Integrations
Most log records never need a language model's opinion, so Jev Logs scores each one first: how diagnostically useful it is, how urgent, and whether deeper analysis is warranted. Only confident, low-value records skip the expensive branch, and the archive always gets everything.
Screenshot unavailable. Open the experiment ↗
On sanitized HDFS and BGL benchmarks it caught 99.3% and 100% of labeled anomalies, but read the filtering numbers too: under 1% of records were held back in either dataset, so the sample shows the safety, not the savings. It ships as an npm package wrapping your existing OpenTelemetry exporter, keeps uncertain and failed records eligible, and can run annotation-only.
Gather examples of routine system notices alongside real error messages from your app. Identify everyday messages that never need costly review, such as routine health checks. Your developer can set up the tool to evaluate incoming records. It then forwards only high-value or uncertain notices to an advanced analysis model.
Every log record still flows directly into your regular permanent archive. Automated scoring can make mistakes. Because of this, severe errors and service hiccups always stay marked for deeper inspection instead of being skipped.