Blog posts

Voicemail detection testing checks whether your outbound agent classifies what picked up, then takes the right branch. The six scenarios that catch real defects, and how to run them on every release.

Measure voice AI latency at every layer: endpointing, ASR, LLM TTFT, TTS, and transport. Monitor p50, p95, and p99, and diagnose spikes with Cekura.

AI agent optimization for agents hosted on Vapi, Retell or ElevenLabs: how Cekura clones it, measures each edit, and promotes only the winner. See the loop.

Cekura handles LLM stalls in voice agents by timing the first token, not the whole reply, then racing a proven backup instead of retrying the same model.

Run AI agent testing inside CI/CD: version test suites as code, validate free with dry runs, and gate every merge with GitHub Actions. See how Cekura does it.

Cekura's Metric Optimizer now asks for human judgment on unclear rules, verifies 100% scores against evaluator noise, and shows every unresolved case.

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

Best AI voice testing platforms tested across staging agents, noisy audio, accents, and prompt regressions. Plus how to test across languages and on a nightly schedule.

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.

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

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

Compare Retell vs. Vapi on pricing, features, latency, and compliance. I tested both platforms to help you pick the right voice AI for your team in 2026.

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.

ElevenLabs pricing in 2026, verified: every plan from Free to Business, how credits and rollover work, overage costs, and which tier fits voice AI teams.

AI red teaming in 2026: seven attack methods, the tools that run them, the OWASP and EU AI Act duties behind them, and a 1-to-5 scoring rubric to copy.

I tested the best Cyara IVR alternative options for 2026. Compare six tools on setup speed, DTMF coverage, published pricing, and AI agent testing.

Hamming publishes no public pricing. Compare 7 Hamming alternative platforms for voice agent testing in 2026 on price, compliance, and CI/CD depth.

AI IVR testing software compared across 5 tools, from DTMF menu checks to LLM agent scoring. See which caught the planted misroute and which missed it.

Containment rate measures the sessions your AI agent closes without a human. Get the formula, 2026 benchmarks, measurement traps, & 7 ways to raise it.

Decagon AI competitors compared for 2026 on billing shape, voice maturity, and verified pricing, plus why published resolution rates don't compare.

Mean Opinion Score (MOS) rates speech quality from 1 to 5. See the nine ITU MOS types, how each gets measured, and what MOS misses on AI voice agent calls.

PolyAI pricing has no public rate card in 2026. See what the pricing page confirms, what sits outside the per-minute rate, and which buying path fits you.

What an AI voice agent agency is, the three business models, what the platforms cost, and the QA layer that keeps client agents from churning callers.

Cyara telecom CX testing reviews from G2, Capterra, and AWS agree on strong global carrier coverage and a steep cost. Here's who the enterprise price fits.

Learn how to make an AI voice assistant in Python with runnable code for voice activity detection, transcription, and streaming replies. Build it in 8 steps.