The Exchanges

Q they call it, probabilistic space. So I don't know how we, we still don't have, we still haven't seen Alexa plus. Maybe it'll be come out by the time this, this interview hits. Uh, but I don't know how you do that in business, uh, because you really in a business, uh, you're kind of risking a lot if you start to leave that open. So how does that work?

A Yeah. So we, we take, um, you know, some off the shelf large language models and we actually have, um, eighteen billion interactions that are, um, that are anonymized, uh, in a database with people rating customer, uh, interactions, a positive or negative, real simple, thumbs up, thumbs down. And we do a lot of post training on our models, the basic models after the fact to try to get their probability to your point, win rate, their resolution rate as high as possible. And what's interesting, um, you know, when a, when a bot makes an error, they do make errors. It's a hallucination. When a human makes an error, you know, it's a mistake. And what we find right now is on our next generation agentic AI bot, um, we have a lower error rate than a human being, uh, from a, you know, contact center, support center, answering like for like inquiries. And so when we talk to our customers, we say, look, there are going to be some errors. A hundred percent. It is going to happen. Um, just like a human being, no matter how much you train them, how much you work with them are going to have some errors. But what we're finding generally is you can serve your customers at a, a much lower cost. You can get their, your customer satisfaction up because if you have a personalized bot that can instantaneously respond versus waiting online for five minutes or 10 minutes for someone to respond to your…

AI assessment note: “we do a lot of post training on our models... to try to get their probability”

Q Alright, so can you just walk us through, because we'd love to hear the practical examples, how would a customer basically use this platform?

A Yeah. A customer would use this platform, say, um, we, what we do is we come in, uh, for our larger customers, uh, for our smaller customers, um, we will go real quick, analyze their ticket data or their customer interaction data. And we say, we think there's an opportunity to automate 40% of those interactions. Okay. And we think we can go do that with you in two or three months or two or three weeks or two or three days, depending on the complexity. And, um, And so we're pushing some things in products to let them know what we think is going to happen. Like, we think we can go automate 40% of resolutions. We think this is going to lower your cost to serve by X. And we think it's going to take your customer satisfaction up from, depending on if you're doing CSAT, uh, top box from a four to 4.4. If you're doing MPS from a 60 to 67. And so we talk about that with them. And then what they do is we go in through, um, you know, an implementation with them in an adoption phase To go get that. We usually do an A B testing to show them, um, on the, um, on the, on the platform that we are getting what we promised. Okay. Through an A B test and they are getting the customer satisfaction. One of the biggest worries we see with our customers is I believe you can go automate X percentage of my interactions. I'm, I'm worried that customer satisfaction is going to go down and, um, companies …

AI assessment note: “what we do is we come in... analyze their ticket data or their customer interaction data”

Q if not more, we'll be, we'll be thrilled about this. And then you've brought up the co-pilot a couple of times. So What is that going to look like in a customer service agent's dashboard? And then we're going to move on to jobs because the questions about what will happen when people jobs are starting to percolate, but I want to know what it looks like in the dashboard.

A Old school human agent would get a, um, you know, information from a customer, whether it came in in a message, a WhatsApp, uh, Apple business chat, an email, a web form, you name it. Okay. And then they would craft their own reply. They'd have some canned replies on likely Interactions, uh, that they could, you know, like cut and paste basically. Okay. That's kind of old school human agent, uh, you know, resolutions. What happens now is if you have, even on a voice bot, but I'll, we'll put voice aside, any kind of digital interaction, the platform should be suggesting a response for you. So it's not you pulling as a human agent, okay, data or response into a response. It's actually, here's the suggested response. We looked at 17 similar customer interactions. This is the response that got the best, uh, response. Again, do you want to personalize it anymore? Do you want to change the tone? Do you want to accept it? Do you want to edit it? And so it's kind of changed in the human agent experience, uh, experience from a lot of times creating things on their own or, you know, pulling information in to more of an editor where they're deciding, hey, this is the right response. This, maybe it's not the right response because, um, the, um, The co-pilot does not realize how upset you are, Alex, because you had to type in agent 55 times to get to the human agent, and you've got a really…

AI assessment note: “the platform should be suggesting a response for you”

Q You said in your example that you can go in and say there's 40% of customer interactions that can be automated. Uh, where did that number come from? And do you think that this is going to be like an average experience with the company?

A Yeah, so what we do is we get permissions from our customers, and we take a look at their interaction data, so we use an algorithm that looks at all their interaction data, so we do about, we process about five billion dollars, five billion tickets, or five billion interactions a year almost, and so a company will have a subset of that. We look at the data, and we say, based upon what we know, we have over 10,000 AI customers right now, and those 10,000 customers We think this password reset, we think this return, we think these 17 use cases can be automated, and we tell them this is three percent of your interactions with your customers, this is four, this is seven, and there's going to be some of those that are going to be really complex that are ultimately going to be humans. We break all that down, and we say these are the interaction types, these are the percentage of those interactions or use cases that we think we can automate, and we give them that the data, and then we say, hey, We've got 10,000 customers around the world using our resolution platform, and you look like two or 300 of those customers. And these are kind of resolution rates that they're getting from doing this automation. So it's not a, um, it's not a, you know, wet your thumb, put it in the air and kind of guess. This is based on, uh, old school, uh, a little bit, uh, algorithms, machine learning to pre…

AI assessment note: “we use an algorithm that looks at all their interaction data”