A GTM engineer shares a practical tactic: ask Claude to identify key sources, use Jev to score each source's quality at a very low cost, and then build an artifact for review. Ideal for large TAM lists.
Underrated GTME tactic when working with agents to map out large TAM lists: Ask Claude to find the key sources where it'll extract the data from. Have JEV score the quality of each at 0.0001 usd. Then have it build you an artifact to review the sources. Specially useful in
The author tested Jev on a benchmark of 1,565 German and English business emails from industrial suppliers across 10 categories, finding it less accurate than Gemini, but focused on where the errors occurred and still plans to put it into production.
Aaron Levie says Jev helps agents make split-second decisions in workflows, data classification, and judgment calls, with a Box and Jev demo pulling an incident report from Box.
Fatih Yildiz says TypeSafe AI's Jev looks promising for observability decision model use cases, noting latency adds up when using frontier models at edge_delta for operational priority scoring and event routing, and is testing Jev.
