Posts tagged with "Qa"

Learn how to build voice evals that automatically improve from human feedback using Meta-Harness, reaching 95-100% human agreement in 4 to 6 iterations.

Pipecat testing with Cekura: run voice agent simulations, add session tracing, and monitor production performance. Catch latency and interruption issues before they reach users.

Learn how to build a complete scenario test suite for your voice AI agent — covering workflow tests, red teaming, knowledge base scenarios, conditional actions, and how many scenarios you actually need.

Cekura's founding Forward Development Engineer shares hard-won lessons on building reliable voice AI evaluation metrics — from avoiding cross-pollination to dynamic variable-driven testing patterns.

Cut through the noise in the Voice AI testing space. Learn the 4 levers — Feature, Integration, AI, and Infrastructure — that separate real platforms from wrappers, and how to evaluate vendors before you commit.

Explore 12 key metrics—like interruptions, WPM, sentiment, and talk ratio—to enhance your AI conversation monitoring and insights.

Discover the 6 most important metrics for monitoring AI conversations—Instruction Following, Latency, Hallucination Rate, CSAT, Interruption Handling, and Voice Clarity—to ensure reliable, high-performing voice and chat agents.

Call transcript QA for a voice bot scores every call's transcript against your rules. How to review voice agent transcripts at scale with Cekura.

We tested 7 AI voice agent testing tools across CI/CD regression, production monitoring, and telephony-layer failures. See which ones actually passed.

Best practices for AI voice agent testing, with honest tradeoffs: 8 rules that catch the failures real callers trigger before they reach production.

Learn how to test your voice agent after building it, across six stages from component checks to production monitoring, with a clear pass bar for each.

Best AI voice testing platforms tested across staging agents, noisy audio, and prompt regressions. Each one fits a different stage of your voice agent.

Most voice AI evals just score logs and miss the real failures. Here are the methods, platforms, and best practices that catch them before customers do.

Chatbot evaluation goes beyond pass/fail. Learn the 3 methods and 8 metrics engineering teams use to catch failures before production.

Persona-based voice AI QA tests whether a voice agent stays accurate, consistent, and on-brand across many simulated caller personas. Here is how it works, why it matters in 2026, and how Cekura runs it at scale.

I reviewed conversational AI platforms for voice automation and production reliability. These 8 handle testing, monitoring, and deployment best in 2026.

I tested the top conversational AI testing tools and documented what works. Best practices, honest reviews, and updated pricing, all in one place for 2026.

Learn automated QA best practices for voice agent testing with realistic audio, multi-turn flows, release gates, production QA, and regression checks.

Conversational analytics turns every voice call into structured data. Learn how it works, what metrics matter, and how voice AI teams use it in production.

LLM monitoring helps teams track quality, latency, cost, safety, and regressions in production. Here are the tools, metrics, and best practices that matter.

LambdaTest works until your product actually scales. I tested the 7 best LambdaTest alternatives and competitors in 2026, so your team doesn't have to.

Most agent failures happen after launch. Here's how to build AI agent evals that catch broken tool calls, drift, and regressions before your users do.

Looking for a Langfuse alternative? I tested 10 LLM observability tools for tracing, evals, and production monitoring. Here's what each one actually does well.

Most AI appointment booking tools sound great on a demo. I tested dozens and selected the 5 that hold up in real booking scenarios and in production.

Learn how to test Pipecat voice agents using conversation simulation, STT-LLM-TTS pipeline benchmarking, regression testing, load testing, and monitoring.

I compared conversational analytics software platforms and found 9 worth using in 2026. Compare features, pricing, and use cases for sales, support, and AI agents.

Explore the 8 best Voice QA platforms in 2026, including Cekura, Braintrust, Roark, Sipfront, Bluejay, Vapi, Relyable and Leaping AI. Compare simulation, monitoring, load testing, and compliance capabilities for production AI voice agents.

Compare the best chatbot testing platforms of 2026, including Cekura, Botium, TestMyBot, Botium Box, and Applause. Explore automation, NLU validation, regression testing, load simulation, and human QA to find the right solution for your chatbot.

Cekura helps teams improve chat agent quality with automated testing, scoring, monitoring, and integrations for fast, reliable conversational performance.

Cekura’s dashboards centralize chat and voice agent test results with real-time metrics, alerts, and regression tracking for better QA insight.

Explore the best chat testing platforms for reliable AI agents. Cekura offers automated QA, regression testing, and real-time monitoring to ensure seamless, scalable, and high-quality chat interactions.

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.