Blog posts

Voice agents infer turn-end from VAD and streaming STT; neither is sufficient. How Cekura's voice agent checks each close against the call's own audio.

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

Every Coval alternative compared on published pricing, plan limits, and telephony coverage. Get the cost at your volume and book a free simulation sweep.

First call resolution measures issues closed without a callback. Get the FCR formula, 2026 benchmarks by industry, and 7 ways to raise it on AI calls.

See 7 voicebot in banking use cases, from card locks to fraud alerts, plus the account-number accuracy bar and the Reg E and TCPA duties your tests must cover.

Voicebot vs chatbot compared on response time, speech recognition error, cost, and TCPA exposure. Learn the key differences and when to use each in 2026.

Word error rate counts the words your voice agent mishears. Get the WER formula, a worked calculation, current 2026 benchmarks, and 7 fixes for your own calls.

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.

Dialogflow CX pricing for 2026: Flows and Playbooks rates, trial credits, voice billing rules, hidden costs, and a 20,000-call monthly cost model to copy.

Genesys Cloud CX pricing across all four tiers, plus AI tokens, telecom, and add-ons, verified against genesys.com on September 25, 2026.

NICE CXone alternatives compared for 2026 on cost, contract terms, and workforce tools, plus what to test before you switch platforms.

PromptLayer pricing runs from free to $49 Pro and $500 Team a month. See every plan cap, how overage bills, and worked costs for 5- and 20-person setups.

What is NiCE CXone? See how the cloud contact center platform routes calls, runs AI agents, and bundles WFM and analytics, plus pricing, pros, and cons.

Amazon Lex pricing guide for 2026. Compare text, speech, and streaming rates, free tier rules, and what a real voice or chat agent costs you each month.

AssemblyAI pricing by product, from $0.15/hr transcription to the $4.50/hr Voice Agent API, with every add-on rate and worked cost examples for real workloads.

Average handle time (AHT) explained: the formula, where the 6-minute benchmark comes from, and how to reduce AHT on human and AI calls without hurting FCR.

Not all voice AI APIs handle real-time audio equally. Testing the best voice AI API for real-time audio processing revealed bigger gaps than you'd think.

Get a Deepgram API key in 6 console steps, pick the right role, test it with cURL, Python, or the CLI, then rotate or revoke it before a leak costs you.

Call deflection resolves customer requests without a live agent. Learn about the call deflection rate formula, honest benchmarks, and 7 ways to raise it.

Conversational AI in banking, covering six servicing use cases, the benefits with real numbers, the rules that apply in 2026, and how to verify any of it.