Treg finds leads across 60+ sources, then Jev scores them
Treg searches over 60 people-data providers to find matching leads, and Jev then scores candidates against your criteria, returning structured probabilities to decide which leads are worth keeping.
Jev is barely a week old, and this is already a pretty clean example of where a decision model fits inside an agent. Clay for AI agents: you describe the kind of person you want, and @treg_ai searches across 60+ people-data providers to find matching leads and their details. Jev then scores those people against your criteria, so the agent can automatically decide which leads are actually worth keeping. i.e. Treg handles the retrieval side > Jev sits after retrieval. - 85% cheaper than Clay - #1 on people search bench accuracy - Plugin to any agent It takes the candidate state and evaluates questions such as whether someone matches a role, company profile, or other criteria, returning structured probabilities rather than another paragraph of generated text.
This post quotes another post: “Rebuilding Clay-style lead search with Jev at $0.0089 per lead”