Kubit joins LLM traces with clickstream activity and business outcomes to explain why users re-prompt, leave, or convert. It accepts data through OpenTelemetry, CDPs, or an existing warehouse and can feed analytical context to coding agents for debugging and verification.
Features
- Agent trace and user behavior correlation
- Intent and sentiment analysis
- Outcome and funnel analysis
- OpenTelemetry, CDP, and warehouse integrations
Use Cases
- Diagnose agent failures
- Analyze re-prompts and drop-offs
- Verify product changes
- Measure AI feature outcomes
FAQ
Kubit joins LLM traces with clickstream activity and business outcomes to explain why users re-prompt, leave, or convert. It accepts data through OpenTelemetry, CDPs, or an existing warehouse and can feed analytical context to coding agents for debugging and verification. Core capabilities include: Agent trace and user behavior correlation, Intent and sentiment analysis, Outcome and funnel analysis.
Common scenarios include: Diagnose agent failures, Analyze re-prompts and drop-offs, Verify product changes.
