Name: Hirevue Innovation Roadshow
Uploaded: 2025-07-23T17:30:06.053Z
Duration: 1 h 30 min 4 s
Description: Hirevue Innovation Roadshow

Transcript for "Hirevue Innovation Roadshow": Alright. Hello. Well, I'm gonna give it just a few seconds here to let everyone get logged in, and then we'll get started. Alright. I think we're gonna go ahead and kick it off. So welcome everybody to HireVue's innovation roadshow webinar. We're extremely excited to have everyone today. I'm Jessica Wirtz, product marketing director here at HireVue. I'm just gonna kick things off really quick. Two quick notes. Be sure to use the q and a throughout. If you have questions or comments, we will try to get to them throughout or at least at the end during q and a. And we also have two polls that you'll see pop up. Be sure to interact with those. We would love to have, interaction from our audience. Alright. So with that, I am gonna quickly introduce our speakers for today. We have Marcy Daniel, our chief product officer, Kathleen Preddy, who is the senior data scientist and innovation lead, and we have Jeffrey Stanford, our group product manager. So I'm gonna go ahead and jump right in and pass it over to Marcy to take it away. Thanks, Jessica. Everybody, we're so excited about speaking with you today and sharing, where HireVue is going and, getting some live demos. Lots of exciting stuff, on this, webinar today. So I'm gonna kick us off, and really start with, the, concept here of, like, we are obviously a technology company and we are innovating, and I'm gonna share a lot more about how we are really pushing forward with our vision around validated skills. And at the core of that is really our science. I think it's, what you'll hear repeatedly from us throughout, the next, forty five minutes, hour is about how we are embedding science in everything that we do. Our organization has a deep history of the science of IA I'm sorry. AI and the science of, that we have brought forward with our VJT work and many others. And so you're gonna start seeing that, come to fruition in all of our different innovation that we're doing. So the world is changing, in terms of the world of work. And so I wanted to spend just a moment setting up, a little bit of some context for, our direction of innovation and our road map at Hirevue. And we'll start with some statistics that really resonate as, many things that you probably are struggling with out there as well. Almost 40% of skills will change over the next five years. So what we're doing for hiring today may not be exactly the skills that we need to hire for in the future. And, in terms of looking out into the broader market, we've heard from employers that, you know, over almost 65% of employers are citing skill shortage as the key barrier, that they're facing. So these are some of the big boulders out there in terms of problems that we need to tackle as being a solution provider, for, talent acquisition and, even talent management teams. I think the other thing, that we have noted and we see this present in our, AI study that we, launched and, published, over the I think it was about six months ago is that about 72% of HR professionals are leveraging AI to drive efficiency. And people are seeing results from that. Right? Increased productivity, automation of different task. We have a number of tools that, are part of our platform that drive that efficiency and productivity improvements. What's also happening is that candidates are using a lot of AI these days. And I think one of the things that we wanted to highlight is resumes have always been a little bit tricky. I think in terms of leveraging them for, our, hiring process, but it's gotten even more, sort of challenging. Candidates are using AI across a number of things. You know, they're thinking about how to update their CV or their resume. They're thinking about, preparing for an interview. Some really positives, really, of how to leverage AI. And many people are starting to see an opportunity of how AI can actually reduce bias, in the hiring process. But some of the, I guess, more, challenging areas are this is a, I think a Reddit post, of, just one interviewer and I think there's probably millions of them out there, today of leveraging ChattGPT to really customize their, resume, for, interviews and really tailoring it and, making it based on the job description, to increase their opportunity to get past sort of screening of the ATS. And so I think that's one thing as we think about, you know, helping candidates get connected to the right role and helping our, TA leaders get connected to the right talent is how do we make sure that we're, enabling teams to, review, and get connected, but also make sure that they have the best, data and validated skill data to operate against. And so we're gonna spend some time, talking about that. Skills are really the new currency for, talent. And so how do we think about that? So first, we wanna hear from you. Jessica mentioned in the beginning we have some poll questions. So we're gonna launch our first poll question, and learn a little bit more about what's happening in your very specific world, related to skills. So please, answer that poll where are you in your skill based hiring journey, and then we will get some results. Okay. Thanks for answering that. We're gonna see, our results and, learn for that as we move forward in today's webinar. Alright. Great. So where, are you in your skill based hiring journey? About 30%, just starting to explore the concept. Just shy of 30 developing a strategy. And then some folks, about 20% are piling skill based initiatives right now. And then, another, 18%, implementing skill based hiring across some roles. And then, 4%, are, already fully integrated with skill based hiring, which is amazing. So everybody is on in different places, I think, with that journey, which is exciting. So I think this will be a great, dialogue. Now I think this is a McKinsey study. They studied, I think, over 400 different, HR, leaders and talked to them about, and also did a lot of research around the predictive nature of skill based hiring. And it's known and well documented to be, you know, five times more predictive to success, for, talent as they enter an organization if they, follow that skill based hiring model. And some of the top fastest growing skills you can see there, AI, is one of the top ones. There's kind of a mix here of hard skills and, soft skills, in different knowledge base, areas. So this is a really good, I guess, marker for us as we think about how do we need to make sure our solutions are building towards that future and being able to really accomplish, a truly mature skill based hiring model, that people are starting to implement. So as I mentioned, we think about skills in really three different areas. Hard skills, that sort of coding or accounting, those skills that you need to have to, do that job. Soft skills, that tends to be more like interpersonal skills or persuasion or critical thinking areas. And then we have another area that we, really consider and you see this reflected in a lot of the work that we do, in the market is skills plus. This is around your motivations, your cultural alignment to our organization, and so many of our, assessments consider these three different areas. And, certainly, as we're thinking about how we move our road map forward, we are thinking about how do we enable, skill identification across these three different areas, for your different roles. So we have one more poll, that we'll put up and, get some feedback. So what do you think is the biggest challenge with skill based hiring? We did some research on this, so we wanna see if your feedback matches up to what we, found, when we did a research study with Amplitude Research. Give you a few minutes to be able to answer that, and we'll see what everybody says. And I think the poll is closing, and we'll see what the results are here. Okay. So about 30% of you said that you've got resistance to change from hiring managers or leadership. A little bit higher on a lot of difficulty in validating skills effectively. Some, technology limitations, but that's pretty low, and then, roughly the same, on lack of alignment, on definitions of skills, and then inability to measure the ROI of that skill based approach. So great feedback. Now let's take a look at the broader survey and see, some of what we found. This was, some research again that we did with Amplitude Research. This is, over 400 global HR leaders, that are operating their organizations, and this actually matches up really well with the, audience poll. So number one, little bit higher difficulty in validating skills effectively is the number one we found across that broader group. Number two is, you know, resistance to change among hiring managers and leadership. Everybody has these older practices that they're accustomed to following in their hiring. And then the third is just lack of alignment. So I would say our top three matched perfectly, to what we saw in the broader survey. And I think when we think about assessing skills, we really look at kind of three areas. And if you look at the top there, they move from the left to the right in terms of certainty. So the first is inferred skills. These are skills that you might see, inferred or grabbed off, like, a LinkedIn profile or GitHub for a technical. These are speculative. They are, not, you know, they're from oftentimes, you know, publicly available data, on, different candidates. The second is self reported. So these are skills that a candidate might indicate on their resume. They, are descriptive in nature, but they certainly don't have, you know, a a validated approach. And then the last one is validated skills where these are predictive. There's a lot of science behind validated skill approach and being able to really bridge that to demonstrable skills, to be able to be successful in a job and predictive to being successful in a job. So when we think about skills, these are the kind of the three, continuum, that we, consider. So couple of the things from that study that I think are interesting is, you know, do you trust AI inference? And about 59% or almost 60% somewhat trust it but prefer validation. And if you look at the top items on terms of what would need to change to improve trust, There's, the majority is kind of taken up with the purple and the pink, slices, better integration with existing HR systems as a core item, and the combination of AI and direct skill validation. So I think one of the things that we've, considered and you'll see some of this in, the work that we're doing in the product demos today is we think that there's a place for both. Inferred has a lot of value, but also that validated, or verified skill is also highly, predictable. So you having both is, like, the best of both worlds. And so thinking about those, systems living together is really important to us. So validating skills at scales is really more of a show me, rather than that self reported proxy measures that, you see in that inferred and self reported measure areas. So I settle that as a stage of, like, thinking about that as we enter into sharing you our broader based product road map. So just to review, some of the challenges that we want to meet, as we think about solutions, our first around, you know, we've got a the sort of the AI talking to the AI. So you have job descriptions that are created by AI. Those are feeding into, resumes that, are, you know, oftentimes created by AI and then profiles. So sometimes it's really difficult to discern, like, what are their real abilities and everything we're hearing from customers is this is a, you know, significant problem statement for them as they think about how to move forward, and move into a skill based hiring strategy. The second big challenge that, we are, tackling and that we hear from folks, in the customer base is there is a lot of skill proliferation. So this is just a sample, but, things like Workday having 47,000 plus, skills identified. Lycass has 32,000. So as organizations move up that maturity model, thinking about how do they live with these different skill tools, from various providers and oftentimes might have multiple taxonomies they're using even within their own organizations by division, or by different use cases, be it learning and development versus a hiring, tool. The, next area I just wanna highlight is that this very traditional recruiting journey, tends to be very linear. I think all of you are familiar with this, you know, path that people, take. You post a job, the candidate applies, recruiting happens, you schedule, we do end screening, and we go through this very, I guess, fairly predictable path. What we're seeing is that there is, starting to be a change to this funnel. So this is a very traditional talent funnel that you're probably all familiar with starting with from awareness and marketing your organization all the way down to, application, evaluate, and hiring. And one of the things that we're considering is, do we need to rethink that paradigm? And we are suggesting here that we could almost, put it on its side and thinking about putting talent and talent engagement and that connection point with candidates, sort of at the center of what we're doing really transforms this into more of an infinity loop. And thinking about as we engage with talent, there's an opportunity for us to continue to learn more about that talent, and there's an opportunity for us to build more that skill profile, for, talent. And that continues to flow in. I hear often from customers that they are really, you know, they're reengaging oftentimes with, talent as they candidates apply for jobs, but maybe they're not a perfect fit for, the application that they, put forward, but there's other opportunities that they would be a great fit. So thinking about this reengagement, and ongoing engagement and building out, more of a skill profile for talent is certainly something that people have on their mind, and also definitely plays into as we think about internal talent, and being able to build more knowledge about what skills you already have in your organization. So just a simple, graphic to help paint the picture of how we're thinking about skill discovery and really that reimagination of, like, you have maybe one touch point where you have find my fit, type of, assessment that is used. Maybe then you have specific job assessments and you keep marching through and the candidate skill profile tends to grow over time as you engage more with talent about what what their interests are, what their skills are, what their, hard skills are, but also their soft skills. The other thing that's really core to setting the stage for Hirevue is our history and, innovation and AI. And just as a quick reminder, I wanted to spend a moment just, highlighting really the deep, commitment that Hirevue has had to AI from the very beginning. We were the first to have an ethical use of AI in hiring, as part of our organization all the way back to 2015, ten years ago. Just other couple other highlights, we were the first, AI tool in HR and company to come out with an explainability statement, that was, verified by the ICO. So we certainly understand the responsible use of AI and very excited today to share about where we're going from an agentic AI standpoint, and you're gonna see some live demos that highlight that. But we have a deep, history, and we are pushing forward, on our AI strategy to enable more tools for you. What that looks like, in terms of an agentic platform, which is, a common word, in all technology companies these days is a AI. We're thinking about this, in a very holistic way. So we start with, user interactions on the left hand side here. So interacting, maybe it's with your mobile phone, maybe it's with Slack or with, you know, WhatsApp, and how agents in that center area can really do work for you. We're gonna do some deep dives on a few of these, but things like scheduling, what, you know, job searching, being able to do a search, for candidates, being able to apply for a job. And then how those build out across our science base, which is very exciting. And then performing those actions, what that action should be and then those outcomes in these common productivity tools or your ATS, or other tools. So we very much, are building out that AgenTek platform, and expanding, our AI capabilities there. So how does all this, come together and really inform our product vision and our higher view path forward? We are the industry backbone of skill intelligence. And what that means, is we have an offering that really offers, discovery tools that supports the entire, hiring journey. So you'll see our mission statement at the heart of our product wheel with connecting talent to opportunity. And I would also take you to the outside of this product, portfolio suite of having a number of tools that really support the platform to make this a very seamless experience. So things that are critical of being part of your ecosystem and your broader tech stack, like ATS integrations, stronger, omnichannel type of communication tools, scheduling tools, a very strong workflow tool that works, across our portfolio that can integrate into other areas that you might need in your HR tech stack, and then, of course, conversational AI and agentic AI, as those come together. We're gonna dive deep into a couple of key areas of our portfolio today, new areas that we're excited about launching, and we're gonna kick it off, with, interview, intelligent interview. And I'm gonna turn it over to Jeffrey and he can share what he has been up to with his team. Thank you, Marcy. Appreciate it. Yeah. So we're working on this interview insights product that I'm really excited to show you guys today. But I wanted to share with you a little bit about the journey that we took to get to get to the product that we're building right now. So we decided to start by looking a little bit into the hiring process and figuring out what are some opportunities for improvements. So first, we, looked at a global survey of of HR, representatives that found that 44% of recruiters surveyed said that their interview process takes too long. And we also saw that 25% of candidates drop off in the interviewing stage of the process. We decided to take a look at what does this look like in our platform. So on Hirevue, we found that on average, the evaluation of an on demand interview takes sixteen minutes, and we saw that in the last thirty days, there were 208,000 on demand evaluations. So we saw that, speed is an important thing. It's something that we're sure matters to you and the efficiency of of the work that you're doing. And it also matters to the candidates too, and it has a direct impact, on your hiring process and the candidates that you're able to bring in. We continue looking. We wanted to look at at the use of inter of interview data. We found that 40 34% of organizations are using objective data consistently in the hiring process. And 7% of organizations felt that they're using HR data effectively. So these days, we feel like we're swimming in a sea of data, but are we using the right data? Are we focusing on the important data when we're looking at hiring candidates? And we found that that's not necessarily the case right now in the way that we're working. And we also looked at training hiring teams. So you can spend all of your time focusing on the right things to keep your hiring team fast and make sure that focusing on the right data. And actually, Aptitude Research's latest findings revealed that poor interview processes marked by inconsistent communication, interview unpreparedness, and reliance on gut decisions are causing eighty two percent of recruiters to lose candidates. So we know that you're focusing on these things already. But how can you be sure that your that your team is actually, using the the, the right processes and doing the right things that you're asking them to do? We found that only 90% of companies reported hiring managers consistently following their recommendations. So even when you are setting them up for success and giving them all the tools, it's hard to know if you're actually using those. We also saw that 28% left the right training to support interviewing. And so we took all of this data that we found and we put it into this new product for interview insights. And we're focusing on speedy evaluations, so that's about doing, having your evaluators and your and your hiring managers working efficiently and also making that process faster for your candidates. Effective use of data, so that at every stage you're adding valuable data to your candidates profiles, and your evaluators and your hiring managers are focusing on the right things, and job related criteria instead of just made making decisions. They're thinking about the candidates in terms of, the vibes the candidates are putting off. And then also, enabling your hiring teams. So that's about doing the best that we can to encourage your evaluators, your hiring managers to focus on job related criteria, and also giving you the insights you need to see how your, hiring managers are conducting those interviews so that you can, manage your teams more effectively. So let's take a quick look at the product. I'm really excited to show you what it looks like, and where let's dive in. So first, I wanna call out that this product is coming to live interviews and on demand interviews. And we know that you live in Teams and Zoom, and so we're making those a a priority, and we're focusing on bringing all these features that you're gonna see to Teams and Zoom. So this is a live interview. This is after it's been completed, and you'll see that we've pulled in the video from that live interview. And if you look at the top right corner, we now have transcripts. So you go to see the full transcript of the interview, and you go to search those transcripts as well. We're also gonna be providing AI overviews. So now you can see a a summary of all the things your candidate said, and we're gonna be highlighting important strengths and weaknesses. So we're gonna be focusing on job related criteria with these strengths and weaknesses. So we're trying to help lead your your evaluators and hiring managers towards, that important data, that important criteria when they're thinking about their candidates and making decisions. We're also going to allow you to leave feedback for these candidates in an easy way directly on the transcript. And when you leave that feedback, you'll see it here in the the video playback mark. You can imagine if you leave a lot of feedback across an interview, you can glance at the feedback bar and very easily see the overall tone, the overall, feedback that you have for this candidate. Then you can also click on these these, response on the playback to jump directly to that point in the interview and reference where that feedback was left for. And leaving feedback is very easy. You just hover over the transcript and click on like, tag, or flag. And here's an example of an AI overview for a specific question. So in a live interview, it's just it's a one long stream of a conversation. Right? But what we're going to do is we're gonna look at your interview guide, and we're gonna separate out that interview into different questions based on what you how your interview guide is set up. And then we're gonna be able to give you a overviews for the candidate's response for each of those questions. So here we're providing a, a summary of a candidate's response for one of those questions. And then to get back to that your hiring teams and enabling them to help your your, your hiring managers and your graduates to do their best job, here we're providing what we're calling interviewer insights. So here, the interviewer can see feedback based on how they conducted the interview in this specific, interview. So at the top, we can see that we have, guided hearings. So you set up this guide with all these questions, but are they actually asking the questions that you set up? And they can see if they did or if they missed any of them. We also have top time ratio. So the interviewer can see, do they give the candidate enough space, in the conversation so that they can really put their best foot forward? Join time, which is the candidate the interviewer can see if they were late or if they're untimed. And then interviewer sentiment. So did the interviewer provide a create an environment that allows the the the, candidates to really flourish and show their best selves? And here we're seeing these four pieces are being shown to the interviewer directly. So the interviewer can give that feedback and make improvements on their own. But for our TA teams, we're also gonna going to be providing the same information here. But also we're gonna show you a a higher level view where you can see multiple interviewers across multiple interviews. So you can identify larger trends to see where you can make improvements for your team and maybe improve your training. And you're you can also pinpoint opportunities for improvement. So if you see one or two, one or two of your interviewers who can maybe make some improvements here or there, you can use this data to have, productive conversations with those interviewers and help to make sure that your interview process is working exactly how you want it to in the best way for your candidates. So that was live interviews, but I just wanted to call out this is the course also working for on demand interviews. So here we're seeing, you know, the summary page for an on demand interview. Again, we have that same transcript functionality with the feedback, the like tag and flag. It's fully searchable. You get that AI overview with the full overview of everything the candidate said and all the responses to the questions. And now we also have, in this case, some score explanations. So here we're seeing an on demand interview where we use our AI score interviews. So what that means is that the candidate went through and asked all these questions and we generated scores based on their responses. And this is the product that exists today in Hirevue. But what we're adding with interview insights is the explanations below the scores. So now your your evaluators and your hiring managers and other people for looking at the candidate can only get a score, but they can get an explanation of where that score came from. And these scores are gonna point directly to the things the candidate said. So not only is it helping your, evaluators and your hiring managers to focus on the right details, but it's almost kind of training them and helping them to learn how to connect these competencies, these important job related criteria directly to the candidate's words. And to bring that to, another level, here you can see this is the AI overview for a specific question. And again, in this question, the candidate was taking an AI score interview. And here the question was focused on the competency of compassion. And so we're getting the summary of the candidate's response at the top here. And then we're getting effective indicators, which are calling out points that we found in the interview where the candidate demonstrated that that competency of compassion. And you can also see for these effective indicators when we can, we're we're giving you a link to the that time step exactly when the candidate demonstrated that that, that key behavior. So it's an easy way for while you're evaluating these these candidates to jump back into the interview and see, exactly where we found good opportunities to to dem the candidate demonstrating these these competencies. And this is all about helping your evaluators and your hiring manager to focus on the right job related criteria. And it's also about speeding up your evaluations. So now you can say, for example, you finished an evaluation, you know, two months ago, and now you're at the next stage of the hiring process and you wanna review what the candidate said. You can jump back into this and get a quick quick summary about what happened. You can this could call us some effective indicators, and we're hoping it'll help your team and you to work more efficiently and to work more effectively and to focus on the right parts that matter. And also giving your team the insight into how your your evaluators and your interviewers are are conducting the interviews. And that's interview insights. This is just the beginning. We have plans to do a lot more. We're really excited about this direction that we're going. And if you have any questions, please feel free to ask, and I'd love to talk to you more about it. Thank you. So I'm gonna talk about another, wedge in our product wheel, talent engagement. So talent engagement kind of flows through our whole product stack. It's a crucial part of the hiring process and probably matters to a lot of you. We wanna keep your candidates moving throughout the end to end hiring process by really meeting them where they're at with flexible and smart solutions. So, we've got conversational AI and easy apply features that really enhance the candidate and the recruiter experience by saving everybody time, providing users with what they need, and not adding all these extra steps to bog things down. So, really, where this fits when we think about that infinity loop of, of talent is right at the heart. It's a central system that really works to support your candidates throughout their whole journey, matching them to their best fit opportunities in this kind of seamless personalized experience, accustomed to them. So, I mean, let's face it. We know that talent is left on the table. Right? We have a APCAS study that said, that the like, ninety two percent of the candidates actually who click the apply button never actually finish applying to the job. So that's a lot of talent left on the table. Plus, you know, Gen z will drop out of hiring processes that are not engaging or comfortable, and we're here to fix that. So, today, I'm actually gonna show, a live demo of of this product. I wanna give you a little, walk through quickly before we do that. So, our match and apply product is powered by AgenTic AI, which we, talked about a little bit today, and I'm sure you're hearing about all the time in every technical field. And the idea is that, we wanna assist candidates in their job discovery process with science backed expert level guidance. Right? So we wanna engage the talent. We wanna match them to roles in a variety of ways, whatever kind of the candidate wants, and then drive those complete applications. So there's three kind of paths to help the candidate search, and these are all kind of they're not like you go down one path and that's the end for you. They can you can kind of choose multiple paths as the candidate. So you can search conversationally by just talking to the bot, going back and forth and talking about your job preferences, talking about where you wanna work, what kind of goal you want, what you've done in the past, what skills you have, whatever matters to you. You can also upload your resume, and receive relevant results based on your resume and then continue that back and forth. You can also take a Find My Fit assessment. It's a predictive assessment that measures, your work style and your job preferences and can help you find kind of the best job matches for you as the candidate, based on just the results of what your work style is, how you like to work. And then we know that match and apply gets people on track and applying to the right role for them, but that's just the start. Right? That's that's one part of that infinity loop. We want it to keep going. So not all good candidates get hired on that first apply. With this opportunity matching solution, we're matching the candidates who are in your existing talent pool to every new requisition that opens. So as soon as you open a requisition, you can you the recruiters can see, like, this tiered list of internal and external predictive matches, which are based on validated skills. You can see who who would be a good fit from your talent pool, you know, your silver medalists and all that. Then those, match candidates can be reengaged and invited to apply through this easy apply solution. So, again, we're meeting the candidate where they're at, whether that's over texting or email or some other channel, And we're engaging talent in this continuous and personalized way. And that the goal of that is to just enable this infinity loop of talent and solve a lot of that talent left on the table problem. So, before we jump into the live demo of our match and apply solution, I wanna define AgenTic AI. So when we say AgenTic AI, basically, we mean AI that does stuff in the real world. So there are some key components of AgenTic AI that differentiate it from, like, a simple large language model interaction. So I wanna kinda, like, walk through those those stages that, are essential to Genentech AI. So the user sends a message into the bot. That's a natural language input, and then the agent interprets this information. It it reads it. Right? It understands it, and then it uses reasoning to figure out what the goal of the user is and what it should do. Then it basically makes a plan. It determines the workflow for itself, lays it out, and then it executes that workflow on behalf of the user. So then finally, you end up with this outcome in the real world. So, for example, you might say, like, reschedule my Tuesday phone screen. It would say, okay. This person has a Tuesday phone screen that they wanna reschedule. I'm, here's my plan for it. I'm gonna look at their calendar. I'm gonna look at everyone else invited to this. I'm going to, you know, look for a new available time on Tuesday or later than Tuesday and, like, all this stuff. And it would make this plan, and then it will go execute those steps and interact with your actual calendar or your email or whatever it's supposed to do to reschedule. And then you would end up with that real results in the real world, the new calendar invite, or whatever it may be. So that's the idea, of a agentic AI. And and Marcy showed us kind of our our agentic AI framework. I wanna really hone in here on, what we're doing with match and apply. So our match and apply chatbot is powered by these state of the art AI agents. Right? They help match talent to the right opportunities, and each of these little robot face agents are, working together to get things done seamlessly under the hood. They're each experts in one thing, and they collaborate to get stuff done. So each agent is leveraging these large language models, and it's utilizing our Hirevue in house science backed tools, a knowledge base that has, information about your jobs, your company documents, whatever we need that agent to know about to make an informed decision and make a good plan, as well as, Hirevue science. Right? So it knows kind of these IO best practices for how to do its job. This results in a chatbot that is contextualized to your company as well as solidly grounded in Hirevue science. So this conversational experience, again, I'm gonna keep saying it. We're meeting candidates and hiring teams where they're at, and we're streamlining this task completion. We wanna provide exactly what the user needs without these superfluous steps. Right? So these agents are also able to flexibly adjust to the information we give them. So without needing some, like, extra setup steps, you can just provide them with the data that they need to make decisions and plans. They operate in they operate independently kind of within that world of their knowledge base and their tools that they have access to. So, although we obviously want the candidate to have this endlessly flexible experience with no dead ends, we, of course, need to constrain the conversation in some key ways to make sure it's safe. So, for example, we have guardrails that require the agent to only use information found in its knowledge base to help the user and answer user questions, which will help prevent hallucinations. We also just wanna talk about, like, what is a guardrail. So in this agentic AI role, a guardrail is a safety system that we can configure to make sure that the, users and the agent, are being appropriate in this context. So we can define what appropriate is. We can configure it. And these guardrails use a variety of technologies to process every message that comes in, every message that goes out to make sure it meets our requirements for appropriate usage. So, essentially, the candidate is driving. Right? But the Hirevue chatbot engineers the track. We're providing these guardrails that keep the user on course and also streamlining their path to the finish line. So let's see it in action. This is the part where I have to switch my screen, so bear with me. Let's do this. Alright. It's live demo time. So here's our, here's our chatbot. It would live on your career page. Right? And like I said, there's a couple ways you can search for jobs here. So you can click search for jobs and just talk with the bot. You can click management jobs. You can also just jump in. So I'm gonna say that, like, let's see. The hard part about this is talking and typing at the same time. So I wanna say I just moved to Dallas looking for anything. I've worked for six years in a warehouse, let's say. Let's see what they got for me. So the idea here from a technical perspective is that the the chatbot is using an agent to kind of analyze my response and say, okay. What is this person looking for? What's their goal here? They're looking for a job and and kind of taking the elements that I've shared about my job search criteria and pulling out what matters. Right? So it's saying, what are the things that matter? How can I search for the appropriate job for them? Looking at all your available jobs comes up with this warehouse supervisor role. It fits my years of experience. It, you know, makes me a strong candidate. Great. Would you like to apply? I'm gonna say, actually, I don't wanna be a manager. Should've said that in the first place. Don't wanna be a manager. Do wanna work at a warehouse. So now it's adding this information to everything it knows about me in this conversation, which obviously isn't that much right now. But, it's it's using that to kind of pull through and say, like, okay. This job is a good fit except for the manager. We know we know these elements about, her. She's worked for six years. She's in Dallas. So here we go. Found one that fits all my criteria so far. I'm gonna say, like, would love to apply to this job. So here, I can, choose this job. I can apply right here in the chat. It's great. The other way that I could do this yes. Restart this so it doesn't remember anything about me. The other way I can do this is to, get matched to jobs. Right? So, to get matched to jobs, you can either take that Find My Fit assessment, which is that work style assessment that I was talking about, which is, great, especially if you don't really know what you wanna do from experience perspective. You just want to, do something you know that is the sort of job you like that fits your work style, fits your personality. Here, I have a resume as a communications manager. So I'm going to upload that and say, like, just show me what kind of fits my, experience that I have so far. So, again, what the agent is doing is it's taking this resume. It's reading through this resume. It's pulling out things that are relevant, based on what it knows about hiring best practices. And it says, like, okay. I found this excellent job for you, and it requires seven years of experience. And I'll say, like, any location, but I'd prefer Chicago. And we'll see if it can find me something with, like, a preference that may be, like, a flexible preference. This one's remote, so I might be okay with it. And, again, like, I could add anything here. I could say, like, I know this is my background for my resume, but I'm actually looking for something totally different. You know, you can make these career shifts. You can make whatever, you wanna do here. So, it's very flexible. Okay. It found me a similar job in Chicago. That sounds great. And so I can say, like, maybe I just want the requirements. I don't really wanna open this all up. Just just tell me what I need to know. And this is kind of the power of Identik AI is there's kind of these no dead ends. It's very flexible. It's gonna tell me what I asked for, and it's not gonna and it's you know, I could come in here, upload my resume, choose a job, good to go, or I can have this back and forth where, I get more granular about what I care about. So here it is. It's telling me all these requirements. I can say, like, you know, what what are the skill gaps I have? Am I qualified for this job based on what it knows about me for my resume and what it knows about best practices in hiring. Because this these these, agents really are experts. They're provided with a ton of information about best practices and how to hire, and they use that to inform how they think and how they plan and how they reason. So so here it is. It's, like, really contextualized to me. It says you have a master's in communications. It exceeds your bachelor's requirement. You've got, you know, these, six years of relevant experience, which is close enough, it says. You know? You've got this management requirement that you meet, and you've got a skill set. So it says, really, the only gap is that I have six years experience instead of the seven, but it's probably enough to compensate that I have a master's and all this other stuff. So I can say, yep. Love to apply. And, again, I can apply here in the chat. So, yeah, that's that's our match and apply solution. I would love to take questions or if anyone wants to try something here. You can throw it in the chat if you wanna try something specific here. I'll do that. Alright. That was awesome, Kathleen. And we actually, if you wanna stay on stage, we do have a question for you. There was a question around, what mechanisms are in place to prevent AI drifting, where the agent's behavior or decision making might, subtly change over time. Sure. So we're using a, a large language model that follows our system instructions and orchestration and all that for the agent. And so we have, obviously, we have a whole set of tests, that are in place to make sure that those results stay the same. And even though large language models are not deterministic, we have these benchmarks for how to what we expect the results to look like over time. And we do monitor that as well as monitoring kind of live traffic to see how that's looking, over time as well. We're also, also kind of versioning these things so we can compare different versions of how it works over time and see if there's any shifts that's either based on something we've changed explicitly or based on that AI drift. So, yeah, that's a good question. I will monitor that. So we're getting a little bit of feedback. I'm not sure if it's one of our speakers. Sorry, guys. Alright. Let's see. A question there's a couple of questions on is this, is the AI agent another SKU? Is it available now? Answer is yes. This is an add on addition to your hiring work flow. So if you are interested, definitely, reach out to us. Or if you already have if you're already customer and you have an account team, you can reach out to learn more. There is a question in here around, can we, do you have the control to enable, disable the AI? I'm not sure if that was being asked for the chat or for interview insights. So Jeffrey or Kathleen, if you want to speak to that one. For the chat, it's all AI powered, but I'm guessing this is for insights. Okay. Yep. Yeah. For it to be insights, what we're gonna do is make it very configurable so you can turn it on or off different features, based on what your company's, comfortable with. And then, like so for example, if your company's not ready for everything that I showed today, but you wanna start with some of the transcript features, if you feedback features, you can do that. And then, you know, hopefully, like, over time, you can turn on more features to to unlock more of the value for your for your, for your workflows. But you can turn off pieces of it. Yeah. So you should be able to turn off those pieces that are, for example, AI overview if that's something that you'd wanna start with. Great. Awesome. Now there's a couple questions on pricing. Definitely reach out to, Hirevue or, like I said, if you have an account team, to learn more about the cost of these products you saw today. Yes. The session is recorded. So we'll be sharing that out. Sorry. Let me just scan through to try to get to another see here. Is the chatbot multi language? I'm assuming that's for chat, French, and Spanish. Go ahead, Catherine. So, for our release right now, our beta release, the chatbot is not multilingual. Although because it's powered by these large language models, it's very good at handling different languages. But for our, full release, yes, it will be multilingual. And then, there is a question. Will it automatically generate a Workday profile when they click apply? Yeah. Yes. So it we have this bidirectional integration with the ATS. I should have talked more about that because it is very cool. So we're there's not a bunch of setup to get your jobs in or pulling in the jobs from your ATS with the integration. And then, yes, we are, when someone is applying, we are submitting that application back in Workday or whatever your ATS is. So, yes. Perfect. Here's one for Marcy. That's fun. I love seeing all this innovation. What will be the next big thing coming? It's a great we have a lot more coming down the pike. Probably the biggest thing that I would highlight that you should dial dial back in for is we have, an assessment builder, a product that we're launching, later this year, which really, builds on all the incredible science that we have. Just quickly, the high level, like, workflow is you can start with a job title or a job description. The tool will discern the core skills associated with that, make a recommendation for what type of workflow, be it, a video interviewing, or an assessment. It will build that assessment, and then it'll put it into the workflow. And has a lot of nice capabilities around, if you wanna use AI, if you don't wanna use AI. The user has some, interactive elements and through this wizard of building this assessment, that's already been built on, predictable and validated skill work that we've done over the last twenty plus years. So very exciting about that. And so definitely dial back in to learn more. Great. Alright. Jeffrey, I have another one for you. Couple on around interview insights. Since the AI overviews and insights are based on a transcript of the interview, How do you avoid bias against people with accents or disabilities where the transcripts may mistranscribe? Yeah. It's a great question. It is something that we care about a lot. I will say, the transcripts that we generate, and these are transcripts that we use for our AI AI score interviews and also transcripts will be generated for the on demand. We're very careful to make sure that we're getting transcripts that are not that are, as much as possible, not biased by accents or those kinds of things. And the transcriptions that we do today are, are are such are just like that. Like, they do not we've done studies on how they do how they work with different different accents and those kinds of things, and the transcripts are are very accurate regardless of accent. So that's really important to us. I'll say for when you're using Zoom or Teams, for your live interviews, then we're using those Zoom or Teams transcripts. I we haven't done ourselves any direct studies on those on those, transcription engines, which we will probably will do some of those as well. But, I think Zoom and Teams is also pretty good at at transcribing regardless of accent. But it's something that it's a good question. It's something that we'll be keeping an eye on and making sure that we're that we're, you know, we're offering a solution that, reduces bias as much as possible. I will say that I've seen cases that where our transcriptions can transcribe better than I can myself, or sometimes even from these little bit difficult to kind of understand some of the words that are coming in. So the accent I'm not familiar with, but the transcriptions are are even better at that. So, it's, again, important. It's something that I think that right now we're handling pretty well. Jessica, I think you're on mute. Sorry, guys. Marcia, this one's for you. Do you, do we integrate with Dayforce or ADP workforce? Yeah. So we one of the enhancements that we, launched, earlier this spring was an enhanced integration platform. So we could have, ATS integration with pretty much most any ATS out there. So, those are things that we would love to, you know, talk to customers about of how we can integrate. The more integrated it is, the the easier it is for folks. Great. And then we do have a couple of questions around, you know, how do we get, a deeper presentation on the road map and enhancements beyond today. And I would encourage you to, reach out to Hirevue or the account team. Marcy and her team are always, happy to do a deeper dive on that. Absolutely. We have a lot more, to share, so, we would love to have that opportunity. Alright. K. Let's see. I will say, Jessica, while you're while you're looking for other questions to to kinda pull up in this in this call, I'm seeing a lot of questions about about questions about bias and how we're working in those kinds of things, and I just wanted to just call up for everybody here, something that's an important topic for us. And so even if we're not able to address your specific question today, please do feel do feel free to come reach out to us, and we'll be happy to have the conversation and even bring in some of our experts to have that conversation with you. Let's see. Looks like we've addressed most of these in one way or another. Another on the session being recorded, which is a yes. Alright. It looks like here's one last one around we talked a lot about better matching. What exactly do you mean by that? Kathleen, you wanna take that one? Yeah. Yeah. So it's really important to us that we are matching people to jobs based on things that really matter. And, obviously, at this pre apply stage when you're first interacting, we don't know that much about you. It it's just what you provide. But we do know a lot about hiring here at Hirevue, and so we know, we have all this wealth of information about how talent is matched to opportunity, and we wanna provide that information to the agents, right, to these chatbot agents that are, helping you search for a job, helping candidates get into the right position. So, so to do that, we're employing these, Agenstic AI strategies that help the agent think like an IO psychologist. So the agent is gonna go access, all those IO psychology information that we have here at Hirevue. It's going to kind of know about the best way to do things, and it's going to, help match the candidate to a job with those best practices in mind. So creating these candidate profiles of things that matter, taking into account things from the job description that matter, and matching the candidate to the job. So it's exciting. Great. Great. Alright. We're just right at time. So thanks everybody, and, hope you enjoyed the Innovation Roadshow. Thanks, everyone.