Google Dialogue Flow. And we could build some really effective conversational AI experiences in that generation, but it felt ro really robotic. Like you couldn’t, the the answers were always gonna be the same to the a particular question and the

the understanding of what the user said, like they had to respond kind of like almost on keywords to in order to have the understanding work well. And then generative AI came along and we were in a great position because we had this platform that had prior generation AI. We were able to plug the current generation into that and suddenly we had this thing that could generate bespoke responses. it could understand very complex drive like

rambling explanations and it could reason it could go and understand kind of like the facts of the c user situation, what the company’s policies and and internal systems said, and combine that all together into like a very cohesive, thought well thought out response. so it’s been a a a great ride. You know, in the conversational AI space, we’ve been deploying conversational AI that’s customer facing now for like three years.

And along the way we’ve learned a lot. we’ve gotten much better at the experience. and also we’ve enhanced the platform to include voice. And it turns out voice is the most challenging channel to communicate on, not just for humans but for computers as well. And so we’ve had to do a lot of work. luckily we had a solid under under underlying foundation for the AI.

but we still had to do a lot of work to get things like interruption handling and responsive and and make make it as close to as possible what a human agent would sound like on the other end of the

Josh Lupresto (05:40)
You know, I I I I like your point of I think there were a lot of people that saw GPT three five come out and go, okay, now I gotta go build something amazing. And and then vibe coding became a thing and it just looked anybody could build anything. And meanwhile, you guys are sitting here going, Yeah. guys, it’s taken a long time to build the platform the way that we want it. Now there’s just an incredible

just i incredible modernization and innovation that it’s allowed you guys to really unlock what what you’re sitting here going, we know how to do these things. And and as the technology got iteratively better, now it just got kind of exponentially better. And and you’re you’re right. I mean, I it’s as much as people want chat, I think it chat is important, but there’s just to your point, we we were talking about this before we got started. I think you guys do a good job of meeting people where they are.

And not forgetting that voice is such a critical medium. So maybe, maybe, maybe that’s where we take it next. there’s a there’s a myth. I want to maybe bust a myth here a little bit. So there’s this thought, okay, all this technology, it’s so easy to build. Anybody can build anything that the chat deflection is kind of table stakes, right? I don’t even know the deflection is the right word, but you just you want to,

Mike Myer (06:49)
Resolution.

Josh Lupresto (06:50)
yeah, you wanna get there we go, you wanna get the resolution, serve the people the right way. So

You know, there’s there’s multiple technologies out there, let’s say that have an AI chat story. So if you know if the resolutions and that deflection and those types of things are doable, where does the real differentiation live in your mind?

Mike Myer (07:09)
You know, I think the the fallacy that you’re pointing out, like, you know, i there’s voice technology. So if I’ve got an AI agent that works on text, why don’t I just plug some voice technology on top of that and like the the voice technology will convert it into text and we can just run it through our normal chat bot and and get a response back. And and truthfully, we all used to think that as well.

And so like, you know, our generation one voice, and we’re probably like generation two point five at this point in time, but our generation one voice was basically that. And it worked, but it wasn’t as conversational as you would expect. And so things like when the the user started talking, and then they had a little bit of pause, like I just did there, did the AI start talking over top over them, and did the AI stop talking if they when they started talking again?

Or if there’s a b a little bit of background noise, what happened then? Did the AI like i interrupt them and start to respond to the background noise? And so getting the kind of like the natural cadence of of the conversation, understanding the the breaks, being able to efficiently like pick it up if you know if I did say, you know, again, this using this example, I’ll try not to get too too technical here, but if I took a break in the middle of the conversation and then I continued my point on.

You know, I had a had a second break there. what we are doing in the background there is we’ve actually we’ve detected that the the voice stopped, we started processing it, and then when the voice started again, we detected that the the voice started, and we’re gonna look at what is being said and is it can a continuation of the prior prior statement? And if it is, we’re gonna cancel the processing that we’re already doing on the on the prior and then wait until the next break and then process that entire segment.

as a new block of text that that needs to be understood by the AI. And so it’s that kind of stuff that it it is the the challenging part. And so that that turns out to be like a really hard because you need a whole bunch of simultaneous processing because on one side you need to be listening. On the other side you need to be processing to un understand like what’s going on and whether we should actually be i responding or if we should be listening for more. So there’s a whole bunch of s kind of like down in the weed stuff, but it’s it’s voice turns out to be

Super hard to get it responsive and and easy to interact.

Josh Lupresto (09:35)
Well, I think the beauty in that though is that you have y you know you kinda said this in the beginning, you’ve picked up so many learnings. I mean the last couple of years, if you’ve had a good starting platform and you’re able to get customers on this platform, which you have, you’ve learned so much. And I think sometimes these best

You know, we we’re smart, we’re engineers, we come up with ideas, but hearing what the customers actually want and what they’re willing to kind of move the needle for, what they’re willing to pay for, you know, the willingness to pay factor, I just think is so fascinating as you hear little things like that and you know, man, if I could solve this for one customer, we gotta figure this out and how to solve it ’cause everybody else is gonna want this. They might not know that they need it yet, but we’re gonna move into they’re not gonna know how they could do without this, right? And then that’s just gonna be the absolute

standard bar, I think as you go forward. So I’d love I love the breakdown of that. That’s cool.

Mike Myer (10:23)
I mean the other thing that’s interesting on on voice is as we’ve been deploying voice, we also have gotten quite a bit better at understanding what’s a good use for case for voice and how do you involve other channels in the conversation. Because you know, if I’m my flight has been disrupted and I’m walking through the airport and it’s given me three different options to reschedule my flight,

You know, I’m slightly distracted in the first place. By the time I’m done listening to the third choice, I probably forgot the time of the first choice. And so having the conversations be multimodal, which for us would mean the agent’s going to say, I can give you three choices, I’m gonna text them to you also. And so while you’re listening to three choices, you’re looking at the text and you got the it’s like right there. And so doing things like that authentication is turns out to be really useful because

and if someone’s like, I need to to validate who you are, I’m gonna send you a link, log into your account, and then we can continue the conversation. There’s a whole bunch of of things that making a conversation multimodal and involving text and voice together and into in in not just like sending one way, but actually being able to take input back from the the customer and have it like truly be one conversation that’s occurring simultaneously across multiple channels. what you can do in voice really kind of like

gets much better than than what you could do in the past.

Josh Lupresto (11:52)
Yeah, I wanna I wanna come back and we’ll I wanna unpack multimodal in a second, but I wanna put a little bit of a baseline down. So let’s you know, we we’ve got a lot of TAs that listen to this that have never done CX, that have been doing nothing but CX, or maybe all in cloud. it’s a it’s a you know big swath, right? So three years plus into this space, I wanna wanna lay down from maybe your perspective, right? As you and your guys and and gals are talking to customers.

You got a lot of you’re you’re you’re running into a lot of customers that have have tested out, done some AI pilots. Walk us through first what you see is running out there in production right now, what customers are buying, what they’re looking for as we think of that kind of upper SMB, maybe mid-market. We go to that, go to that place. What you seeing out there? And then I want to come back and unpack how you guys are solving a little bit of that multimodal side.

Mike Myer (12:43)
I think that w so what are we seeing today? you know, we we used the term chatbot earlier in the this conversation and I kind of steer away from that because chatbot is kind of like this thing that does not very smart and it just responds to the immediate thing that that you just said, like almost like keyword based. And so what are we seeing today? I mean there’s a lot of that stuff out there and there’s n I think the technology and the capabilities of the technology are leading

the implementations at this point in time. Like, you know, if if I ask you, you know, Josh, in your personal life, what’s the best AI agent you’ve interacted with? You might have to think about a little bit and be like, okay, this one’s okay. And you know, we we do demos where the AI agent is amazing. the the common customer experience is not at that level. And so like the capabilities, the technology are are there.

To do like really great experiences, experiences that essentially replace kind of your frontline contact center agent. And so what we’re seeing in the industry right at this point is an upgrade of kind of like those prior generation, not that smart experiences. we’re seeing a lot more of the combined experience where I have won a you know, a across channels.

I might have a an AI agent that’s deployed on my website. I have the same AI agent deployed on voice, and and we can do that because we can build once and and deploy multiple times. So we’re seeing a lot more kind of like voice and and digital simultaneously. we’re seeing compliance sensitive industries move towards AI. And so you know, in our customer base, we’ve got a a ton of experience with

Folks that are in like travel hospitality or consumer services or in in retail, and like Roku is is a client, or core hotels is a client. And those are the industries that are kind of like risk forward. Those are the folks that are who like implement new technologies first. in the last six months, healthcare, insurance, consumer financial, banking, like the things that have compliance sensitive, like

We’re starting to see the a lot of interest there. And those industries have traditionally been a little bit more voice centric than maybe travel, hospitality, and and retail. and so voice becomes like even more important to the the folks that have kind of not adopted digital and mass like other industries are. So we’re seeing a ton of ton of like, you know, I don’t want to say old school, but in some ways the the their contact centers are maybe a little bit more old school.

really making a leapfrog and doing that with voice.

Josh Lupresto (15:34)
What’s what’s the what’s the linchpin when you think about hospitality and retail like that, where i is it that those guys tested out a chat tool and that didn’t do it and they knew they had such a core business in voice and they just didn’t find anybody that really embedded voice and chat and kind of the entire experience. And so now i if you’re able to solve that, that makes them less concerned about the risk to say, well

Okay, they’ve taken care of all my voice things and it’s just in an easy succession into all the other technologies. Is it just painting the path for them and sitting here saying, Hey, we our whole platform, you you got access to all of this. Is that the it’s it’s just not scary for them because they don’t have to go outside the walls anymore once they move over to you? Is that that vertical?

Mike Myer (16:23)
Yeah, you know, I think that that there’s a lot of folks who are trying to understand like how their customer journey is not just like siloed in one channel, but actually like lives from channel to channel.

And so that that’s what n gets people excited is they’re they’re not no longer talking about like, you know, on on the website we’ve got this tool, on voice we’ve got this tool, for email we’ve got this tool. now they can be like, Well, it’s just one journey across all three and and the interaction can even actually move across channels. If somebody can start on a website, like we have a a

garage door opener company, large largest kind of a deployer of garage door openers and it comes with like an an app. And so when you’re debugging your garage door, like you have an issue with your garage door, it starts on your computer because you were probably on the website like looking at at FAQs and documentation at troubleshooting. And then it you’ve contacted the business and then the person says like can you tell me what the dip switches say on your control or something like that. And the person’s like, I need to go to the garage to see that.

Okay. Well, at that point in time, instead of like running out of the garage and then back to the computer again, take the conversation with you on your mobile device and so you know scan this QR code and you’ll be able to continue the conversation on your mobile from the garage.

Josh Lupresto (17:44)
I love that. it i again, it just goes back to we gotta meet, we can’t build it one way. We gotta meet everybody where they’re at. and i you know, it it just seems to tie that voice is at the epicenter of this. So i you think about all these things that go into a engagement like that. You got interruptions, you got latency, you got call, you know, voice, you got all these things. What what breaks when you traditionally put an AI agent

on a live phone call and then how do you guys solve for that?

Mike Myer (18:15)
you know, interestingly, the it’s it’s probably less about the the voice in the AI technology. The biggest challenge that we have in a lot of our implementations is just getting access to systems and being able to do the same thing that the the agents would do. Because, like, if you’re gonna make like AI that’s as effective as a human agent, the AI has to be able to handle the same issues, the same types of intent that the the human agent can handle. And so oftentimes the biggest challenge for us is

you know, legacy internal systems and they’re like, like this system’s only available inside of a web browser for our human agents in the contact center. We can’t actually make that available to you. And so it’s not like a limitation of the technology, it’s a limitation of the data and the systems that we can get to is oftentimes the limiting factor on the resolution rate. And and I do use resolution as the not containment, not deflection. Those are not good things. Resolution’s a good thing.

You know, the the limits on resolution is oftentimes just like the the scope of the cases that we can handle because we don’t have access to the internal systems to do everything.

Josh Lupresto (19:22)
So I’ve you know I I I’ve seen the platform, right? I’ve seen the studio kind of environment and I I think when others have come out with platforms that they say, low code, no code, it’s i early on it was still pretty complicated, but I think you guys do a great job. You know, I’m a I’m a big fan of also kind of the eval framework. You know, hey, don’t just build this. Let’s let’s see how it kind of deploys. Can you maybe just I know everybody’s listened to this majority on on audio, but but but

Walk them through a little bit if I’m okay, if I’m ready to move into POC, I’m ready to kinda test this, I’m ready to build it. What’s my life like as an administrator of this that maybe lightly technical, maybe a little bit technical, building that flow, designing it, testing it, integrations, all that, maybe just walk walk us through a day in the life of

Mike Myer (20:10)
Yeah, the when I think about

building an AI agent, there’s really two personas involved. There’s someone that we would call an AI engineer who actually kind of like understands the the underlying AI, might do a little bit of prompt work. And then there’s a a business owner who really understands kind of like what the ideal customer journey is and what the business policies are. And so in a Quiq implementation, oftentimes we’ll do that AI engineering work for the client. But

in the in the context of you know the Tolaris e ecosystem a TA couldn’t could do that work as well. obviously they they need to to gain the skills in the platform but the platform’s open like anybody can can do that work. and so the the typical implementation is let’s take our standard template do a little bit of customization around like brand level voice maybe integration with internal systems

custom guardrails or or any particular sp special handling that needs to be done for the the use case. and then so that’s the the kind of like the technical setup. And then inside of the technical setup we have a concept that we call a process guide. And a process guide is like a manual that you would associate for a contact center agent. You know, like like a a contact center agent goes through a couple weeks of training and at the end of the couple weeks of training he’s got a a set of s

procedures that say things like if the customer has an order issue, here’s the steps you should take. Like collect this information, go to these systems, and then apply this reasoning and output the result. And that same description that’s inside that agents manual is what we call process guide. And so the business user writes a process guide to describe how to solve something. And the process guide really is kind of like what needs to be done, what tools are available.

What policies are in place, what it like the what. And then the AI gets to determine the how because every situation is a little bit different. Maybe in the user’s initial utterances, they provided enough information that you don’t have to actually collect the some information. Maybe the information’s not complete. Maybe the AI needs to collect it. And then the AI gets to determine to reason through given all the information, the information from the internal systems, the the policy information, what the customer said.

Like all those things together apply true like artificial intelligence and and provide a response back. So long story short, that process guide is like a super key element. and as an implementer in a a a user inside the business, it’s written in English and it’s it’s very kind of like procedural because it describes like what needs to be done. So we see the business users be able to kind of like

work within the side the system alongside the AI engineers.

Josh Lupresto (23:10)
So who would you say I I w I wanna move to a little bit you know, for okay, if I’m a TA, I’m listening, I’m excited about this, I’m starting to understand kind of where it fits. Who who would you say is the ideal customer for this? A customer that is I mean just do a little fill in the blank here you fill in the blank here. A customer that is struggling with blank and wants to be able to do blank.

Mike Myer (23:37)
Okay, that that’s a good question. And it really is situational because some businesses are like, My board told me I need to deploy AI to d to reduce costs. So fill in the blank, reduce cost. we recently implemented a health insurer and they deal with very spiky business. So in the fall when there’s annual enrollment period.

Like the number of phone calls goes up by like a factor of ten. And so in the past, in order to handle that spike, they spent a ton of money bringing agents on for a couple months, and then they had a couple months of benefit enrollment period, and then those agents went away again. And the process repeated every year, and it was very like time consuming and and cost effective. So the ability to scale, that’s a another reason, fill in the the blank. another one is ability to provide quality.

service during off hours. A lot of businesses have you know, the that we’re open from eight to five, and if you’re after at n five o’clock at night, if AI can solve 60% of the issues, you’re gonna be that much farther ahead and people, you know, or it’s only 40% of the people are going to be dispat dissatisfied that you were closed. another use case is languages. you know, if you’re d a business that’s dealing with a a large consumer base in in different parts of the world,

It may not make sense to have you know, a set of sp two or three Spanish agents. maybe AI can handle those foreign language questions without actually staffing foreign language skills. Cause staffing foreign language skills turns out to be really expensive in the contact center. So there’s there’s really like it’s there’s a ton of different reasons why people build AI. A lot of times everybody’s like, Well, you’re building AI to save costs. scalability, capacity, like there’s a whole bunch of reasons.

Josh Lupresto (25:31)
Okay, so if we take that, that’s a that’s an awesome foundation. If we take that and say, okay, here is as you’re going to talk to your customers that you’ve sold X and Y and Z2, what would be as many as you want here? But what what are just a couple probing questions? I’m a huge fan of, hey, ask these questions when you’re with XYZ person at your customers at your prospects. because your platform does so much. I think there’s direct benefits, and then there’s all these other indirect benefits and use cases that.

we just kinda stumble into with you guys. So what would

Mike Myer (26:03)
Right.

Josh Lupresto (26:03)
be those questions that you would if if you could wave the Mike’s magic wand and all TAs ask these questions to to to uncover, what would it be?

Mike Myer (26:13)
I mean the the the most obvious one to start off with is first of all like in in your customer facing operations in your CX, what are you trying to solve for? Is it cost, customer satisfaction, scalability, capacity, et cetera? And then the next level down is okay, so let’s imagine it’s cost. That’s probably one of the the most frequent ones. I’m trying to make my my operations more efficient. well

i if you’re trying to make your operations more efficient, what are the drivers of that cost? Like, you know, what are the top two to three types of questions that you’re getting asked? And w what’s the underlying data and and what’s the process to solve those questions if it’s available like AI should be able to to do it. It in general, when I think about, you know, what’s a good application for AI, anything where you have a an interaction where you could take a human

give them a couple of weeks worth of training and then sit them in the seat, AI should be able to do that job.

Josh Lupresto (27:18)
I love it. you know, you’ve you’ve got this other I know I I TAs always ask us, please talk about more use cases, talk about more examples, talk about and I think you’ve done a great job of walking through some of those. there there’s another one that you have, I mean it’s this is tying a little bit on extending this probing questions theme. There’s the other one that you have where you know you’re it’s a it’s a two minute demo. I think it’s on your YouTube page as well. But it’s a you know, somebody calls in, they’re renting some cabins from the resort place and the AC’s broken.

And you’re you know, the the agent goes through multiple steps to to resolve that. And I think the you know, there’s this reaction from the guy calling in of, you could just text me yeah, yeah, text me the you know, but can you just kinda walk us through that like in the in the questions theme of

How somebody could uncover a use case like that, right? Obviously the solve was I didn’t know that we could have a platform that could could communicate in all these different ways of ways people wanted to be communicated with, integrated, seamless platform. But what’s the best? I don’t know how to ask this, but what’s the best question that uncovers, my gosh, I didn’t even think about, you know, could we do this? Could we move, could we, could we modernize our whole platform and our company to do something like that?

Mike Myer (28:30)
yeah, good point. And and a a lot of the context of my answers to so far in the conversation have been around kind of like an AI agent that’s directly helping a hum the consumer, like like direct replaces the human agent. and that’s certainly like one of the most common cases that people think about AI. But it’s not the only case. the demo you’re recording referring to that recording was a human agent that was assisted by AI and you know, in

a conversation, we can have an AI agent that is listening to the conversation and taking actions on behalf of the the human agent. And so what’s the like the one of the longest things that takes time inside of a conversation? Well the the human agent asks a bunch of questions and then they’re like, okay, let me go investigate that.

And they’re probably like cut paste, cut paste from like, you know, s like the conversation into or or from their brain in the voice conversation. They’re typing it into a a form somewhere so they can actually go do something in another system. And that level of automation to be able to actually have the AI do the data entry and the the back end system integration. And so the example that that you’re referring to, the guy’s air conditioning is broken and the human agent says, bummer,

We’re gonna move you to a new cabin. I’m gonna send you a text with a couple of choices. And so to your point, like no number one, the action of sending the text is automated, so it doesn’t the human agent doesn’t have to do any work. And then the interaction style is so easy because it’s back to my what I said earlier about like if you got a bunch of choices, it’s much easier to look at them on the screen.

And so the text message makes that that input easier. And then the response comes back. The AI agent collects the response. It fills in the back end system and the human agent says, Great, I’ve got that. Like I’ve rebooked you into a different a different cabin. So yeah, that kind of like it’s an interesting mix of AI either in front of the customer or alongside of your human agent. And then the conversations being not just voice, but voice plus another channel really and makes it more powerful.

Josh Lupresto (30:37)
Love it. Love it. such a good example. all right. Well, in AI, we’ve got to look at the future as we kind of wrap this up here. And I know that’s that’s hard because every time we blink, there’s a new thread on X that’s the next greatest thing, or it’s the next greatest vaporware, and we’ve got to figure it out. So for for you, you know, just let’s call this Mike’s perspective here. you know, look, we’ve gone the world’s gone from this kind of chat bot theme to

AI agents, resolving calls, end to end, all of those things. So as we look out, 2027’s right around the corner, fall is kind of happening, right? You got agents talking to agents, you got all these things.

Mike Myer (31:14)
Yeah.

Josh Lupresto (31:15)
What is wh where does this customer journey go in as as advisors are not only just taking all of the things that you gave them in this episode and talking to their customers, talking to their prospects, what what else should they be building towards being prepared for?

Mike Myer (31:30)
You know, i i it’s interesting. In the in the next year or two, I don’t think that there’s gonna it needs to be like a a technology revolution or there’s gonna be like another big bang type moment. I think in the next year or two is really when the potential of the technology gets the the current technology gets realized. And so you know, in in two years from now, one of the ways that I think we’ll have achieved success is if you’re like, I’m calling

American Express or the the bank, whatever. and you’re actually disappointed to get the human instead of the AI. ‘Cause

Josh Lupresto (32:08)
Ha ha.

Mike Myer (32:09)
right? Like the the ultimate is you when you’re like the AI

Josh Lupresto (32:11)
I love it.

Mike Myer (32:12)
is so good, I really don’t want to talk to human because they’re fallible, but the AI is like great. And so like we’ve got a ways to go until people are like today, people are like, I got stuck with the chat bot. in

Josh Lupresto (32:24)
Yeah.

Mike Myer (32:24)
the future we we want to be so good that people are like good.

I got answered by a really capable AI agent, not by a human.

Josh Lupresto (32:34)
I love it. It’s a great place to wrap it. Mike, that’s all I got for you today. I really appreciate you coming on and sharing everything and anything about Quiq. thanks so much, man. It was good stuff.

Mike Myer (32:45)
Josh, it was a pleasure. I enjoyed the conversation.

Josh Lupresto (32:49)
Awesome. All right. Everybody, that wraps us up for today. As always, don’t forget, wherever you’re listening to Apple, Spotify, elsewhere, just make sure you know every Wednesday this episode drops. And as long as you are subscribed and followed, you will get these before anybody else does. So like I said, that wraps us up for today. I’m your host, Josh Lupresto SVP of sales engineering at Telarus Mike Myer founder and CEO of Quiq This has been Everyone Selling AI Chat. Voice is where the deals are. Till next time.