Posts tagged with "Evaluation"

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.

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.

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.

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 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.

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.

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.

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.

Test prompts show whether an LLM prompt still works after each change. Learn the methods, copy the examples, and compare tools that catch regressions.

PromptLayer versions, tests, and logs LLM prompts. See its features, plan costs, and G2 pros and cons, then follow our 5-step guide to run your first eval.

Voice design creates a new AI voice from a text description. Learn how to write the prompt, pick between candidates fairly, and test the winner on real calls.

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.

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.

Chatbot automation testing tools decide which failures reach your users. See which caught the planted regression and red-team attacks, and which didn't.

Voice AI sentiment analysis for upsell offers fails on timing. Compare four trigger designs, real accuracy ceilings, and the tests that catch a bad offer.

Chatbot evaluation goes beyond pass/fail. Learn the 3 methods and 8 metrics engineering teams use to catch failures before production.

Custom KPIs for voice agent monitoring are team-defined metrics that score live calls against your own business rules. How to build and track them in Cekura.

Hallucination detection for voice AI measures whether a voice agent's spoken answers stay grounded in its knowledge base instead of inventing facts. Here is how RAG grounding checks, factuality evals, and Cekura catch hallucinations before production.

Instruction following evaluation tests whether a voice bot obeys its system prompt across a full call. How Cekura scores instruction adherence at scale.

Persona-based voice AI QA tests whether a voice agent stays accurate, consistent, and on-brand across many simulated caller personas. Here is how it works, why it matters in 2026, and how Cekura runs it at scale.

LLM as a judge uses a large language model to score AI outputs at scale. Learn how it works, its pros, cons, and best practices for voice and chat agents.

Braintrust pricing looks simple until overage costs kick in. I broke down every plan, real monthly costs, and where the free tier stops being enough in 2026.

Galileo AI pricing looks simple until you hit production, then issues arise. Here's what the plans actually cost you at real trace volumes in 2026.

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

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.

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

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

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.