The Use Case by RecruitingDaily
RecruitingDaily discusses with guests how practitioners make the business case or the use case for purchasing their technology. Each episode is designed to inspire new ways and ideas to make your business better.
The Use Case by RecruitingDaily
Storytelling About ishield With Kesavan Kanchi Kandadai
We also explore iShield's incredible applications throughout the talent lifecycle, from employer branding to recruitment. Learn how companies are harnessing the power of this AI platform to craft job descriptions, career website copy, and candidate nurturing campaigns that are tailor-made for their brands. Plus, discover how iShield seamlessly integrates with popular applicant tracking systems and talent CRM platforms, ultimately creating a more inclusive hiring process. Don't miss this enlightening conversation on the future of AI in talent communications!
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Welcome to Recruiting Daly's Use Case Podcast, a show dedicated to the storytelling that happens or should happen when practitioners purchase technology. Each episode is designed to inspire new ways and ideas to make your business better as we speak with the brightest minds in recruitment and HR tech. That's what we do. Here's your host, William Tincup.
Speaker 2:This is William Tincup and you are listening to the Use Case Podcast. Today we have Keshavon and he's on. His company is called iShield and we're going to learn about the use case or the business case for iShield. Keshavon, would you do us a favor and introduce yourself and iShield?
Speaker 3:Thank you, William. I'm super excited to be here. I'm Keshavon, co-founder and CEO of iShield. iShield is a generative AI platform that helps HR teams create talent communications 10x faster and at scale and bias free. So that's what we do.
Speaker 2:Yeah, yeah, So telecommunications. give us some examples of that.
Speaker 3:The simplest form of talent communication is a job description, and we have recruiters and sources who are creating a job description and they need to publish the job description. Typically, it's not enough that you create a job description, but you also create multiple versions of it for internal job posting. Then you have a social media post that you want to promote the job description, then you want to probably send it out in a job alert email campaign, then you will do another version for getting referrals. So a recruit and source does all of these content that they are creating, and so we've created an AI platform that can help companies create that content using the AI so it's faster and brand and consistent all the time.
Speaker 2:So are we using chat GPT at all? I love the part of the AI because it gets smarter right. So the more you use it. But in practice, the more you use it, especially with job descriptions, the smarter it gets with the nuances of your company or your hiring managers or your recruiters, etc. Yeah, so the question is are we interacting with chat, gpt or large language models in general?
Speaker 3:Yeah, so chat GPT gives you a average job description and it's not customized to the brand. It's not free of biases or errors. It's not personalized. So what you have is like a classic case of a good English speaking intern writer, but the amount of time you take to make it precise for your brand is probably the same as you creating it manually. That's the state of affairs to the MSHAS. We've created our own AI models, we file patents for some of these and we do three things. Of course, we use a large language model that speaks good English or that gives us good English content, but we trained our models on millions of job descriptions.
Speaker 3:So our millions of all talent, communications, i would say job description is one example, this could be an assessment, this could be a candidate nurturing campaign, this could be a social media post or an employer branding blog. It could be all of that content. So we made trained our models on millions of such data points So we understand the architecture and the content. Then we also are AI understands the brand. So when we deploy for a company X, then it would learn about all the brand personalization that is relevant for that company X. And, most importantly, we have built models that detects biases in AI recommended content, and this biases could be towards gender, race, religion, language, whatever that is. Our AI detects that there is a recommendation or a piece of content that has bias, so it auto eliminates and replaces that with bias free content. Essentially, in a nutshell, we make the AI really work for you as a brand. So I gave you a long answer. Yes, we do use the large language model, but you've built your own, we built our own for an enterprise. that's not enough.
Speaker 2:So, first of all, this is fascinating Bias is one person's bias might not be another person's bias. In fact, i asked them recently what's the difference between preference and bias, and they said preference is how you justify bias, which I found fascinating. So some folks this is also true in the sourcing world. So I know, you know this It's like somebody, people have a mandate, like we need to recruit more veterans or more people with disabilities, and maybe even more specifically with people with disabilities, etc. So what if one of your customers actually wants to be biased, or maybe prefer women for the engineering position, etc. Which would normally come out as bias? But can they tailor it? Is there a way to modify it for that particular post?
Speaker 3:Of course that is a preference, as you rightly put it, and it forms broadly under the realm of a company is they're taking a conscious action. So we want to differentiate between what is unconscious bias and what is conscious bias. So unconscious bias is something discriminating or having a piece of language that discriminates women or stereotypes women is an unconscious bias. But what the example that you have given is a customization for the brand where they so in our platform, they can customize and say this is the theme that we are creating this content for. If you're writing a women's day post, it is going to be about women. So, yes, so that's part of the customization. It's conscious, And so if it is conscious, then our filters let that through because that's the choice that the customer has made.
Speaker 3:That's first point. The second one is we don't have any auto correction modules for our bias. They're all assistive in nature. That is, we make a recommendation and it we still leave it to the human to decide whether we want they want to take that recommendation or let it go. So they have a discretion and that is how they can align it to their own goals and say yes, I understand that this is, this could be biased, but this is aligned with the program that we are running, So I will let it go. So that's okay. So our platform has? no, we have not built any restrictive capabilities yet.
Speaker 2:I love that. So, other than job descriptions because you're broadening out with talent communications what else are your customers using iShield for?
Speaker 3:Yeah. So if you look at the overall talent life cycle, the first part is the whole employer branding. So companies use us for creation of employer branding content career website copy, blogs, articles and they also use us to give them an audit. They use them to do an audit of their all their existing content to see how aligned and how branded that is and how bias free that is. So they get they use it for branding purposes. The second bit is recruitment. So recruiters and sources use our, use our AI to create job descriptions, candidate campaigns, nurturing campaigns, candidate emails, campaign content and emails and all the recruiter work, so to say, wherever they are creating content. So this is how companies are using our product today.
Speaker 2:It's almost if you're going to use Microsoft Word to open up and create a document, whatever that document might be, if it's social post, whatever do you don't need to do that.
Speaker 3:Correct. Yeah, a different way I put it is it's like having a full fledged content marketing agency work with you. It's just that. It is an AI.
Speaker 2:So what's the workflow for them? If, let's say, we're working with a company and they're going with through 500 jobs in a year, they're going to hire 500 people Is it recruiters and employer branding and sources like what? and then what I'm really trying to figure out is what's it connected to or is it connected to anything else within there kind of their talent? there's technology standard.
Speaker 3:That's where we differentiate ourselves from a lot of other tools people may want to use. We have built the integrations with the most popular applicant tracking systems and talent CRM systems. So let us say that the job post or the communication originates in greenhouse. Then we have a plugin for greenhouse So you get the content recommendations right in the greenhouse platform. So this could be greenhouse, this could be lever, this could be Avecher, or you could be using Phenom people, smashfly. So we've integrated with all these platforms So you're not navigating away from the system where you're currently working and you're getting the content recommendations right inside the platform. So that's the use. But if customers do want greater account management and team management, then we do have a web app they can use.
Speaker 2:But most of our customers use our plugins in their HR ecosystem Right Almost as an overlay. So they're doing it and if they need to be published or it needs to be housed again in the ATS And on the marketing side, it would be also. Like I said, avecher, it'd be in some of the CRMs. That is that. I think you mentioned Smashfly, so that would happen there as well. Smashfly Phenom people yeah, yeah, have you been asked to integrate with some of the programmatic, the Pandologic Appcast, symphonytalent, some of those folks? have you been asked to integrate with the programmatic job That's coming? omnichannel job That is yeah.
Speaker 3:Yeah, We evaluated SymphonyTalent integration. One of the use cases there is production of variations of the ad postings. Oh interesting.
Speaker 2:At a mass scale. You can do. you can do A-B testing.
Speaker 3:We can do A-B testing, yes exactly Cool And you can do, and you can do variations, the same content, and you can keep refreshing, so to say, over a period of time, of the ad service. So, yes, we have, that's in beta. Right, we have, we are not live yet. It is something that we've been asked. We have a product for that in beta Love that OK.
Speaker 2:So let's do some buy side questions, because the podcast is trying to help all of us, but practitioners learn how to buy software And this is a new category. This is, i wouldn't say investors hate it when I say new category, but if they haven't bought something like iShield before, what should they be asking? What types of questions, if not specific questions, should they be asking in your team?
Speaker 3:So there are four questions that customers ask us repeatedly. Because this is an AI, everybody is concerned about data privacy and data security, because if we are learning about a brand, then people want to know where is that intelligence residing? So that's a question that people ask us. The second question is how we generate branded content. So they want to understand how unique it is going to be compared to, let's say, generating the content for some other brand, because, quite honestly and this is again if two teams use chat, gpt, they both will get almost similar content recommended. Same query, but here companies are concerned for us in terms of how unique is the content recommendation? The third question that companies ask us is the intelligence of people and persona and audience is locked up in HR systems that already exists in the company, so they know who their candidate is, what the candidate pool is, what their mix is. All that information, or the applicant data, and the information is locked up in ATS, crm, various systems.
Speaker 3:So the third question they ask us a lot is around personalization. In order for you to personalize, you need integration And you need to know these insights for you to generate personalized content. So people ask us a lot about are you integrated with And they list a few software they use. And that's what they ask. And the fourth question really is around people, communications, talent, communications needs layers of debiasing. So they are very curious what types of biases, at what level does it work? Can it work at a state level? Can it work at a company organization level? What about different geographies? So there are a lot of questions around unconscious biases. There are a lot of questions around compliance. There are questions around state level compliances And they really have a checklist they want to go through to say do you meet these requirements or not? So we keep customers ask us these four questions Security, data security and privacy, personalization through integrations and insights And bias.
Speaker 2:I love all those questions, especially the personalization Which you talked about at the very beginning. It's large language models that are out there. That's great, but if it's not personalized, you're still going to have to do a lot of work. I used the chat GPT this weekend and I had it right by bio, basically said the 1,000 words will last right, william, 10 cups bio. Then I flipped it around and I said OK, 1,000 words will last right, william, 10 cups obituary.
Speaker 3:Yeah, okay.
Speaker 2:Which is a little dark, which I understand. but the obituary was 95%. It was pretty spot-on And again, that's generalized, which people understand. that it is wonderful. It really is a kind of fun thing to do to really play with the large language models And, again to your point earlier, it can get you generic content if that's what you need. That is correct.
Speaker 3:It gets you started, it gives you a decent draft, but if you tried the regeneration process three or four times, you start getting more and more garbage, so it doesn't become more precise. It all depends upon how you query it. So there is a barrier for an enterprise to adopt it at an enterprise scale, and what we are doing is giving that last mile problem solution to companies to actually make it work for enterprise. That's what is probably the thing that companies need before they say okay, i'm using Generative AI.
Speaker 2:Right? Do you find that it helps with prospects if they've played with or taken around with open AI or chat, gpt or any of the others that are out there? Does it help if they know how to run queries?
Speaker 3:I think everybody has already tried it in some shape or form. So I think that question people have crossed that barrier already. I also think that a lot of companies have thought about integrating it but are also scared. We saw what happened with Samsung. I think that I was at a conference where I asked 72 CHRs were in a room, they were discussing a topic and only 10% were ready to use it at an enterprise level, but almost everyone was using it personally. So they've learned to the chat GPT and it helps that they know AI can do a lot of good stuff. but there are barriers to crosses. How I look at it.
Speaker 2:Do you feel like again running queries, even in Europe, because it's more specific to the company and to recruiting in general? do we need to teach them queries? Do we need to teach them how to write more specific, like a job description for a software engineer? Do I can write the query myself, but do we need to teach them that?
Speaker 3:To use our product. No, okay, when we deploy, we have abstracted all of that learning into the AI and that's how we deploy it for a company, and so they still use simple, plain English to say can you generate a job description for the following post or for the following role and give some basic information or data points, and that's it. That's all what they need to do. No query, training or any is required.
Speaker 2:Thank you, That's perfect, All right. Your favorite part of the iShield demo? when you get to show somebody that's never seen it before, maybe not even understand exactly what you do. What's your favorite part of the demo?
Speaker 3:Before chat GPT became popular, we already had the generative AI and when I used to demo that the AI creates the content, people did go wow, but there was also very healthy skepticism, right. But today that wow factor has gone away. But people do say wow when they see unconscious biases being highlighted by the tool. Right, and you make manual edits or AI is generating the content. All these biases are highlighted and you so that people do see, okay, wow, now I understand why AI requires these additional filters. I can see the value add. So that's my one of my favorite parts of the overall iShield demo Love it.
Speaker 2:Listen, this has been absolutely wonderful. I love what you're doing. How long have you been doing it? How long is I forgot to ask you at the beginning how long is iShield been available to folks?
Speaker 3:Two years actually. So we started very early, when it was still in the realm of possibility, the general generative AI things. We started really early and we started working on creating our own proprietary models. And yeah, here we are now, the years down the line, still working at it.
Speaker 2:Congratulations. You built something absolutely beautiful. Thanks for coming on the podcast.
Speaker 3:Thank you very much. This has been great.
Speaker 2:Thank you for chatting and giving me this opportunity 100%, and thanks for everyone listening to the Use Case podcast Until next time.
Speaker 1:You've been listening to Recruiting Daly's Use Case podcast. Be sure to subscribe on your favorite platform and hit us up at RecruitingDailycom.
