Posts tagged with "Observability"

Call analytics for voice agents should tell you why calls fail, not just how many. See how Cekura Insights clusters failing calls into a few root-cause fixes.

Discover why Cekura provides superior monitoring capabilities compared to traditional tracing platforms for conversational AI agents.

Custom KPIs for voice agent monitoring are team-defined metrics that score live calls against your own business rules. How to build and track them in Cekura.

Helicone vs Langfuse vs Cekura aren't competing for the same users. Here are the main differences, and what's best for your voice or chat AI stack in 2026.

Voice agent performance testing goes beyond transcripts. This guide covers five methods that catch what manual reviews miss, with examples from real teams.

Learn exactly what voice observability is, why it matters for production voice AI agents, and how to trace failures across infrastructure, execution, and UX.

LLM observability is the difference between knowing your AI works and assuming it does. Here's everything you need to know to set it up right.

Monitor Retell AI voice agents in production with latency tracking, interruption detection, tool-call monitoring, and conversational quality analysis.

Explore top voice agent monitoring platforms for tracking latency, interruptions, tool calls, and instruction adherence in production.

Discover the 10 best chatbot testing platforms in 2025. Compare tools like Cekura, Botium, Applause, QBox, and more to test, validate, and monitor AI chatbots for reliability, NLU accuracy, and performance.