Cekura

Automated QA and observability platform for testing and monitoring voice and chat AI agents in pre-production and production.

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About Cekura

Cekura is a web-based automated QA and observability platform designed for enterprises building and deploying voice AI and chat AI agents. It enables teams to validate conversational AI systems before deployment through pre-production simulations that test end-to-end voice pipelines, including speech input, instruction-following, tool calls, and multi-turn task completion across diverse personas. The platform supports automated scenario generation and replay using scripted cases, AI-generated scenarios, and production conversation replays to cover core flows and known failure points. It also specializes in edge-case testing for voice agents, including handling accents, interruptions, silence, and background noise to ensure real-world conversational quality. In production, Cekura provides monitoring and analytics with dashboards and alerts that track metrics such as latency, task success, WER, interruption handling, and tool-call metadata. The platform is notable for combining pre-release evaluation and production observability into a single workflow, with support for regression and A/B testing in CI/CD pipelines. Formerly known as Vocera, Cekura serves data scientists, developers, and operations teams focused on conversational AI reliability and performance.

Key features

Pre-production call and conversation simulations
Automated scenario generation and replay
Edge-case testing for voice agents
Production monitoring and analytics
Regression and A/B testing in CI/CD pipelines
Support for diverse personas and real-world scenarios
Latency, task success, and interruption tracking
Tool-call metadata and WER metrics
Scripted and AI-generated test cases

Use cases

Validating voice and chat AI agents before deployment
Monitoring conversational AI performance in production
Testing edge cases like accents, interruptions, and background noise

Pros

Combines pre-release evaluation and production observability into a single workflow for conversational AI reliability
Supports automated scenario generation and replay using scripted cases, AI-generated scenarios, and production conversation replays
Specializes in edge-case testing for voice agents, including handling accents, interruptions, silence, and background noise
Provides monitoring and analytics with dashboards and alerts tracking metrics such as latency, task success, WER, and interruption handling
Offers benchmarking capabilities to compare performance across different voice AI platforms and models

Cons

May require integration with existing tools and workflows, which could involve setup time
Focused primarily on voice AI and chat AI agents, limiting applicability to other AI domains

Frequently asked questions about Cekura

What does Cekura do?

Cekura provides an automated QA and observability platform for voice AI and chat AI agents, enabling teams to test, monitor, and improve conversational AI systems before and after deployment.

Who should use Cekura?

Cekura is designed for data scientists, developers, and operations teams focused on building and deploying reliable conversational AI agents in enterprise environments.

How does Cekura help with testing voice AI agents?

It supports pre-production simulation with synthetic conversations, adversarial red-teaming, and edge-case testing for accents, interruptions, and background noise to validate agent performance.

Does Cekura integrate with existing tools?

Yes, Cekura integrates with the tools teams already use, allowing them to scale AI deployments without disrupting workflows or requiring additional infrastructure.

What kind of monitoring does Cekura provide in production?

Cekura offers live drift detection, call analytics, and dashboards that track metrics such as latency, task success, interruption handling, and compliance adherence across production calls.

How can teams get started with Cekura?

Teams can start by launching Cekura in minutes, running synthetic tests against their agents, and using the platform’s insights to continuously improve performance before and after deployment.