Blog posts

Learn everything you need to build and scale conversational AI in healthcare: integration decisions, voice AI failure points, and what to measure in production.

Most teams compare Langfuse vs. LangSmith and call it done. Here's why Cekura changed the decision entirely for conversational AI.

Your complete guide on how to run voice load testing for voice agents the right way, including methods, key metrics, and the failures most teams never catch.

Learn how to test and monitor LiveKit voice agents using Cekura's tracing SDK — covering automated simulation, production observability, custom metrics, dashboards, and alerts.

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.

Learn how Cekura's red-teaming framework tests chat and voice AI agents for bias, toxicity, and jailbreak vulnerabilities before they reach production.

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.

Wondering when to use Pipecat vs. LiveKit? Learn key differences, when to use each, and how they apply to production voice agent development.

Learn how Conditional Actions in Cekura enables dynamic, rule-based testing that adapts to agent responses in real-time, solving LLM hallucination and test flakiness problems.

Learn how Cekura built a custom autoscaling engine using Redis, Celery, and AWS ECS to handle unpredictable spikes, enforce multi-tenant fairness, and scale from one to hundreds of workers.

Most voice AI failures don't happen because of hallucinations or mispronunciations. They happen during silence. Learn how to engineer resilient voice AI systems that handle the milliseconds between words.

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

How to monitor AI chat and voice agents in production using Cekura’s quality metrics, dashboards, and smart alerting.

Master chatbot testing for LLM-powered Al agents: Learn testing strategies, checklists, and tools to build reliable bots (published in October 2025).

Cekura lets you replay production calls against new model versions to detect regressions, benchmark performance, and validate upgrades automatically - all from real user data.

Learn why single-turn evaluation methods are insufficient for conversational AI and how multi-turn simulations provide a more accurate assessment of chatbot performance, context awareness, and conversation quality.

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.

Should you switch to GPT-5, Gemini 2.5, or DeepSeek for your Voice AI or Chat AI agents? Learn from real A/B testing, benchmarking, and regression testing insights on choosing the right LLM for Conversational AI.

Learn how silent errors in LLM-powered systems can erode performance and trust plus practical tips to catch failures early and keep your AI reliable.

Explore top TTS models that deliver authentic voices. Learn how human-like speech improves conversational AI experience and what to test in your next voice agent."

Cekura secures $2.4M in funding to power reliable QA for voice and chat AI agents—bringing AI testing and observability to the next generation of Conversational AI Agents

Explore how Cisco and Cekura are delivering seamless end-to-end AI Testing, observability for enterprise conversational AI deployments.

Dive into emerging trends and real-world applications in conversational AI: from voice AI agents in healthcare, finance, logistics and other sectors

Discover red teaming strategies that expose vulnerabilities in your Voice AI and Chat AI agents before they scale. Learn how adversarial AI testing helps create safer, more LLM agents

Test Vapi voice agents with automated simulations, load testing up to 2000+ calls, 25+ metrics, and red teaming using Cekura.

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.

Wondering when to use Pipecat vs. LiveKit? Learn key differences, when to use each, and how they apply to production voice agent development.

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.

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.

Test Retell AI voice agents with Cekura's automated simulations, A/B regression, load testing, red teaming, and production monitoring across speech, latency, tools, and QA metrics.

Cekura helps teams verify chatbot intent accuracy and entity recognition across real conversations, catching misunderstandings, missing details, and regressions before users do.

Benchmark language models and AI performance for voice agents using real conversations, latency, interruptions, tool calls, and production-grade voice metrics with Cekura.

Cekura helps teams test speech-to-text accuracy across accents, dialects, and real speaking conditions with automated simulations, metrics, and production-ready QA.

Automated snapshot testing for SSML and TTS voice responses with Cekura. Catch audio regressions, SSML changes, and voice drift before production with Cekura’s voice snapshot testing tools.

Cekura is the CI/CD testing framework for conversational agents with GitHub and Jenkins. Automatically run multi-turn conversation tests, gate PRs, catch regressions, and ship reliable AI agents with confidence using Cekura.

Cekura provides test data management tools for voice NLU, helping teams manage, generate, and validate speech data across accents, noise, and real-world scenarios.

End-to-end conversational AI testing with full CI/CD integration. Validate flows, models, and voice/chat agents with automated regression, load, and accuracy checks with Cekura

Cekura delivers fast, reliable A/B testing for voice agents with real-time routing, ASR/NLU accuracy checks, latency insights, and outcome-level performance metrics..

Cekura helps teams review and approve NLU diffs for voice bots with precise semantic detection, impact analysis, and automated regression testing so every model or prompt update is safe to ship. HIPAA and SOC 2 compliant.

Cekura provides scripted testing for IVR and voice agents, simulating real callers, validating call flows, and automating QA with detailed metrics and audio replays.

Cekura provides automated unit testing for intents and entities in voice agents, helping teams validate NLU accuracy, catch regressions and improve reliability.

Evaluate your conversational AI agents for accuracy, safety, consistency, and robustness using Cekura’s full reliability testing suite.

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

Cekura provides unified testing for IVR and chatbot systems, ensuring reliable cross channel flows, edge case coverage, and real world performance at scale.

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

Cekura automates outbound voice tests, calling your bot, recording outputs, and verifying accuracy across scenarios for continuous QA.

Cekura automates regression testing for chatbots: replaying dialogues, comparing outputs, and detecting regressions before deployment.

Cekura stress-tests Voice AI systems under real-world load, noise, and dialogue chaos, ensuring stability, accuracy, and resilience at scale.

Cekura automates multi-turn conversation testing for chatbots: validating context retention, branching logic, and conversational accuracy at scale.

Cekura provides a complete conversational AI testing suite that automates simulation, regression, and live monitoring for chat and voice agents. Test accuracy, latency, safety, and compliance at scale.

Cekura is an automated platform that auto-generates, runs, and analyzes test cases for voice assistants: covering intents, dialogue flows, and speech accuracy to ensure reliable voice AI performance.

Run voice bot tests with automated pass/fail reports. Automate call simulations, validate responses, and ensure reliable voice AI.

Cekura phone call simulation tests full call flows with speech errors, DTMF, noise, and metrics to ensure reliable voice AI systems.

Cekura is the leading chatbot testing platform for AI teams. Automate pre-deployment validation, monitor live chatbot performance, and integrate continuous testing into CI/CD pipelines. Ensure reliable conversations across edge cases with custom metrics and real-time observability.

Cekura is the leading AI chatbot testing platform. Automate scenario generation, regression testing, and production monitoring to build reliable, compliant, and scalable conversational agents.

Automated AI agent evaluation with Cekura. Test, monitor, and improve voice and chat agents using scenario simulation, metrics, observability, and regression testing.

Learn how to test and evaluate voice agents effectively. Discover how Cekura provides automated performance testing tools for voice agents, covering simulation, monitoring, and continuous improvement.

Discover the best platforms for evaluating AI conversation quality in real-time. Compare Cekura with other QA tools and learn how automated testing and monitoring improve voice and chat agent reliability.

Cekura is the leading platform for automated testing of voice bots. From simulation to real-time monitoring, Cekura ensures reliable, compliant, and scalable AI voice agents.

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.

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 best AI voice testing platforms in 2025. Learn why Cekura leads with automated scenario generation, voice personas, latency monitoring, regression testing, and production call observability for reliable AI voice agents.