Using Jev for GTM signal filtering: separating signal from noise in under 500ms
The author built two versions of a Jev workflow that classifies alerts full of same-name company noise into real signals, with the automated version pushing only useful mentions to Slack.
Two tools. Both named Clay. Both funded startups. Both showing up in your alerts. Only one of them matters to you. In GTM, signals are everything. A competitor mentioned in a community thread. A prospect asking the right question. A brand mention that could turn into a conversation. Miss the signal and you miss the moment. The window closes. Someone else gets there first. The problem: your alerts are full of noise. Few more examples: • Apollo - the outbound sales platform or the GraphQL framework. • Rippling - the HR tool or "rippling effects" in every economics article. •Pitch - the presentation tool, a sales pitch, or a football pitch. See..No easy tell. Just alerts that look real until you actually read them. And while you're skimming noise, the real signal is sitting unread. So I built a simple workflow using Jev to fix this for you. Two versions. Version 1 - Signal Sifter: paste your alert snippets, describe what you're actually tracking, and Jev classifies each one in under 500ms. Real signal or noise. Clean output. No setup. Version 2 - the automated one: connect your alerts feed, and only the mentions that actually matter land in your Slack DMs. Everything else filtered before it reaches you. Felt almost too simple when I first thought of it. Then I remembered I actually do this manually every week. That's usually the sign it's worth building. Links below for both the versions below!