
Overviews
How it works?
Monitor LLM response quality
Track the quality and accuracy of large language model outputs in production, identifying performance degradation or unexpected behaviors that require model adjustment or retraining.
Run automated evaluation tests
Execute systematic tests against your LLM applications using predefined test cases, ensuring consistent performance and catching regressions before they impact end users.
Alert on performance anomalies
Receive notifications when model performance metrics fall outside acceptable ranges, enabling rapid response to quality issues in AI-powered applications.
Collect user feedback data
Aggregate user ratings and feedback on LLM responses, creating datasets that inform model improvements and identify common failure patterns requiring attention.
Compare model versions
Analyze performance differences between model versions or prompt variations, making data-driven decisions about which configurations deliver superior results for your use case.
Track cost and usage metrics
Monitor API usage, token consumption, and associated costs across LLM applications, helping optimize spending and identify opportunities for efficiency improvements.
Generate performance reports
Create scheduled reports summarizing LLM application performance, quality metrics, and usage trends for stakeholders and technical teams to review regularly.
Trigger retraining workflows
Initiate model improvement processes when quality metrics indicate the need for prompt refinement, fine-tuning, or other optimization activities.

Configure
Build
Continuous LLM improvement system
Build an automated platform that continuously evaluates LLM performance against quality benchmarks, collects user feedback, analyzes failure patterns, and triggers improvement workflows including prompt optimization and model retraining when performance thresholds are not met.
“You can’t do this anywhere else.”



















































Your stack,
connected.

