Vocera
Developer Tools Unknown

Vocera

Web Application 4.3/5 web

What is Vocera?

End-to-end testing and observability for Voice AI and Chat AI agents. Simulate scenarios, monitor production, and catch issues before they go live.

Cekura is an end-to-end testing and observability platform for conversational AI agents, including voice and chat. It enables teams to run pre-production simulations across diverse personas, monitor production conversations in real time, and evaluate key quality metrics like empathy, responsiveness, and hallucination. The platform integrates with popular voice AI frameworks such as Vapi, Retell, and ElevenLabs, and supports custom scenario creation, parallel testing, and alerting. Cekura helps ensure reliable, high-quality conversational experiences before deployment.

Key Features

pre-production simulation
diverse persona testing
real-time production monitoring
voice quality signals (gibberish, interruption, latency, sentiment)
LLM judge tuning
custom evaluation prompts
replay real conversations
parallel calling
alerting (Slack, email, webhooks)
conversation analytics
flow analysis
user insights
integration with Vapi, Retell, ElevenLabs, etc.
custom scenario creation
thousands of pre-built scenarios

Use Cases

QA engineers simulate thousands of customer calls with diverse personas (accents, emotions) to catch agent failures before deployment, reducing production incidents by 80%.
Product teams test how prompt changes affect core flows like appointment cancellations by running parallel simulations, ensuring no regressions in user experience.
Voice AI developers monitor production calls in real time for gibberish detection and interruption tracking, enabling rapid fixes to maintain high-quality interactions.
Compliance officers automatically verify that agents include required disclaimers in every call by running scenario-based tests, avoiding regulatory fines.
Customer success teams replay problematic conversations to debug recurring issues, improving agent accuracy and customer satisfaction scores.
ML engineers tune LLM judges by editing and scoring evaluation prompts against real recordings, aligning automated quality scores with human judgment.
Operations managers set up custom dashboards to track duration trends, sentiment, and drop-off rates across agents, optimizing performance and resource allocation.
voice AI testingchat AI testingconversational AIQA automationobservabilitysimulationpersona testingLLM evaluationmonitoringalerting
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Frequently Asked Questions

What does Vocera do?

End-to-end testing and observability for Voice AI and Chat AI agents. Simulate scenarios, monitor production, and catch issues before they go live.

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