Jev read thousands of outreach messages and found the signals that booked demos.
Pierre-Eliott Lallemant gave Jev a dataset built from thousands of outreach messages and asked which intent signals led to the most booked demos. He had the answer in 40 seconds, for less than $0.20. It is coming to Gojiberry AI and its MCP.
A worked guide to Jev's Score primitive: writing a request, defining rubric levels, reading recorded probabilities and the weighted-score math.
Jev Trader · Research & data
Jev Trader
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Research & data
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Sends a detected bot's last 48 request paths to Jev, which picks what it's after (prices, articles…) and how it crawls (pagination, IDs…), or unknown.
WebDecoy · Research & data
WebDecoy
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Paste an article or post link. Fake / Real extracts its claims, finds outside evidence, and has Jev judge whether the evidence supports or contradicts each one.
@DansiDanutz · Research & data
@DansiDanutz
Site
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Combines rubric science and LLM-as-a-judge research to grade outputs with AI judges: LLMs, decision models like Jev, or both.
@deliprao · Research & data
@deliprao
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Research & data
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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 Show more
