Blog posts

NVIDIA Nemotron 3.5 Transcribe has the lowest overall word error rate on Cekura Bench: 2.28% across public clips and private phone calls, first of 21 speech-to-text models.

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.

Voice AI use cases by call type, from scheduling and intake to banking, insurance, collections and dispatch, with what each agent does and what breaks.

The best TTS voices for AI agents in 2026, compared on naturalness and latency. How human-like speech improves conversational AI and what to test in your next voice agent.

τ-bench tests whether a model can do the task. Cekura Bench tests your deployed voice agent on real audio calls, three runs each. See how the two compare.

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 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.

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.

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.

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

Sierra AI alternatives compared for 2026 on voice maturity, deployment model, and real pricing, plus the pre-launch testing each one still needs.

Nemotron 3.5 Transcribe is NVIDIA's open-weight speech-to-text model for recordings. Specs, accuracy, 40 languages, license, and how to run it.

The best-rated AI virtual receptionist voice tools all promise 24/7 coverage. I tested ten options for 2026 to find out which ones actually deliver it.

AI voice agents for business, tested: 7 platforms compared on latency, CRM depth, compliance, and current pricing, plus where each one breaks under live calls.

How voice agent platforms and speech-to-speech models performed across 5,000+ recorded test calls, plus STT and TTS accuracy and speed.

Picking the wrong AI voice agent platform can cost you weeks. I tested each one on calls across outbound, support, and enterprise. Here's what survived.

Tested across STT, TTS, and full-stack orchestration in production. So, what is the best AI voice API for developers? It depends on your stack layer.

We ran 10 TTS models through real voice-agent calls and ranked them on latency, naturalness, and price. The best TTS in 2026, with the numbers to prove it.

Compare the 11 best conversational AI platforms for enterprise customer service, from Kore.ai to Google and NiCE Cognigy, on channels, governance, and cost.

Conversational voice AI holds real-time spoken conversations. Learn how it works, where businesses use it, how it differs from IVR, and the 2026 platforms.

The 9 best Langfuse alternatives in 2026, compared on tracing, evals, agent simulation, self-hosting, and pricing, plus what changed at Langfuse this year.

Langfuse vs LangSmith in 2026: how they compare on open source, self-hosting, LangChain fit, evals, alerts, and pricing at real volume, plus which to pick.

Amazon Connect pricing looks simple until telephony and add-on charges land on top of the channel rate. See what each plan includes and what it adds up to.

See how each Deepgram API product works, which protocol it runs on, and the use cases worth starting with, with Python SDK examples.

OpenLLMetry turns each LLM call into an OpenTelemetry span. See what a traced call records and which questions it leaves for evaluation to answer.

Silero VAD is a free, MIT-licensed voice activity detector. See how it works, what changed in v5 and v6, and how it compares to WebRTC VAD and TEN VAD.

Call center quality monitoring software decides which calls get checked, and manual sampling leaves the rest unseen. Compare how each scores.

LLMStudio usually means LM Studio, a desktop app that runs open models offline. Learn how it works, what it costs, and which other tools share the name.