Rolewise uses Jev to make resume-to-job fit decisions
The author tested Jev on matching resumes to job descriptions with a single suitable/not-suitable decision, evaluating 838 jobs in 61 seconds at 20 concurrent requests, noting this is a throughput test rather than an accuracy benchmark.
Spent some time testing #Jev from @typesafeai on a use case I care about: matching a resume against real job descriptions. I built a small demo called Rolewise. @metix_ai handles job retrieval; Jev handles the fit decision. The setup: - Parse the resume and generate editable search filters. - Retrieve jobs and full descriptions through the Metix AI API. - Send each resume–JD pair to Jev, with 20 requests in flight. - Ask for one decision: suitable or not suitable. One run: 838 jobs evaluated in 61.3 seconds. That excludes parsing and retrieval. I started with multiple scoring dimensions, then simplified the task to a single fit decision. For this demo, I wanted to see whether Jev could help someone narrow a list of opportunities without adding another scoring system to interpret. The recording shows actual requests completing—no simulated progress. Retrieval and evaluation are separate steps, so you can inspect the jobs before running Jev. This is a throughput result, not an accuracy benchmark. I haven’t established how reliably those decisions agree with a human reviewer yet. That’s the next thing I’d test, especially on borderline cases. Sharing this as a concrete Jev example. If you’re building something similar, I’d be curious how you’re defining “fit” and checking the results. Job data/API: https://platform.metix.ai