Jev Is Unfair For Work (5 Use Cases)

Jev is an absolute game changer for marketing. Everybody's talking about it, and it is crazy because it's not necessarily an LLM. It's something that you add into the LLMs that you're using right now, and it will turbocharge all the work that you're doing. And so when you have a Jev that can help you make decisions in here, it makes it better, right? And so it gave 40 ideas here, and what Jev did was it evaluated and said, hey, do we already have similar content? So here's 40 ideas. It shortlisted 10, so it eliminated 30 off the, off off the bat and say, hey, is this type of content already live on your website already? Because all the knowledge work that we do, there's a lot of time that we spend classifying things. So I look at it as a classifier. It can intake the data that you have that's coming in and it can classify it very quickly. So imagine that you have a corpus of 500 leads that are coming in, 500 emails that you have to evaluate or thousands of them. It can look through it very quickly. And you can see over here, not only can it evaluate very quickly, but it's also very cheap as well. And they're not going to charge you on output tokens. It's really the input tokens that are coming inside, right? And so the way you want to think about Jev is it is a middle layer that you add into the work that you're doing. And so you can see here, like, it's classifying what's happening inside of Doom, for example, right? So you can see, you know, over here, the player's playing Doom, and then it's classifying everything that's happening inside of it. So that's one piece of it, but there's also examples where it's classifying what's happening on Wikipedia. And so you can see there's a little WikiRace tool that was run over here, and you can see how quickly it's able to JEV up here. Boom, done. It's done so quickly. All these other ones, they take more time. Second place, third place, this one's taking forever down here. And then when you look at the benchmarks, let's just take a look. Okay, it happened in 0.827 seconds. But you can see here the, the cost is way less, right? And when you compare it to Claude Sonnet 5, it is 34.5x less, okay? And you can get the work done a lot faster. So I think these are the absolute best marketing use cases that you need to start with, with Jeff. And hopefully this is going to give you some ideas to get going. So let's get into it. So the first marketing use case is obviously you're going to use it for short form. And so the cool thing is I made a short form content library that spots all the content formats that I like on Instagram, for example, right? And so you can see here, this is with, with Ryan Deiss. Um, the cool thing is you can pick the different ideas that it's given me already. And this is with Jev. Before, it didn't have Jev. It just gave me like a library, like it just showed me all the formats, and that still required more thinking. But the cool thing here is like, okay, it's like choose your idea, rank 5 marketing jobs by how they are ready for AI. Okay, so you can see here's what to borrow, over here's a visual reference. And like, I like how it's explaining it to me where I don't have to think as much, right? Right. So I didn't have this before until I added Jev in today. And that makes it a lot more powerful because then I can just kind of get to the point, right? So when it comes to just selecting the right content formats and also content clipping as well. So when you want to clip the right moments, you can just make sure that when you're using something like a JEV, you have to kind of set the eval gates for it, right? Which means the criteria that you define as important. But if I'm looking for something around AEO or SEO, maybe I want to make sure that the content hasn't been published before and I want to make sure the content is actually a fit for my ICP. But Jev will really quickly figure out how to sort it. And the next use case I'm going to show you is around AEO and SEO. So I've added a massive upgrade to my AEO SEO bot inside of GrokBot. So you can see what this does is it looks for ideas to mine from basically all the content that I've been putting out, or maybe internal calls that we've been having, or customer calls, sales calls, things like that. It looks for content spikes, right? And it gives me a set of ideas to look through every day, right? And so it gave 40 ideas here, and what Jev did was It evaluated and said, hey, do we already have similar content? So here's 40 ideas. It shortlisted 10. So it eliminated 30 off the bat. And it said, hey, is this type of content already live on your website already? Okay. Right. And so I can kind of fix the evals here, but this is its first pass at it and I didn't really give it any guidance. So it's like saying, hey, even though— so it's saying, hey, this is already live. It gives the justification for it. And then from here, I'm just like, okay, I just quickly look at it and I can say, okay, well, Let's just go for these over here. But here's a compare table and we can just see that now it's basically drafting it and it's putting it into— here are the drafts. I can just review it afterwards. So anyway, from an AEO SEO standpoint, I made this bot already. It's sitting inside of GrokBot. Notice that I'm using multiple harnesses here. I'm using Codex, I'm using GrokBot, I'm using all these different harnesses and I'm using Hermes as well. I would recommend that when you're testing Jev, just think about the highest leverage thing. Just go to each of your harnesses and say, hey, based on what you know about me, what is the highest leverage use case we have for type-safe Jev? And so, and I found that you just can't call it Jev. You should just say type-safe and get the API in there and then let the harness figure it out for you and you could work with the harness. If you are enjoying this video around talking about Jev, then you will enjoy going to Single Brain. So Single Brain is where we help people with AI marketing implementation, where we have these marketing agents that live inside of Slack and Teams. And they help execute on AEO, SEO. They help execute on creative scaling. They help execute on outbound email infrastructure. And there's a whole host of other things. If you want to work in the future and you want to understand how marketing services software is going to look, we're already doing it right now. So just go to singlebrain.com and we'll see you on the other side. So the next one is around lead flow, specifically speed to lead. Okay? So when I think about leads that are coming into the business, if you're marketing, you're trying to drive People to the point of sale, right? Well, also you wanna make sure that those leads are getting to the sales team. And so just using Grokbot here, we have a speed to lead bot, and this is a speed to lead product-led growth qualifier for Eric. And it'll qualify leads coming in from inbound forms, and then it'll figure out, hey, is this an ICP fit? Should we move them faster? And it'll score them, and then it'll basically decide, should we be calling them within the first 60 seconds? But it was already good on its own and we were testing this because building this with GrokBot, you can use xAI or you can use other voice agents, voice bots out there. But the cool thing is now that you have Jeff, it will help you evaluate if someone is a fit much faster and decide if you should be calling the tier 1 leads, tier 2, tier 3, and maybe it'll route, right? So it's not going to— once it makes the decision to classify for you, you can have maybe a text message if it's a tier 1 lead. Sent over to your salespeople. Okay, if it's tier two or tier three, maybe they get a phone call speed to lead. Okay, if they're unqualified, maybe they get a text message or an email saying, "Hey, it's that we're not the best fit to work with you, but maybe we can refer you to one of our partners. Would you like that?" And you can have an agent handle it that way, right? But anyway, so I basically, I the team gave feedback on the speed to lead bot, and then I was just asking. I was like, "Hey, is this um you know should we just get Jev in there?" And it's like, well, here's where it helps us. It replaces our backup rule score and it'll catch a student job seeker DQ before we burn a dial. It'll keep Alfredo's Slack queue high signal when confidence is low. And so, okay, that's interesting. And so where it doesn't talk to leads, it doesn't transfer for us. Okay. But classification alone is really helpful. Just think about adding it. To all the workflows that you're using right now, the skills that you're using the most, the routines that you're using the most, the loops that you're using the most, and just try to insert it in there and see how high leverage it is. And then once it gets installed, re-ask it again. Hey, now that you built this, how would you change the classifiers out there to make sure that we get maximum leverage? I'm always asking about more leverage, more leverage, more leverage with these things, and it does a pretty good job of answering. By the way, guys, I just want to show you an example here. So Jev actually found a new moment. I actually haven't seen this clip before. And so let's just take a look at this together. So from one little video that I made on YouTube, it created these carousels over here, and we know that this type performs really well on Instagram. Basically from one piece of content, we've created 4 new pieces of content that can then be published to multiple channels, right? And so when I say kind of tongue-in-cheek that this has replaced my marketing team, the way I think about this is in many ways it has. It has replaced the people who don't know how to adapt. But it hasn't replaced the people who know how to run these workflows. That was actually really good. There's a good hook and I might change this overlay up top over here, but I haven't seen this one before. I'm like, okay, I'm at the point now where I'm just like, hey, this is actually really good. Do we need to make this into a skill? From a workflow standpoint, this is how we should be working with this stuff. I was just like, hey, I want you to evaluate Jev over here and I want you to do it for content. What is the difference? It started to evaluate a bunch of our content over here. By the way, look at this inference cost over here, 0.001938. It took 204 milliseconds median response over here. What did it do exactly? It took a look at a bunch of content and it's like, hey, did you actually fulfill a complete thought over here? Not really. Again, this is like 0 to 1. Missing essential context. Yep, you're missing essential context. Visual proof needed. Looks like I got it. Topic value 1.15. Okay, so not that good, right? It's just not that good, right? And so we can go through here. You're like, I can trust that it's going to do a better job of finding the moments. And then you can let the LLM, right, plus Jev work together to get you better net results. And we all want better net results. And so that is the power of this. One more thing. We're talking about Jev, but also if you need your agents to talk to all the different tools that you have, well, you don't want to just have all your API keys out there and same thing with your your team, do you want to make sure that you have a gateway that you can use? So we have the SingleBrain Gateway. If you go to SingleBrain with the B, singlebrain.com/gateway, you can get free access to our gateway. And that's where you can have your tools condensed into one area and make it easy for agents to access and then also make it easy for agents to run through Jev and your life's just going to get a lot better. So singlebrain.com/gateway and we'll see you on the other side. So the next one I'm going to show you here is content clipping. So I briefly touched upon it, but this is an example. This is dummy data over here. Okay. So not a real dashboard, but imagine it looking, it's like, oh, hey, here are the candidate placements in the episode based on the ones in greens are probably the ones you should be attacking. Here's the one workflow, the one field every workflow needs or the meeting that should have been a queue. This is all general. But imagine that it's scanning a podcast that you've done. Maybe you spent an hour on making a piece of content or maybe you made a video like this that's 20 minutes or so. What could be good content that would be good for you to put into a mid-form piece that goes onto LinkedIn? So let's say it's 2 to 5 minute horizontal or so, or maybe it can be like a longer form video, 5 to 15 minutes that also goes onto LinkedIn or X. And then obviously you have the full video as well. And obviously there has to be a payoff, overlay and all that that you have to be optimizing for each channel. So what I would say here is it'll spot the candidates for you and I think that's helpful, right? I hate to show you another dashboard because I believe that humans have this disease called dashboarditis. Where we like to show each other these dashboards and act like we're doing something. But this is helpful because this helps me understand what's happening exactly. There's one for content patterns over here. So here's your engagement rate. Here's engagement rate by hook. These content patterns are working for you. These aren't working for you over here. You should be attacking these more. That's helpful data for me. Let's say you want to look at client health. So let's say, oh, this ARR needs attention right now. There's some expansion signals over here, explicit requests for more seats. This is more of a PQL, product qualified lead standpoint. Open issues by account over here. Here's what we should be working with. This is like a client services or client success person would probably be doing this. From a product standpoint, where are new users getting stuck? And so for me, it's very clear I can just constantly be attacking this every single week and asking questions. And for search and AI, hey, here's organic clicks by topic over here. But then if you look at customer voice, which is like from your Granola or your Gong calls, it's like, oh, what are buyers bringing up over here? And based on the topics, what topics are actually winning deals versus losing deals? So if you see a topic that's obviously winning deals for you, you should probably create more content around that and that should be sitting over here. You should be getting more AI citations in this area. And so you, depending on where you sit in your business, maybe it's just focused on product, maybe it's just focused on marketing, maybe it's just focused on sales. Or if you're an entrepreneur, you're leading your business, maybe you have something like this. But all these dumb dashboards we've been making now become way more valuable if you have the right connectors and you have something like a Jeff that's in there classifying for you so you can parse the data quickly, you can get to the point a lot faster, which means you can make decisions a lot faster, which means your business is going to grow faster. So we called out AEO, we called out what you could do from a speed-to-lead standpoint, from a content standpoint, from a clipping standpoint, from a format standpoint. We called out what you can do From even a reporting standpoint to make better decisions. So I would recommend what you do is you take the transcript from this video and then just dump it into your harness, put JEV in there, right? And put all your other skills and say, what is the highest leverage thing I should be doing with this? Here's this video. It says I should be using JEV, right? And based on what you know about me, what is the number one thing we should be doing? And then give me like a top 9 after that as well, okay? So hope you enjoyed this video and we'll catch you in the next one. Bye.