Virlo uses Jev to filter content research candidates
Virlo's research agent uses Jev to judge whether each of 12.8M short videos is actually on-niche, at roughly 7k tokens and 0.03 cents per 25 videos, passing high-confidence items and escalating shaky ones to a larger model or a human.
Jev is the first AI model that actually fits content research... numerous individuals are sharing poor-quality demos of it; thus, here is the marketing use case that truly resonates. Jev does not engage in writing; instead, it makes decisions You present it with a question and options, and it provides a selection, a score, or a yes/no likelihood, accompanied by a confidence metric that's literally what content research is: thousands of tiny calls a human makes while doomscrolling here's how Virlo wired it in: - a research agent pulls videos from 12.8M+ TikToks, Reels and Shorts in your niche - Jev checks every video first: is it actually on-niche, or just hashtag-stuffing - judged on caption + hashtags + transcript, not tags alone - 25 videos = ~7k input tokens, around 0.03 cents at Jev's pricing - only what passes gets tagged by hook, format and angle - the top outlier becomes a beat-by-beat script the rule: high confidence -> act, shaky -> escalate to a bigger model or a human you don't need a reasoning model to check if a video is on-topic... that's hiring a lawyer to sort your mail
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