Blog posts

Closing the eval loop was the algorithm. Here's the product and the POC playbook that made customers willing to run it on their own production voice agents.

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.

Voice AI simulation is what makes agent testing reliable. See how Cekura builds realistic testing agents, and the accuracy and latency tradeoffs that matter.

How Cekura protects conversational AI data end-to-end: encryption, tenant isolation, PHI redaction, audit logs, SOC 2 / HIPAA / GDPR compliance, multi-region residency, and BYOC deployment for enterprises.

Learn the mechanics of endpointing and turn detection in voice AI, the three signals modern agents use, and how to measure and test conversational timing at scale.

Cekura has an MCP server. Coding agents (Claude Code, OpenAI Codex, Cursor, Windsurf) can trigger voice agent test runs, schedule recurring evals, and review pass/fail results without leaving their editor.

Learn how to build a self-improving voice agent loop that automatically diagnoses failing evals, applies prompt fixes, catches regressions, and iterates to 100% pass rate.

Developer's guide to voice AI evaluation in 2026. Metrics, scenario testing, hallucination detection, persona QA, and per-stack testing for major voice stacks.

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.

Learn how to build production-grade knowledge base connectors and implement RAG-based agentic retrieval for voice AI agents — with async syncing, SSRF protection, and observability.

Your customers don't all sound the same. Your testing shouldn't either. Discover how Cekura tests voice AI agents across 30+ languages, regional accents, and culturally authentic personalities.

In Voice AI, small changes are dangerous. Learn how to build a production-grade CI/CD pipeline with unit tests, E2E infrastructure testing, and a production feedback loop that catches failures before they reach users.

Single-turn red teaming has a 19.5% success rate. Multi-turn attacks hit 92.7%. Here's the data behind why multi-turn red teaming works and how we automated it for voice AI.

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.

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.

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.

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

AI voice technology is the pipeline behind spoken AI agents. This guide covers how it works, where each layer fails, and what that costs in production.

Compare the best Vapi alternatives for voice AI APIs in 2026 on pricing, latency, compliance, and telephony. See which one fits best before you switch.

Conversational AI voice bots skip the menu and carry the full conversation. This covers the architecture, where they break, and which platforms hold up.

Cyara pricing is tailored to each product. This breakdown covers what each one does, who it makes sense for, and five alternatives with published pricing.

How to test voice AI agents using 7 methods, from scripted calls to red teaming, plus what each catches, what it misses, and the right order to run them.

LiveKit Agents is an open-source framework for realtime voice AI. Here is how the SDK works, how to build an agent, and what to test before launch.

Speaker diarization tells you who spoke when. Learn how it works, why DER benchmarks mislead, where it fails on agent calls, and how to test in production.

Compare the 7 top conversational AI platforms with voice CRM in 2026, ranked by how deeply each one reads and writes your records live during the call.

Voice activity detection decides when your agent hears speech. A developer's guide to VAD models, threshold tuning, the four failure modes, and testing.

Most teams answer 'what is an AI voice agent?' too late. This guide covers how the pipeline works and where deployments break in production.

Cyara omnichannel CX testing gets high marks from enterprise QA teams. The licensing structure and agentic limitations tell a more complicated story.

How to test AI chat workflows before launching? Learn the five methods that surface failures functional testing misses, from sycophancy to load collapse.

Retell AI voice automation features in 2026, broken down across building, voice, telephony, monitoring, and pricing, and the limits that shape deployments.

Telephony testing checks if your voice agent holds up on a real phone call. See the methods, tools, and metrics that catch failures before customers do.

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

A clear look at Retell AI voice automation features across build, deploy, and monitor, and where each one needs testing before it reaches real callers.

I tested Twilio vs Vapi across deployment, billing, and production QA — one comparison turned into three tools solving three different problems.

Vapi vs ElevenLabs solve different jobs, and neither tests itself. Here are the key differences in 2026, plus where a QA layer fits in your voice stack.

AI voice agent services for businesses come in many forms. I tested 7 platforms for 2026 on latency, CRM depth, and what breaks under live call volume.

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

Conversational voice AI lets software hold a real-time spoken conversation. Here is how the conversation loop works and the key platforms to know in 2026.

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.

Voice AI workflow automation chains triggers, branching logic, and actions into one call. See how it works, where it breaks, and the top tools to build it.

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

These are the best voice AI CRM integration solutions tested against multiple areas and real sales stacks in 2026 to help you make an informed decision.