Jev is not a standalone LLM but a cheap, fast classifier layer added inside the AI tools you already run, and it earns its keep by sorting large batches of work, like 40 content ideas or thousands of leads, down to the few worth a human's attention.
Who This Is For
Read if. Skip if.
READ IF YOU ARE…
You run marketing or growth work through AI harnesses like Grokbot, Codex, or custom Slack bots and want a cheap layer to pre-sort the output.
You deal with high-volume batches, content ideas, inbound leads, long-form recordings, dashboards, that currently get triaged by a human reading through all of them.
You're comfortable wiring a new model or API into an existing workflow rather than adopting a finished, packaged product.
SKIP IF…
You're looking for a consumer chatbot or a single do-everything AI app; Jev is a middle-layer add-on, not a destination product.
You want a proven, benchmarked case study; several examples here are early, unreviewed tests and the reporting dashboard runs on demo data.
TL;DR
The full version, fast.
Jev, from TypeSafe AI, is a small, cheap classifier that plugs into AI harnesses you already use to rank and filter batches of work fast. Eric Siu tests it across five marketing jobs: picking the strongest content format from a list, shortlisting SEO and AEO ideas by checking what's already live, scoring inbound leads into speed-to-lead tiers before a human calls, scanning long recordings for clip-worthy moments, and parsing reporting dashboards for client health and content performance. The through-line is that Jev doesn't replace the decision, it replaces the manual sorting step before the decision, and it's built to run on nearly every input token rather than output tokens, so it stays cheap at volume.
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Cold open framing Jev as a game-changer added on top of the LLMs already in use.
00:13 – 00:34
02 · From 40 content ideas to 10
First look at Jev shortlisting a content idea list by checking for existing similar content.
00:34 – 01:02
03 · What Jev does
Frames Jev as a classifier middle-layer for high-volume triage like sorting thousands of leads.
01:02 – 02:05
04 · Doom and Wikipedia demonstrations
TypeSafe AI's own benchmark demos: classifying a Doom playthrough and a Wikipedia speed-run, with cost and speed numbers on screen.
02:05 – 03:04
05 · Use case 1: Content formats
A content-format picker ranks marketing job ideas by how AI-ready they are and explains what to borrow.
03:04 – 03:20
06 · Define your criteria
Sets up eval gates, unpublished, on-ICP, before clipping or selecting content.
03:20 – 04:24
07 · Use case 2: SEO and AEO
An AEO/SEO bot inside Grokbot mines content ideas from calls and posts, then Jev shortlists them against what's already live.
04:24 – 04:55
08 · Using Jev with your AI tools
Advice to test Jev across every harness (Codex, Grokbot, Hermes) and ask each one for its highest-leverage use case.
04:55 – 05:26
09 · Single Brain
Sponsor break for Single Brain's managed revenue agents.
05:26 – 07:19
10 · Use case 3: Lead qualification
A speed-to-lead bot uses Jev to score inbound leads into tiers and decide call, text, or referral routing.
07:19 – 07:42
11 · Improve your classifiers
Habit of re-asking the harness how to tighten the classifiers after each one ships.
07:42 – 09:35
12 · Use case 4: Find useful clips
Jev scores transcript moments for completeness and topic value to surface clip-worthy content, including one new moment the presenter hadn't seen before.
09:35 – 10:07
13 · Single Brain Gateway
Sponsor break for Single Brain's tool-connection gateway.
10:07 – 12:55
14 · Use case 5: Reporting and decisions
A dashboard (demo data) surfaces content patterns, client health, product friction, and search/AI citation topics for faster decisions.
12:55 – 13:29
15 · Where to start
Closing advice: feed the video transcript into your own harness and ask it to rank the highest-leverage use case for you.
Atomic Insights
Lines worth screenshotting.
Jev is a classifier layer added inside an existing AI harness, not a standalone LLM you talk to directly.
In a benchmark shown on screen, Jev ran a Wikipedia classification pass 4.26x faster and 2.6x cheaper than a comparison model.
Against Claude Sonnet 5 on the same task, Jev's cost came in roughly 34.5x lower.
Jev is priced mostly on input tokens, which matters when the job is reading thousands of items rather than generating long output.
Fed 40 raw content ideas, the workflow used Jev to eliminate 30 of them by checking for existing similar content, leaving 10 to review.
For lead qualification, Jev's classification decides tier 1, 2, or 3 routing, but a separate system still makes the actual call, text, or handoff.
A speed-to-lead workflow used Jev to catch a low-signal student or job-seeker inquiry before it burned a live sales dial.
One video clip-mining example measured an inference cost of $0.001938 and a 204-millisecond response for scoring a single content clip.
The presenter reused one recorded video to generate four separate pieces of content for other channels once Jev flagged the strongest moment.
The stated workflow habit is to keep re-asking the harness for the highest-leverage use case after each new classifier gets installed.
The reporting and decisions dashboard shown was explicitly demo data, not a live client account.
The presenter's own framing: Jev replaces manual classification and triage, not the judgment calls or the actual outreach that follow it.
Takeaway
A classifier layer, not a chatbot, is what makes AI triage cheap at volume
WHAT TO LEARN
Adding a fast, input-token-priced classifier in front of an existing AI workflow turns slow, manual sorting into an automated first pass, as long as a human or the harness still owns the final judgment call.
05Use case 1: Content formats
A classifier can rank format ideas by how ready they are for AI production, not just list them.
Explaining the reasoning behind a ranking (what to borrow, what to change) cuts the thinking a person still has to do.
07Use case 2: SEO and AEO
Mining ideas from internal calls and existing posts gives a classifier real material to shortlist, not a blank prompt.
Checking 'is this already live on the site' before drafting prevents duplicate content work.
10Use case 3: Lead qualification
Classification alone, without talking to the lead or transferring the call, is still enough to change who gets contacted first.
A supervised go-live (classifier suggests, human still approves) is a safer rollout than letting it act immediately.
12Use case 4: Find useful clips
Scoring for 'does this fulfill a complete thought' catches clips that sound fine but are missing context.
A classifier finding a moment the creator hadn't noticed themselves is the actual proof of value, not the cost or speed numbers.
14Use case 5: Reporting and decisions
A dashboard is only useful if it changes a decision that week, not just displays a metric.
Cross-referencing which topics win deals in sales calls against which topics get organic search traffic points to what content to make next.
Glossary
Terms worth knowing.
Jev
A fast, cheap AI classifier product from TypeSafe AI that gets added into existing AI workflows to evaluate, score, or sort batches of content, leads, or data.
TypeSafe AI
The company behind Jev, credited on screen for the Doom and Wikipedia classification benchmark demos referenced in the video.
AEO
Answer engine optimization, the practice of shaping content so AI answer engines and chatbots are more likely to cite or surface it.
ICP
Ideal customer profile, the specific type of buyer or lead a business is trying to identify and prioritize.
PQL
Product qualified lead, a prospect identified as a good fit based on how they've used or engaged with a product rather than by a form fill alone.
Speed to lead
The practice of contacting an inbound lead within minutes of signup, since response speed strongly affects conversion.
Eval gates
The criteria a classifier is given up front to decide whether something passes or fails, such as requiring content to be unpublished and on-ICP.
“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.”
self-aware, quotable jab at reporting culture→ newsletter pull-quote↗ Tweet quote
The Script
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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.
It 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? 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 right 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 on output tokens it's really the input tokens uh that are coming inside right and so the way you want to think about jeff is it is a middle layer that you add into the work that you're doing and so you can here like it's uh it's it's classifying uh what's happening inside of a doom for example right so you can see you know over here the the player's playing doom and then it's classifying everything that's happening inside of it so that that's one piece of it but there's also examples where it's it's classifying what's happening on wikipedia and so you can see there's a little wiki race tool that that was that was ran over here and you can see how quickly it's able to jev up here boom done
Right. It's done so quickly. All these other ones, they take more time.
Second place, third place. This was taking forever down here. And then when you look at the benchmarks, let's just take a look.
OK, it happened in point eight to seven seconds. But you can see here the cost is way less. Right.
And when you compare it to Claude Sonnet five, it is thirty four point five X less. OK. 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 Ryan Dice. The cool thing is you can pick the different ideas that it's given me already. And this is with Jeff before it didn't have Jeff.
It just gave me like a library. Like it just showed me all the formats and that's still required more thinking. But the cool thing here is like, okay, it's like, choose your idea, rank five 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.
I like how it's explaining it to me where I don't have to think as much. Right. So I didn't have this before until I added Jeff 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 Jeff, you have to kind of set the eval gates for it, right? Which means the criteria that you define as important.
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 Jeff 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 Jeff did was... It evaluated, it 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, you know, 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 just say, okay, well. let's just go for these over here but here's a compare table and we can just see that um Now it's basically drafting it and it's putting it into here, the drafts.
I can just review it afterwards. So anyway, from an SEO standpoint, like 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 harness and say, hey, based on what you know about me, what is the highest leverage use case we have for TypeSafe Jev?
And so, and I found that you just can't call it Jeff. You should just say type safe and like get the API in there and then let the harness figure it out for you. And then you can work with the harness.
If you are enjoying this video around talking about Jeff, 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 want to 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 um and it will qualify leads coming in from inbound forms and then it'll figure out hey is this an icp fit should we move them faster um and it'll score it'll score them and then um 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 kind of 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 one lead, tier two, tier three, and maybe a 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 one 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 um 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, you know, here's where it helps us. It replaces our backup rule score, you know, and it will catch a student job seeker DQ before we burn a dial. It'll keep, you know, Alfredo's slack Q high signal when confidence is low.
And so, you know, okay, that's interesting. And so, you know, where it doesn't, it doesn't talk to leads. It doesn't transfer for us.
Okay. But classify classification alone is really helpful, right? Just think about adding it.
to all the workflows that you're using right now, the skills that you're using, the most of routines that you're using, the most loops that you're using the most. I 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 Jeff 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 four new pieces of content that can then be published on 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.
And so there's a good hook and I might change this overlay up top over here, but I haven't seen this one before. And so 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?
So from a workflow standpoint, this is how we should be working with this stuff, right? So I was just like, hey. you know, I want you to evaluate Jeff over here and I want you to do it for content.
Like what is the difference? And so it started to evaluate a bunch of our, um, our, our content here, by the way, look at this inference cost over here, 0 .001938. It took 204 milliseconds, medium response over here.
So 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.
Okay. So again, this is like zero to one, right? Missing essential context.
It looks like I got it right. 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, and, and then you can let the LLM right. Plus Jeff 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 Jeff, 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 the same thing with your team. Do you want to make sure that you have.
a gateway that you can use so we have the single brain gateway if you go to single brain 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, right? This is like 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 at 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 two to five minute horizontal or so.
Or maybe it can be like a longer form video, five to 15 minutes that also go 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.
You have to be optimizing for each channel. What I would say here is it will spot the candidates for you, and I think that's helpful. 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 are the... 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, okay?
Let's say you want to look at client health, right? 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.
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 of 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 call that a EO.
We call that what you can do from a speed to lead standpoint, from a content standpoint, from a clipping standpoint, from a format standpoint, we call that 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 Jeff in there, right?
And put all your other skills. I say, what is the highest leverage thing I should be doing with this? Here's this video.
It says I should be using Jeff, 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 nine after that as well.
Okay. So hope you enjoyed this video and we'll catch it in the.
The Hook
The bait, then the rug-pull.
Eric Siu opens by calling Jev an unfair advantage, then spends the next thirteen minutes proving it: not with a pitch, but with five live workflows where a cheap classifier layer does the sorting a person used to do by hand.
Frameworks
Named ideas worth stealing.
02:05list
Five Jev marketing use cases
Content formats
SEO and AEO
Lead qualification
Find useful clips
Reporting and decisions
The five live workflows the video walks through where a classifier layer replaces manual sorting.
Steal forauditing any AI workflow for a spot where a cheap classifier could pre-sort volume before a human or an LLM makes the final call
CTA Breakdown
How they asked for the click.
VERBAL ASK
13:00next-video
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An agency owner walks through the workflows where an AI agent now drafts YouTube packaging, brand vision docs, recruiting outreach and social carousels, and where a human still has to step in.
Eric Siu runs the same four marketing prompts — a website build, a thumbnail redesign, a growth-strategy memo, and a batch of social clips — on GPT Sol 5.6 and Claude Fable 5, and picks a winner based on which one actually finishes the job.
Eric Siu walks through where he'd bolt TypeSafe AI's Jev classifier onto his existing agent stack, using one real vendor benchmark and a run of admittedly fictional dashboards to make the case.
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