Jev for lead scoring and sales

Moritz Kremb

@moritzkremb

I built a Sales Copilot using Jev → Listens to sales call live → Tells you what to say next → Helps you follow the script and handle objections → Shows you what stage of the call you're in → Gives you live signals and probability of closing Demo below on a recorded sales call:

Romàn

@romanbuildsaas

JEV is INSANE. We gave it 700 high-intent leads and personalised outreach messages. In 40 seconds, it predicted how each message would perform, assigned a confidence score and detected lead-message mismatches. All for just $0.09. JEV can also score leads, analyse buying signals, match each prospect with the best message and identify the campaigns most likely to perform based on data. Coming soon to @GojiberryAI+ MCP. Comment “JEV” for early access.

Sarvagya Kulshreshtha

@sarvagya_kul

JEV is INSANE. We gave it 400 companies and one candidate profile. In 12 seconds, it predicted which jobs the candidate had the highest chance of getting, assigned a confidence score and detected job-candidate mismatches. All for just $0.0005 It can also score companies, analyse your experience, match you with the right roles and identify the opportunities you’re most likely to get based on your profile. Coming soon to @textbackdoor Comment “JEV” for early access.

Kai

@hqmank

I rebuilt my job crawler with Jev. The task: start at a company's official homepage, find Careers, and identify jobs that match my profile. Before, with an LLM: ~5 minutes. After, with Jev: just over 20 seconds in my test. Every company organizes its website differently. Jev identifies the Careers entry point, chooses which links to follow, recognizes job pages, and scores each role against my profile. This is where Jev makes sense to me: automation that needs lots of small decisions, with faster responses and lower costs than calling an LLM at each step. Packaged it as a skill: jev-job-hunter. Demo below.

Pierre-Eliott Lallemant

@pierreeliottlal

JEV is insanely fast. We gave it a massive dataset based on thousands of outreach messages and asked: Which intent signals generated the most booked demos? 40 seconds later, we had the answer. Cost: less than $0.20. JEV can also rank leads, measure prospect-message fit, and uncover what actually drives campaign performance. Coming soon to @GojiberryAI + MCP.

Vadim Choi

@vadimchoi

$10k/month just to decide which agent should answer. not to answer. to decide. group chat + @mentions: fast, cheap, terrible UX. an LLM orchestrator: nice UX, 4-7s and $0.00046 a message. @typesafeai jev: 0.29s median, $0.00002. same decision, $20/month.

Aron | TomorrowLab

@AronTomorrowLab

Jev is actually insane 605,461,046 tokens later: only 47% of the leads in our live campaigns actually fit the client's ICP we used to check ICP fit by sending agents to read every website (Claude Code or Clay). 409 companies took 38 minutes. fine for one campaign, but ofc not scalable for a whole database. now we write the client's ICP once (client.md), and Jev runs every company against it. 100k companies in a few minutes less volume, but better leads (and only relevant replies)

Loutchone

@LiorNsnd

bon un peu de serieux, jev peut etre vraiment utile... pour préparer un workflow de qualification de leads après un audit client, je veux un premier tri de ce que l’agent propose avant de mettre les étapes dans Obsidian. j’ai donné à Jev le contexte de qualification avec les étapes candidates. À lui de choisir pour chacune : retenir, clarifier ou écarter. sur cet appel API, il retient la qualification par téléphone et le rappel des prospects injoignables. Par contre le rappel à J+2 ressort « à clarifier » : le contexte fourni ne fixe aucun délai. L’enrichissement LinkedIn est écarté, il était hors périmètre. la première image, c’est sa réponse réelle avec les probabilités. La deuxième, le workflow qu’on en a tiré, avec le délai toujours à confirmer. dans mon process, je garde la main sur cette présélection : je vérifie et je valide avant de l’intégrer à l’audit. Voilà où je vois l’intérêt de Jev dans mon taf.

Han Keo

@KeoGrowth

En regardant Jev et Recursive Mass, je peut être sûr d'une chose les LLM ne sont pas la voie royal pour être le cerveau des agents, du moins pas pour longtemps. Jev même n'est pas encore au top du top sur ces probabilité montre déjà ces preuves pour ses prises de décision (plus rapide que des LLMs) Et Le Recursive Mass (pensées latentes sous forme de nombres) montrent qu'il est plus efficace pour les agents de communiquer entre eux partageant leu états internes plutôt que générer du texte compréhensible par des humains. Une chose est clair, on ne doit pas utiliser les IAs pour nos agent commet on les utilises pour nos discussions. Les Modèles n'ont pas fini d'évoluer :)

Aradhye Agarwal

@AradhyeAgarwal

While the whole world is going crazy over Jev, I give you a more skeptical take. I think it's a great model––but its modeling choices cause real failure modes. 1. The way the speed argument is presented in the quoted tweet isn't valid IMO. You can simply treat every row in the output as independent and sample the autoregressive LLM in parallel. 2. The main benefit of autogressive models is enabling increased computation depth with the same amount of model parameters (via CoT). I don't see how that would be possible with Jev. 3. Jev isn't a magic wand that will one-shot any decision task. I can come up with a task where LLMs do better in terms of efficiency: suppose you have 100 choices out of which you have to choose 10. Also suppose someone has already chosen some 10 choices. You have to tell the "goodness" of this choice. With Jev you could naively multiply the probabilities of choosing the options, but this would not work. Making one choice changes the probability distribution of the other options which has to be accounted for. A simple example one of the chosen options is repeated. Jev will give both the options equal probability (due to symmetry) but it's unlikely that this would be the optimal choice (assume repeating options is downright bad). With LLMs however you can prompt it like so: "okay you have 100 options and you have to answer which ones (a total of 10) you would choose in a sorted order." Then gaslight the LLM into thinking it generated the 10 options (in sorted order) by just prefixing those tokens to the assistant channel and then measure the probability of generating that output. This would happen in one-shot. A careful reader might say: "but we can do the same thing with Jev too right?" Yes that's correct, you could have Jev choose one option then conditioned on that choose the second one and so on. Then multiple the probabilities. But the problem is that this will take 10 sequential steps; you can't one-shot it unlike with an LLM where there is no recurrence.

Yannis

@yannnis

Enough is enough! Is it that LinkedIn sucks or what? Let's settle this. I go viral on LinkedIn for slop (attached) Then I spend 2 days to build a JEV product to scan Reddit for leads. And get tumbleweed!?! How??

Prasad Pilla

@prasad_pilla

AGREED. team built an internal tool using Jev to qualify candidates, and Jev itself is genuinely impressive at what it’s designed todo. but recruiting is hard. there are too many variables that don’t fit neatly into a rubric: context, trajectory, ownership, quality of work, potential, and the signals you only catch by actually digging into someone’s work tbh https://x.com/idleshubh/status/2102429846660079971?s=20