ZeroGold/call-coach-ai
A live sales-call assistant that sends the conversation to Jev after every sentence and shows the rep what to do next along with a confidence score.
ProjectConfidence-gated actions
Jev for lead scoring, sales and ecommerce: 9 projects
Sales and shopping both come down to matching: a lead to a score, a message to a likely outcome, a listing to what a shopper wants. A decision model can score each match as a typed number with a confidence value, or answer a yes or no about a trait the site's own filters do not cover. The examples in this hub run on Jev and other decision models. Examples include promptgtm-shared/clay-jev-people-ranker, which ranks B2B leads, ZeroGold/call-coach-ai, a live sales-call assistant, and NoSugarForKids by Nikunj Kothari, which scored thousands of snacks against several criteria. There are also shopping demos, like a natural-language Zillow search. Many entries are single demos, not maintained products. Before you copy one, check what data it needs, whether the scoring criteria are written down, and how a wrong score would show up. Check that you have the right to use the lead or listing data. Because the data is often personal, read how each project stores and sends it.
9 entries
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Showing 9 of 9
promptgtm-shared/clay-jev-people-ranker
A Clay agent skill and Python workflow that scores and ranks B2B leads with Jev for prospect qualification and people search.
promptgtm-sharedOpenpromptgtm-shared/clay-jev-people-ranker on github.com
Scoring 700 leads and outreach messages with Jev (X)
Given 700 high-intent leads and personalized outreach messages, Jev predicted how each message would perform, assigned a confidence score and flagged lead-message mismatches in 40 seconds for $0.09.
Romàn (@romanbuildsaas)OpenScoring 700 leads and outreach messages with Jev (X) on x.com476k viewsSpeed 700 in 40sCost $0.09
Discussion
A natural-language search for Zillow listings with Jev (X)
Jev scanned thousands of Zillow listings and classified them by traits Zillow's own filters do not expose, such as architecture style, renovation status and proximity to freeways, in under 20 seconds for $0.18.
Justine Moore (@venturetwins)OpenA natural-language search for Zillow listings with Jev (X) on x.com107k viewsScan cost <20s, $0.18
Discussion
NoSugarForKids
Compares packaged snacks for kids across brands, sorted by sugar content and filterable by age, allergen and category. Jev scored 3,000 snacks against multiple criteria in 28 seconds for $0.11.
Nikunj Kothari (@nikunj)OpenNoSugarForKids on nosugarforkids.com92k views3,000 snacks 28s, $0.11
ProjectComposite scoring
lurk by AnyAPI
Find Reddit buyer intent and see what every lead's data cost.
Kevin Wang (@mxfp4)Openlurk by AnyAPI on lurk.so84k viewsThreads scanned 4,000
Article
Ego Lite shopping decisions (X)
A video demo of the Ego Lite shopping agent using Jev with DeepSeek Flash, completing 20 Amazon product decisions in 3.71 seconds versus 54.45 seconds for GPT-5.6 Sol, with the same result on both runs.
ego (@ego_agent)OpenEgo Lite shopping decisions (X) on x.com63k viewsJev run 3.71s / 20 itemsGPT-5.6 Sol run 54.45s
Discussion
Finding the intent signals that book demos with Jev (X)
Given a dataset of outreach messages, Jev found which intent signals correlated with the most booked demos in 40 seconds for under $0.20; the poster says it can also rank leads and measure prospect-message fit.
Pierre-Eliott Lallemant (@pierreeliottlal)OpenFinding the intent signals that book demos with Jev (X) on x.com22k viewsTurnaround 40sCost <$0.20
Discussionsales-conversion-model-reinf-learning (claimed 2025 Jev precursor)
MIT-licensed PPO model over BAAI/bge-m3 sequence embeddings that emits turn-by-turn conversion probabilities without generating text (arXiv 2503.23303). Its author claims on Hacker News it is the same non-autoregressive, schema-constrained architecture Jev later shipped, a year earlier.
DeepMostInnovations (nandakishor_ml)Opensales-conversion-model-reinf-learning (claimed 2025 Jev precursor) on huggingface.co
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How decision models are used for sales and ecommerce
Lead scoring follows the same loop as other scoring jobs. Each lead gets the same typed questions, such as fit and intent, and arithmetic you can read turns the answers into a rank. The Clay people ranker does this inside a Clay workflow. Scoring 700 leads and outreach messages with Jev, by Romàn, also flags mismatches between a lead and its message.
Listing and product matching works the other way round. Instead of scoring a person, the model checks traits. Justine Moore's Zillow search had Jev classify listings by architecture style, renovation status and nearby features that Zillow's own filters do not expose. Ego Lite shopping decisions shows an agent making product choices with Jev and a second model. Verify any listing claims against the source.
The limit is feedback. Few of these entries show whether a high score led to a sale. A reinforcement-learning model in the alternatives list predicts conversion from the conversation itself, which is a different approach. Treat scores as a way to prioritize, not as a forecast. If you copy one, log the score next to the later outcome so you can check it yourself.
FAQ
Can Jev score sales leads?
Yes. promptgtm-shared/clay-jev-people-ranker scores and ranks B2B leads, and other demos rate leads and outreach messages. The scores rank leads for a person to review. They do not predict closed deals.
Can Jev help with product search or shopping?
Entries here classify listings by traits that normal filters miss, such as the Zillow search, and score products against several criteria, such as NoSugarForKids. They work on data you already have.